From 470939d483d1c89b7292f78bac1fd27c42c171ce Mon Sep 17 00:00:00 2001
From: Kevin Wang <kevmo314@gmail.com>
Date: Mon, 8 Jul 2024 03:26:53 -0400
Subject: [PATCH 001/125] common : preallocate sampling token data vector
 (#8363)

`emplace_back` repeatedly-called is slower than preallocating the vector to the vocab size and directly inserting the data. Some rudimentary profiling with `chrono` improves the performance of this block of code from ~500us/op to ~40us/op.

Overall, this slightly improves the sampling performance which has a more substantial impact for the `examples/lookahead` implementation -- I am able to see a ~10% performance boost in lookahead inference.
---
 common/sampling.cpp | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index e9db2715f2649e..6a483c81545740 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -378,7 +378,7 @@ static llama_token_data_array llama_sampling_prepare_impl(
     if (ctx_sampling->grammar != NULL && !apply_grammar) {
         GGML_ASSERT(original_logits != NULL);
         // Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this.
-        *original_logits = {logits, logits + llama_n_vocab(llama_get_model(ctx_main))};
+        *original_logits = {logits, logits + n_vocab};
     }
 
     // apply params.logit_bias map
@@ -391,10 +391,10 @@ static llama_token_data_array llama_sampling_prepare_impl(
         llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
     }
 
-    cur.clear();
+    cur.resize(n_vocab);
 
     for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-        cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+        cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
     }
 
     llama_token_data_array cur_p = { cur.data(), cur.size(), false };

From fde13b3bb9f569f07fd8af74696ee48a43d05131 Mon Sep 17 00:00:00 2001
From: John Balis <phobossystems@gmail.com>
Date: Tue, 2 Jul 2024 11:09:52 -0500
Subject: [PATCH 002/125] feat: cuda implementation for
 `ggml_conv_transpose_1d` (ggml/854)

* conv transpose 1d passing test for 1d input and kernel

* working for different input and output channel counts, added test for variable stride

* initial draft appears to work with stride other than 1

* working with all old and new conv1d  tests

* added a test for large tensors

* removed use cuda hardcoding

* restored test-conv-transpose.c

* removed unused arugments, and fixed bug where test failure would cause subsequent tests to fail

* fixed accumulator bug

* added test to test-backend-ops

* fixed mistake

* addressed review

* fixed includes

* removed blank lines

* style and warning fixes

* return failure when test fails

* fix supports_op

---------

Co-authored-by: slaren <slarengh@gmail.com>
---
 ggml/src/ggml-cuda.cu                    | 13 ++++
 ggml/src/ggml-cuda/conv-transpose-1d.cu  | 87 ++++++++++++++++++++++++
 ggml/src/ggml-cuda/conv-transpose-1d.cuh |  5 ++
 tests/test-backend-ops.cpp               | 42 +++++++++++-
 4 files changed, 146 insertions(+), 1 deletion(-)
 create mode 100644 ggml/src/ggml-cuda/conv-transpose-1d.cu
 create mode 100644 ggml/src/ggml-cuda/conv-transpose-1d.cuh

diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu
index 1c9ccc8a15e54e..ed784ea1c20246 100644
--- a/ggml/src/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -29,6 +29,7 @@
 #include "ggml-cuda/tsembd.cuh"
 #include "ggml-cuda/unary.cuh"
 #include "ggml-cuda/upscale.cuh"
+#include "ggml-cuda/conv-transpose-1d.cuh"
 
 #include <algorithm>
 #include <array>
@@ -2261,6 +2262,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_IM2COL:
             ggml_cuda_op_im2col(ctx, dst);
             break;
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            ggml_cuda_op_conv_transpose_1d(ctx,dst);
+            break;
         case GGML_OP_POOL_2D:
             ggml_cuda_op_pool2d(ctx, dst);
             break;
@@ -2804,6 +2808,15 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
                 ggml_type src0_type = op->src[0]->type;
                 return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
             } break;
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                ggml_type src1_type = op->src[1]->type;
+                if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
+                    return true;
+                }
+                return false;
+            } break;
         case GGML_OP_NONE:
         case GGML_OP_RESHAPE:
         case GGML_OP_VIEW:
diff --git a/ggml/src/ggml-cuda/conv-transpose-1d.cu b/ggml/src/ggml-cuda/conv-transpose-1d.cu
new file mode 100644
index 00000000000000..b1e94d6f770aa1
--- /dev/null
+++ b/ggml/src/ggml-cuda/conv-transpose-1d.cu
@@ -0,0 +1,87 @@
+#include "conv-transpose-1d.cuh"
+
+static  __global__ void conv_transpose_1d_kernel(
+        const int s0, const int p0, const int d0, const int output_size,
+        const int src0_ne0, const int src0_ne1, const int src0_ne2, const int src0_ne3,
+        const int src1_ne0, const int src1_ne1, const int src1_ne2, const int src1_ne3,
+        const int dst_ne0, const int dst_ne1, const int dst_ne2, const int dst_ne3,
+        const float * src0, const float * src1,  float * dst) {
+    int global_index = threadIdx.x + blockIdx.x * blockDim.x;
+    if (global_index >= output_size) {
+        return;
+    }
+
+    int out_index = global_index / dst_ne0;
+
+    float accumulator = 0;
+
+    for (int c = 0; c < src0_ne2; c++) {
+        int idx = global_index % dst_ne0;
+
+        int kernel_offset = (src0_ne0 * src0_ne1 * c) + (out_index * src0_ne0);
+        int input_offset = src1_ne0 * c;
+
+        for (int i = 0; i < src1_ne0; i++) {
+            if (!(idx >= i*s0 && idx < i*s0 + src0_ne0)) {
+                continue;
+            }
+            int weight_idx = idx - i*s0;
+
+            float kernel_weight = src0[kernel_offset + weight_idx];
+            float input_value =  src1[input_offset+i];
+
+            accumulator += kernel_weight * input_value;
+        }
+    }
+    dst[global_index] = accumulator;
+}
+
+static void conv_transpose_1d_f32_f32_cuda(
+        const int s0, const int p0, const int d0, const int output_size,
+        const int src0_ne0, const int src0_ne1, const int src0_ne2, const int src0_ne3,
+        const int src1_ne0, const int src1_ne1, const int src1_ne2, const int src1_ne3,
+        const int dst_ne0, const int dst_ne1, const int dst_ne2, const int dst_ne3,
+        const float * src0, const float * src1,  float * dst,
+        cudaStream_t stream) {
+
+    const int num_blocks = (output_size + CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE - 1) / CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE;
+    conv_transpose_1d_kernel<<<num_blocks,CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE, 0, stream>>>(
+        s0,p0,d0,output_size,
+        src0_ne0, src0_ne1,  src0_ne2, src0_ne3,
+        src1_ne0, src1_ne1,  src1_ne2, src1_ne3,
+        dst_ne0,  dst_ne1,   dst_ne2,  dst_ne3,
+        src0,src1, dst);
+}
+
+void ggml_cuda_op_conv_transpose_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+
+    const ggml_tensor * src1 = dst->src[1];
+    const float * src1_d = (const float *)src1->data;
+
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+
+    const int32_t * opts = (const int32_t *)dst->op_params;
+
+    const int s0 = opts[0];
+    const int p0 = 0;//opts[3];
+    const int d0 = 1;//opts[4];
+
+    const int64_t kernel_size = ggml_nelements(src0);
+    const int64_t input_size = ggml_nelements(src1);
+    const int64_t output_size = ggml_nelements(dst);
+
+    conv_transpose_1d_f32_f32_cuda(s0, p0, d0, output_size,
+        src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+        src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
+        dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
+        src0_d, src1_d, dst_d, stream);
+}
diff --git a/ggml/src/ggml-cuda/conv-transpose-1d.cuh b/ggml/src/ggml-cuda/conv-transpose-1d.cuh
new file mode 100644
index 00000000000000..6c2cf666b68dad
--- /dev/null
+++ b/ggml/src/ggml-cuda/conv-transpose-1d.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE 256
+
+void ggml_cuda_op_conv_transpose_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 2bb71ac03817fd..83d4a0eb153cf4 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -1266,6 +1266,36 @@ struct test_pool2d : public test_case {
     }
 };
 
+// GGML_OP_CONV_TRANSPOSE_1D
+struct test_conv_transpose_1d : public test_case {
+    
+    const std::array<int64_t, 4> ne_input;
+    const std::array<int64_t, 4> ne_kernel;
+
+    // stride
+    const int s0;
+    // padding
+    const int p0;
+    // dilation
+    const int d0;
+
+    std::string vars() override {
+        return VARS_TO_STR5(ne_input, ne_kernel, s0, p0, d0);
+    }
+
+    test_conv_transpose_1d(std::array<int64_t, 4> ne_input = {197, 32, 1, 1}, // [input_width, input_height, input_channels, 1]
+                           std::array<int64_t, 4> ne_kernel = {16, 32, 32, 1}, // [kernel_width, kernel_height, input_channels, 1]
+                           int s0 = 1, int p0 = 0, int d0 = 1)
+        : ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), p0(p0), d0(d0) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data());
+        ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_kernel.data());
+        ggml_tensor * out = ggml_conv_transpose_1d(ctx, kernel, input, s0, p0, d0);
+        return out;
+    }
+};
+
 // GGML_OP_IM2COL
 struct test_im2col : public test_case {
     const ggml_type type_input;
@@ -1279,7 +1309,7 @@ struct test_im2col : public test_case {
     // padding
     const int p0;
     const int p1;
-    // dilatation
+    // dilation
     const int d0;
     const int d1;
     // mode
@@ -2098,6 +2128,16 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32));
     test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16));
 
+    test_cases.emplace_back(new test_conv_transpose_1d());
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 3, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 2, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {2,3,2,1}, 1, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,2,2,1}, 2, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,2,2,1}, 1, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1));
+    test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1));
+
+
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1}));
     test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1}));

From 6847d54c4f72f8bf6767b6feae7a95394654335e Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 8 Jul 2024 10:39:36 +0300
Subject: [PATCH 003/125] tests : fix whitespace (#0)

---
 tests/test-backend-ops.cpp | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 83d4a0eb153cf4..a1bfc8fddd418c 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -1268,16 +1268,12 @@ struct test_pool2d : public test_case {
 
 // GGML_OP_CONV_TRANSPOSE_1D
 struct test_conv_transpose_1d : public test_case {
-    
     const std::array<int64_t, 4> ne_input;
     const std::array<int64_t, 4> ne_kernel;
 
-    // stride
-    const int s0;
-    // padding
-    const int p0;
-    // dilation
-    const int d0;
+    const int s0; // stride
+    const int p0; // padding
+    const int d0; // dilation
 
     std::string vars() override {
         return VARS_TO_STR5(ne_input, ne_kernel, s0, p0, d0);

From 2ee44c9a1865a928ccbbc16a2d7841d7513f31c1 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 8 Jul 2024 10:39:50 +0300
Subject: [PATCH 004/125] sync : ggml

ggml-ci
---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 2da33e913bc8fa..80159b70b94148 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-5378ea0d3c2f25bcd330ecb226ad2db454be86d0
+e3b3846976c94163f2b3dd128cc959782653edbb

From 3f2d538b817112ad8429341c7e8657dcd660f4d3 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 8 Jul 2024 13:51:31 +0300
Subject: [PATCH 005/125] scripts : fix sync for sycl

---
 scripts/sync-ggml-am.sh | 3 +++
 scripts/sync-ggml.sh    | 1 +
 2 files changed, 4 insertions(+)

diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh
index b05a33747f8313..ba3bedf21342ab 100755
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -63,6 +63,7 @@ while read c; do
         src/ggml*.metal \
         src/ggml*.cu \
         src/ggml-cuda/* \
+        src/ggml-sycl/* \
         include/ggml*.h \
         tests/test-opt.cpp \
         tests/test-grad0.cpp \
@@ -113,6 +114,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # src/ggml-quants.c       -> ggml/src/ggml-quants.c
     # src/ggml-quants.h       -> ggml/src/ggml-quants.h
     # src/ggml-rpc.cpp        -> ggml/src/ggml-rpc.cpp
+    # src/ggml-sycl/*         -> ggml/src/ggml-sycl/
     # src/ggml-sycl.cpp       -> ggml/src/ggml-sycl.cpp
     # src/ggml-vulkan.cpp     -> ggml/src/ggml-vulkan.cpp
     #
@@ -153,6 +155,7 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
         -e 's/([[:space:]]|[ab]\/)src\/ggml-quants\.c/\1ggml\/src\/ggml-quants.c/g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-quants\.h/\1ggml\/src\/ggml-quants.h/g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-rpc\.cpp/\1ggml\/src\/ggml-rpc.cpp/g' \
+        -e 's/([[:space:]]|[ab]\/)src\/ggml-sycl\//\1ggml\/src\/ggml-sycl\//g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-sycl\.cpp/\1ggml\/src\/ggml-sycl.cpp/g' \
         -e 's/([[:space:]]|[ab]\/)src\/ggml-vulkan\.cpp/\1ggml\/src\/ggml-vulkan.cpp/g' \
         -e 's/([[:space:]]|[ab]\/)include\/ggml\.h/\1ggml\/include\/ggml.h/g' \
diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh
index 2f32c1ce84cc7d..402446ef95b773 100755
--- a/scripts/sync-ggml.sh
+++ b/scripts/sync-ggml.sh
@@ -18,6 +18,7 @@ cp -rpv ../ggml/src/ggml-metal.metal    ./ggml/src/ggml-metal.metal
 cp -rpv ../ggml/src/ggml-quants.c       ./ggml/src/ggml-quants.c
 cp -rpv ../ggml/src/ggml-quants.h       ./ggml/src/ggml-quants.h
 cp -rpv ../ggml/src/ggml-rpc.cpp        ./ggml/src/ggml-rpc.cpp
+cp -rpv ../ggml/src/ggml-sycl/*         ./ggml/src/ggml-sycl/
 cp -rpv ../ggml/src/ggml-sycl.cpp       ./ggml/src/ggml-sycl.cpp
 cp -rpv ../ggml/src/ggml-vulkan.cpp     ./ggml/src/ggml-vulkan.cpp
 

From 2ec846d558f6385ea647f7b8e665eb249c1ebce7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alberto=20Cabrera=20P=C3=A9rez?= <alberto.cabrera@intel.com>
Date: Mon, 8 Jul 2024 14:22:41 +0100
Subject: [PATCH 006/125] sycl : fix powf call in device code (#8368)

---
 ggml/src/ggml-sycl/rope.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-sycl/rope.cpp b/ggml/src/ggml-sycl/rope.cpp
index eabf1693e2d2f4..6f507941a022ae 100644
--- a/ggml/src/ggml-sycl/rope.cpp
+++ b/ggml/src/ggml-sycl/rope.cpp
@@ -55,7 +55,7 @@ static void rope_norm(
     const int i = row*ne0 + i0;
     const int i2 = row/p_delta_rows;
 
-    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+    const float theta_base = pos[i2] * sycl::pow(theta_scale, i0 / 2.0f);
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
 
@@ -98,7 +98,7 @@ static void rope_neox(
     const int i  = row*ne0 + i0/2;
     const int i2 = row/p_delta_rows;
 
-    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+    const float theta_base = pos[i2] * sycl::pow(theta_scale, i0 / 2.0f);
 
     const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
 

From c4dd11d1d3903e1922c06242e189f6310fc4d8c3 Mon Sep 17 00:00:00 2001
From: b4b4o <zwbao@foxmail.com>
Date: Mon, 8 Jul 2024 22:19:24 +0800
Subject: [PATCH 007/125] readme : fix web link error [no ci] (#8347)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5498d5fc6755ac..aa707081165715 100644
--- a/README.md
+++ b/README.md
@@ -98,7 +98,7 @@ Typically finetunes of the base models below are supported as well.
 - [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia)
 - [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b)
 
-(instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md))
+(instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md))
 
 **Multimodal models:**
 

From a130eccef42b75a84da270411cefeed45c153e30 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alberto=20Cabrera=20P=C3=A9rez?=
 <alberto.cabrera@codeplay.com>
Date: Mon, 8 Jul 2024 21:35:17 +0100
Subject: [PATCH 008/125] labeler : updated sycl to match docs and code
 refactor (#8373)

---
 .github/labeler.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/labeler.yml b/.github/labeler.yml
index 9c0397d16e1a03..89436740d1ffb2 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -16,7 +16,9 @@ SYCL:
         - any-glob-to-any-file:
             - ggml/include/ggml-sycl.h
             - ggml/src/ggml-sycl.cpp
-            - README-sycl.md
+            - ggml/src/ggml-sycl/**
+            - docs/backend/SYCL.md
+            - examples/sycl/**
 Nvidia GPU:
     - changed-files:
         - any-glob-to-any-file:

From 7fdb6f73e35605c8dbc39e9f19cd9ed84dbc87f2 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 9 Jul 2024 01:36:38 +0300
Subject: [PATCH 009/125] flake.lock: Update (#8342)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Flake lock file updates:

• Updated input 'flake-parts':
    'github:hercules-ci/flake-parts/2a55567fcf15b1b1c7ed712a2c6fadaec7412ea8?narHash=sha256-iKzJcpdXih14qYVcZ9QC9XuZYnPc6T8YImb6dX166kw%3D' (2024-06-01)
  → 'github:hercules-ci/flake-parts/9227223f6d922fee3c7b190b2cc238a99527bbb7?narHash=sha256-pQMhCCHyQGRzdfAkdJ4cIWiw%2BJNuWsTX7f0ZYSyz0VY%3D' (2024-07-03)
• Updated input 'flake-parts/nixpkgs-lib':
    'https://github.com/NixOS/nixpkgs/archive/eb9ceca17df2ea50a250b6b27f7bf6ab0186f198.tar.gz?narHash=sha256-lIbdfCsf8LMFloheeE6N31%2BBMIeixqyQWbSr2vk79EQ%3D' (2024-06-01)
  → 'https://github.com/NixOS/nixpkgs/archive/5daf0514482af3f97abaefc78a6606365c9108e2.tar.gz?narHash=sha256-Fm2rDDs86sHy0/1jxTOKB1118Q0O3Uc7EC0iXvXKpbI%3D' (2024-07-01)
• Updated input 'nixpkgs':
    'github:NixOS/nixpkgs/b2852eb9365c6de48ffb0dc2c9562591f652242a?narHash=sha256-C8e9S7RzshSdHB7L%2Bv9I51af1gDM5unhJ2xO1ywxNH8%3D' (2024-06-27)
  → 'github:NixOS/nixpkgs/9f4128e00b0ae8ec65918efeba59db998750ead6?narHash=sha256-rwz8NJZV%2B387rnWpTYcXaRNvzUSnnF9aHONoJIYmiUQ%3D' (2024-07-03)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 flake.lock | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/flake.lock b/flake.lock
index 973ff4e5675c77..5001fbf912c5ce 100644
--- a/flake.lock
+++ b/flake.lock
@@ -5,11 +5,11 @@
         "nixpkgs-lib": "nixpkgs-lib"
       },
       "locked": {
-        "lastModified": 1717285511,
-        "narHash": "sha256-iKzJcpdXih14qYVcZ9QC9XuZYnPc6T8YImb6dX166kw=",
+        "lastModified": 1719994518,
+        "narHash": "sha256-pQMhCCHyQGRzdfAkdJ4cIWiw+JNuWsTX7f0ZYSyz0VY=",
         "owner": "hercules-ci",
         "repo": "flake-parts",
-        "rev": "2a55567fcf15b1b1c7ed712a2c6fadaec7412ea8",
+        "rev": "9227223f6d922fee3c7b190b2cc238a99527bbb7",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1719506693,
-        "narHash": "sha256-C8e9S7RzshSdHB7L+v9I51af1gDM5unhJ2xO1ywxNH8=",
+        "lastModified": 1720031269,
+        "narHash": "sha256-rwz8NJZV+387rnWpTYcXaRNvzUSnnF9aHONoJIYmiUQ=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "b2852eb9365c6de48ffb0dc2c9562591f652242a",
+        "rev": "9f4128e00b0ae8ec65918efeba59db998750ead6",
         "type": "github"
       },
       "original": {
@@ -36,14 +36,14 @@
     },
     "nixpkgs-lib": {
       "locked": {
-        "lastModified": 1717284937,
-        "narHash": "sha256-lIbdfCsf8LMFloheeE6N31+BMIeixqyQWbSr2vk79EQ=",
+        "lastModified": 1719876945,
+        "narHash": "sha256-Fm2rDDs86sHy0/1jxTOKB1118Q0O3Uc7EC0iXvXKpbI=",
         "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/eb9ceca17df2ea50a250b6b27f7bf6ab0186f198.tar.gz"
+        "url": "https://github.com/NixOS/nixpkgs/archive/5daf0514482af3f97abaefc78a6606365c9108e2.tar.gz"
       },
       "original": {
         "type": "tarball",
-        "url": "https://github.com/NixOS/nixpkgs/archive/eb9ceca17df2ea50a250b6b27f7bf6ab0186f198.tar.gz"
+        "url": "https://github.com/NixOS/nixpkgs/archive/5daf0514482af3f97abaefc78a6606365c9108e2.tar.gz"
       }
     },
     "root": {

From 7d0e23d72ef4540d0d4409cb63ae682c17d53926 Mon Sep 17 00:00:00 2001
From: compilade <git@compilade.net>
Date: Tue, 9 Jul 2024 01:04:49 -0400
Subject: [PATCH 010/125] gguf-py : do not use internal numpy types (#7472)

---
 gguf-py/gguf/lazy.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/gguf-py/gguf/lazy.py b/gguf-py/gguf/lazy.py
index c50124cd912d40..6e266f34f76ace 100644
--- a/gguf-py/gguf/lazy.py
+++ b/gguf-py/gguf/lazy.py
@@ -6,7 +6,6 @@
 from collections import deque
 
 import numpy as np
-from numpy._typing import _Shape
 from numpy.typing import DTypeLike
 
 
@@ -219,7 +218,7 @@ class LazyNumpyTensor(LazyBase):
     _tensor_type = np.ndarray
 
     @classmethod
-    def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: _Shape) -> np.ndarray[Any, Any]:
+    def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: tuple[int, ...]) -> np.ndarray[Any, Any]:
         # The initial idea was to use np.nan as the fill value,
         # but non-float types like np.int16 can't use that.
         # So zero it is.

From 9beb2dda038e2107a39a95def94a4cf971e048ea Mon Sep 17 00:00:00 2001
From: daghanerdonmez <44506702+daghanerdonmez@users.noreply.github.com>
Date: Tue, 9 Jul 2024 09:16:00 +0300
Subject: [PATCH 011/125] readme : fix typo [no ci] (#8389)

Bakus-Naur --> Backus-Naur
---
 grammars/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/grammars/README.md b/grammars/README.md
index 886023f770218d..01b02abb4de9c3 100644
--- a/grammars/README.md
+++ b/grammars/README.md
@@ -4,7 +4,7 @@ GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.
 
 ## Background
 
-[Bakus-Naur Form (BNF)](https://en.wikipedia.org/wiki/Backus%E2%80%93Naur_form) is a notation for describing the syntax of formal languages like programming languages, file formats, and protocols. GBNF is an extension of BNF that primarily adds a few modern regex-like features.
+[Backus-Naur Form (BNF)](https://en.wikipedia.org/wiki/Backus%E2%80%93Naur_form) is a notation for describing the syntax of formal languages like programming languages, file formats, and protocols. GBNF is an extension of BNF that primarily adds a few modern regex-like features.
 
 ## Basics
 

From 9925ca4087a34ab973b07bf06c0b770cb586830b Mon Sep 17 00:00:00 2001
From: Borislav Stanimirov <b.stanimirov@abv.bg>
Date: Tue, 9 Jul 2024 11:38:00 +0300
Subject: [PATCH 012/125] cmake : allow external ggml (#8370)

---
 CMakeLists.txt | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 4f6cd68724aa6b..cb97d87913955b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -115,7 +115,10 @@ llama_option_depr(WARNING     LLAMA_SYCL_F16            GGML_SYCL_F16)
 # build the library
 #
 
-add_subdirectory(ggml)
+if (NOT TARGET ggml)
+    add_subdirectory(ggml)
+    # ... otherwise assume ggml is added by a parent CMakeLists.txt
+endif()
 add_subdirectory(src)
 
 #

From 5b0b8d8cfb5ddf2118f686ba6c30fab3f71b384b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alberto=20Cabrera=20P=C3=A9rez?=
 <alberto.cabrera@codeplay.com>
Date: Tue, 9 Jul 2024 15:03:15 +0100
Subject: [PATCH 013/125] sycl : Reenabled mmvq path for the SYCL Nvidia
 Backend (#8372)

* SYCL : Reenabled mmvq path for the SYCL Nvidia Backend

* Reduced verbosity of comment
---
 ggml/src/ggml-sycl.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl.cpp
index 21006cd7bebf7c..9c419ba8967064 100644
--- a/ggml/src/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl.cpp
@@ -3658,6 +3658,10 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
     use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
 #endif // SYCL_USE_XMX
 
+    // mmvq path is faster in the CUDA backend.
+    if (ctx.stream()->get_backend() == sycl::backend::ext_oneapi_cuda)
+        use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
+
     if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
         // KQ single-batch
         ggml_sycl_mul_mat_vec_p021(ctx, src0, src1, dst);

From a03e8dd99d3954e7547137936c5a2a3b348bdb7f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 9 Jul 2024 17:11:07 +0200
Subject: [PATCH 014/125] make/cmake: LLAMA_NO_CCACHE -> GGML_NO_CCACHE (#8392)

---
 CMakeLists.txt |  4 ----
 Makefile       | 12 +++++++++---
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index cb97d87913955b..befc4433cd0f83 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -50,9 +50,6 @@ endif()
 # option list
 #
 
-# general
-option(LLAMA_CCACHE "llama: use ccache if available" ON)
-
 # debug
 option(LLAMA_ALL_WARNINGS           "llama: enable all compiler warnings"                   ON)
 option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF)
@@ -77,7 +74,6 @@ option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
 include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
 
 # override ggml options
-set(GGML_CCACHE             ${LLAMA_CCACHE})
 set(GGML_SANITIZE_THREAD    ${LLAMA_SANITIZE_THREAD})
 set(GGML_SANITIZE_ADDRESS   ${LLAMA_SANITIZE_ADDRESS})
 set(GGML_SANITIZE_UNDEFINED ${LLAMA_SANITIZE_UNDEFINED})
diff --git a/Makefile b/Makefile
index bb6e2f968cf0f7..be7fb94013beb8 100644
--- a/Makefile
+++ b/Makefile
@@ -245,17 +245,22 @@ MK_CFLAGS    = -std=c11   -fPIC
 MK_CXXFLAGS  = -std=c++11 -fPIC
 MK_NVCCFLAGS = -std=c++11
 
-ifndef LLAMA_NO_CCACHE
+ifdef LLAMA_NO_CCACHE
+GGML_NO_CCACHE := 1
+DEPRECATE_WARNING := 1
+endif
+
+ifndef GGML_NO_CCACHE
 CCACHE := $(shell which ccache)
 ifdef CCACHE
 export CCACHE_SLOPPINESS = time_macros
-$(info I ccache found, compilation results will be cached. Disable with LLAMA_NO_CCACHE.)
+$(info I ccache found, compilation results will be cached. Disable with GGML_NO_CCACHE.)
 CC    := $(CCACHE) $(CC)
 CXX   := $(CCACHE) $(CXX)
 else
 $(info I ccache not found. Consider installing it for faster compilation.)
 endif # CCACHE
-endif # LLAMA_NO_CCACHE
+endif # GGML_NO_CCACHE
 
 # clock_gettime came in POSIX.1b (1993)
 # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
@@ -926,6 +931,7 @@ $(info   - LLAMA_NO_LLAMAFILE)
 $(info   - LLAMA_NO_ACCELERATE)
 $(info   - LLAMA_NO_OPENMP)
 $(info   - LLAMA_NO_METAL)
+$(info   - LLAMA_NO_CCACHE)
 $(info )
 endif
 

From e500d6135ac42c4a23baf6a9a1a934dc023e5c7d Mon Sep 17 00:00:00 2001
From: Clint Herron <hanclinto@gmail.com>
Date: Tue, 9 Jul 2024 11:54:43 -0400
Subject: [PATCH 015/125] Deprecation warning to assist with migration to new
 binary names (#8283)

* Adding a simple program to provide a deprecation warning that can exist to help people notice the binary name change from #7809 and migrate to the new filenames.

* Build legacy replacement binaries only if they already exist. Check for their existence every time so that they are not ignored.
---
 Makefile                                      | 78 ++++++++++++++++++-
 examples/deprecation-warning/README.md        | 51 ++++++++++++
 .../deprecation-warning.cpp                   | 35 +++++++++
 3 files changed, 160 insertions(+), 4 deletions(-)
 create mode 100644 examples/deprecation-warning/README.md
 create mode 100644 examples/deprecation-warning/deprecation-warning.cpp

diff --git a/Makefile b/Makefile
index be7fb94013beb8..1041200905371b 100644
--- a/Makefile
+++ b/Makefile
@@ -64,10 +64,14 @@ TEST_TARGETS = \
 	tests/test-tokenizer-1-spm
 
 # Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned
-LEGACY_TARGETS = main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
+LEGACY_TARGETS_CLEAN = main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
 	simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \
 	retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm
 
+# Legacy build targets that were renamed in #7809, but we want to build binaries that for them that output a deprecation warning if people try to use them.
+#  We don't want to clutter things too much, so we only build replacements for the most commonly used binaries.
+LEGACY_TARGETS_BUILD = main quantize perplexity embedding server finetune
+
 # Deprecation aliases
 ifdef LLAMA_CUBLAS
 $(error LLAMA_CUBLAS is removed. Use GGML_CUDA instead.)
@@ -193,7 +197,7 @@ ifdef GGML_RPC
 	BUILD_TARGETS += rpc-server
 endif
 
-default: $(BUILD_TARGETS)
+default: $(BUILD_TARGETS) $(LEGACY_TARGETS_BUILD)
 
 test: $(TEST_TARGETS)
 	@failures=0; \
@@ -228,7 +232,7 @@ test: $(TEST_TARGETS)
 	fi
 	@echo 'All tests passed.'
 
-all: $(BUILD_TARGETS) $(TEST_TARGETS)
+all: $(BUILD_TARGETS) $(TEST_TARGETS) $(LEGACY_TARGETS_BUILD)
 
 ifdef RISCV_CROSS_COMPILE
 CC	:= riscv64-unknown-linux-gnu-gcc
@@ -1098,7 +1102,7 @@ clean:
 	rm -vrf ggml/src/ggml-cuda/template-instances/*.o
 	rm -rvf $(BUILD_TARGETS)
 	rm -rvf $(TEST_TARGETS)
-	rm -rvf $(LEGACY_TARGETS)
+	rm -rvf $(LEGACY_TARGETS_CLEAN)
 	find examples pocs -type f -name "*.o" -delete
 
 #
@@ -1494,3 +1498,69 @@ llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \
 	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
+#
+# Deprecated binaries that we want to keep around long enough for people to migrate to the new filenames, then these can be removed.
+#
+# Mark legacy binary targets as .PHONY so that they are always checked.
+.PHONY: main quantize perplexity embedding server finetune
+
+main: examples/deprecation-warning/deprecation-warning.cpp
+ifneq (,$(wildcard main))
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	@echo "#########"
+	@echo "WARNING: The 'main' binary is deprecated. Please use 'llama-cli' instead."
+	@echo "  Remove the 'main' binary to remove this warning."
+	@echo "#########"
+endif
+
+quantize: examples/deprecation-warning/deprecation-warning.cpp
+ifneq (,$(wildcard quantize))
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	@echo "#########"
+	@echo "WARNING: The 'quantize' binary is deprecated. Please use 'llama-quantize' instead."
+	@echo "  Remove the 'quantize' binary to remove this warning."
+	@echo "#########"
+endif
+
+perplexity: examples/deprecation-warning/deprecation-warning.cpp
+ifneq (,$(wildcard perplexity))
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	@echo "#########"
+	@echo "WARNING: The 'perplexity' binary is deprecated. Please use 'llama-perplexity' instead."
+	@echo "  Remove the 'perplexity' binary to remove this warning."
+	@echo "#########"
+endif
+
+embedding: examples/deprecation-warning/deprecation-warning.cpp
+ifneq (,$(wildcard embedding))
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	@echo "#########"
+	@echo "WARNING: The 'embedding' binary is deprecated. Please use 'llama-embedding' instead."
+	@echo "  Remove the 'embedding' binary to remove this warning."
+	@echo "#########"
+endif
+
+server: examples/deprecation-warning/deprecation-warning.cpp
+ifneq (,$(wildcard server))
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	@echo "#########"
+	@echo "WARNING: The 'server' binary is deprecated. Please use 'llama-server' instead."
+	@echo "  Remove the 'server' binary to remove this warning."
+	@echo "#########"
+endif
+
+finetune: examples/deprecation-warning/deprecation-warning.cpp
+ifneq (,$(wildcard finetune))
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	@echo "#########"
+	@echo "WARNING: The 'finetune' binary is deprecated. Please use 'llama-finetune' instead."
+	@echo "  Remove the 'finetune' binary to remove this warning."
+	@echo "#########"
+endif
diff --git a/examples/deprecation-warning/README.md b/examples/deprecation-warning/README.md
new file mode 100644
index 00000000000000..1e20feb4aab872
--- /dev/null
+++ b/examples/deprecation-warning/README.md
@@ -0,0 +1,51 @@
+# Migration notice for binary filenames
+
+> [!IMPORTANT]
+[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809)
+
+This migration was important, but it is a breaking change that may not always be immediately obvious to users.
+
+Please update all scripts and workflows to use the new binary names.
+
+| Old Filename | New Filename |
+| ---- | ---- |
+| main | llama-cli |
+| server | llama-server |
+| llama-bench | llama-bench |
+| embedding | llama-embedding |
+| finetune | llama-finetune |
+| quantize | llama-quantize |
+| tokenize | llama-tokenize |
+| export-lora | llama-export-lora |
+| libllava.a | libllava.a |
+| baby-llama | llama-baby-llama |
+| batched | llama-batched |
+| batched-bench | llama-batched-bench |
+| benchmark-matmult | llama-benchmark-matmult |
+| convert-llama2c-to-ggml | llama-convert-llama2c-to-ggml |
+| eval-callback | llama-eval-callback |
+| gbnf-validator | llama-gbnf-validator |
+| gguf | llama-gguf |
+| gguf-split | llama-gguf-split |
+| gritlm | llama-gritlm |
+| imatrix | llama-imatrix |
+| infill | llama-infill |
+| llava-cli | llama-llava-cli |
+| lookahead | llama-lookahead |
+| lookup | llama-lookup |
+| lookup-create | llama-lookup-create |
+| lookup-merge | llama-lookup-merge |
+| lookup-stats | llama-lookup-stats |
+| parallel | llama-parallel |
+| passkey | llama-passkey |
+| perplexity | llama-perplexity |
+| q8dot | llama-q8dot |
+| quantize-stats | llama-quantize-stats |
+| retrieval | llama-retrieval |
+| save-load-state | llama-save-load-state |
+| simple | llama-simple |
+| speculative | llama-speculative |
+| train-text-from-scratch | llama-train-text-from-scratch |
+| vdot | llama-vdot |
+| tests/test-c.o | tests/test-c.o |
+
diff --git a/examples/deprecation-warning/deprecation-warning.cpp b/examples/deprecation-warning/deprecation-warning.cpp
new file mode 100644
index 00000000000000..11b35d2c22500b
--- /dev/null
+++ b/examples/deprecation-warning/deprecation-warning.cpp
@@ -0,0 +1,35 @@
+// Warns users that this filename was deprecated, and provides a link for more information.
+
+#include <cstdio>
+#include <string>
+#include <unordered_map>
+
+// Main
+int main(int argc, char** argv) {
+    std::string filename = "main";
+    if (argc >= 1) {
+        filename = argv[0];
+    }
+
+    // Get only the program name from the full path
+    auto pos = filename.find_last_of('/');
+    if (pos != std::string::npos) {
+        filename = filename.substr(pos+1);
+    }
+
+    // Append "llama-" to the beginning of filename to get the replacemnt filename
+    auto replacement_filename = "llama-" + filename;
+
+    // The exception is if the filename is "main", then our replacement filename is "llama-cli"
+    if (filename == "main") {
+        replacement_filename = "llama-cli";
+    }
+
+    fprintf(stdout, "\n");
+    fprintf(stdout, "WARNING: The binary '%s' is deprecated.\n", filename.c_str());
+    fprintf(stdout, " Please use '%s' instead.\n", replacement_filename.c_str());
+    fprintf(stdout, " See https://github.com/ggerganov/llama.cpp/tree/master/examples/deprecation-warning/README.md for more information.\n");
+    fprintf(stdout, "\n");
+
+    return EXIT_FAILURE;
+}

From fd560fe680c72fd0a0af2bc8881add20ad919071 Mon Sep 17 00:00:00 2001
From: Andy Salerno <andysalerno@gmail.com>
Date: Tue, 9 Jul 2024 11:58:44 -0700
Subject: [PATCH 016/125] Update README.md to fix broken link to docs (#8399)

Update the "Performance troubleshooting" doc link to be correct - the file was moved into a dir called 'development'
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index aa707081165715..058919068b872c 100644
--- a/README.md
+++ b/README.md
@@ -453,7 +453,7 @@ To learn more how to measure perplexity using llama.cpp, [read this documentatio
 - [How to build](./docs/build.md)
 - [Running on Docker](./docs/docker.md)
 - [Build on Android](./docs/android.md)
-- [Performance troubleshooting](./docs/token_generation_performance_tips.md)
+- [Performance troubleshooting](./docs/development/token_generation_performance_tips.md)
 - [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
 
 **Seminal papers and background on the models**

From a59f8fdc85e1119d470d8766e29617962549d993 Mon Sep 17 00:00:00 2001
From: Clint Herron <hanclinto@gmail.com>
Date: Tue, 9 Jul 2024 18:26:40 -0400
Subject: [PATCH 017/125] Server: Enable setting default sampling parameters
 via command-line (#8402)

* Load server sampling parameters from the server context by default.

* Wordsmithing comment
---
 examples/server/server.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 47bea159136001..8feff67029da1a 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -884,7 +884,8 @@ struct server_context {
 
     bool launch_slot_with_task(server_slot & slot, const server_task & task) {
         slot_params default_params;
-        llama_sampling_params default_sparams;
+        // Sampling parameter defaults are loaded from the global server context (but individual requests can still override them)
+        llama_sampling_params default_sparams = params.sparams;
         auto & data = task.data;
 
         if (data.count("__oaicompat") != 0) {

From 8f0fad42b9589f9b11362c74ea5338c51e4c7e61 Mon Sep 17 00:00:00 2001
From: laik <laik.lj@me.com>
Date: Wed, 10 Jul 2024 19:19:10 +0800
Subject: [PATCH 018/125] py : fix extra space in convert_hf_to_gguf.py (#8407)

---
 convert_hf_to_gguf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 6cea73f08b7434..ecb807262b84bc 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -1356,7 +1356,7 @@ class LlamaModel(Model):
 
     def set_vocab(self):
         try:
-            self. _set_vocab_sentencepiece()
+            self._set_vocab_sentencepiece()
         except FileNotFoundError:
             try:
                 self._set_vocab_llama_hf()

From e4dd31ff892627665d3c53c5d1a03c0d282d9d45 Mon Sep 17 00:00:00 2001
From: RunningLeon <maningsheng@sensetime.com>
Date: Wed, 10 Jul 2024 19:26:40 +0800
Subject: [PATCH 019/125] py : fix converter for internlm2 (#8321)

* update internlm2

* remove unused file

* fix lint
---
 convert_hf_to_gguf.py | 68 +++++++++++++++++++++++++++++++++----------
 1 file changed, 52 insertions(+), 16 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index ecb807262b84bc..ebb5ca376133b9 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2144,6 +2144,9 @@ def set_vocab(self):
                 toktype = SentencePieceTokenTypes.UNUSED
             elif tokenizer.IsByte(token_id):
                 toktype = SentencePieceTokenTypes.BYTE
+            # take care of ununsed raw token
+            if piece.startswith('[UNUSED'):
+                toktype = SentencePieceTokenTypes.UNKNOWN
 
             tokens.append(text)
             scores.append(score)
@@ -2159,6 +2162,47 @@ def set_vocab(self):
                     scores.append(-1000.0)
                     toktypes.append(SentencePieceTokenTypes.USER_DEFINED)
 
+        chat_eos_token = '<|im_end|>'
+        chat_eos_token_id = None
+
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
+                for token_id, foken_data in added_tokens_decoder.items():
+                    token_id = int(token_id)
+                    token = foken_data["content"]
+                    if token == chat_eos_token:
+                        chat_eos_token_id = token_id
+                    token = token.encode("utf-8")
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
+                        assert(tokens[token_id] == token)
+                    tokens[token_id] = token
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+                    if foken_data.get("special"):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+
+        tokenizer_file = self.dir_model / 'tokenizer.json'
+        if tokenizer_file.is_file():
+            with open(tokenizer_file, "r", encoding="utf-8") as f:
+                tokenizer_json = json.load(f)
+                added_tokens = tokenizer_json.get("added_tokens", [])
+                for foken_data in added_tokens:
+                    token_id = int(foken_data["id"])
+                    token = foken_data["content"]
+                    if token == chat_eos_token:
+                        chat_eos_token_id = token_id
+                    token = token.encode("utf-8")
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
+                        assert(tokens[token_id] == token)
+                    tokens[token_id] = token
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+                    if foken_data.get("special"):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+
         self.gguf_writer.add_tokenizer_model("llama")
         self.gguf_writer.add_tokenizer_pre("default")
         self.gguf_writer.add_token_list(tokens)
@@ -2168,28 +2212,16 @@ def set_vocab(self):
 
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
         old_eos = special_vocab.special_token_ids["eos"]
-        if "chat" in os.path.basename(self.dir_model.absolute()):
+        if chat_eos_token_id is not None:
             # For the chat model, we replace the eos with '<|im_end|>'.
             # TODO: this is a hack, should be fixed
             #       https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048
-            special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
-            logger.warning(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
-in chat mode so that the conversation can end normally.")
+            special_vocab.special_token_ids["eos"] = chat_eos_token_id
+            logger.warning(f"Replace eos:{old_eos} with a special token:{chat_eos_token_id}"
+                           " in chat mode so that the conversation can end normally.")
 
         special_vocab.add_to_gguf(self.gguf_writer)
 
-    def _try_get_sft_eos(self, tokenizer):
-        unused_145_list = tokenizer.Encode('[UNUSED_TOKEN_145]')
-        im_end_list = tokenizer.Encode('<|im_end|>')
-        eos_token = None
-        assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1)
-        if len(unused_145_list) == 1:
-            eos_token = unused_145_list[0]
-        if len(im_end_list) == 1:
-            eos_token = im_end_list[0]
-        assert eos_token
-        return eos_token
-
     def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int):
         if n_head_kv is not None and n_head != n_head_kv:
             n_head = n_head_kv
@@ -2208,6 +2240,10 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
         self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
         self.gguf_writer.add_file_type(self.ftype)
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "linear":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         num_heads = self.hparams["num_attention_heads"]

From a8be1e6f5995bec3b1d8474d48ce3a139553a8e1 Mon Sep 17 00:00:00 2001
From: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Date: Wed, 10 Jul 2024 13:38:58 +0200
Subject: [PATCH 020/125] llama : add assert about missing llama_encode() call
 (#8400)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
---
 src/llama.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/llama.cpp b/src/llama.cpp
index 2b9ace28584572..80cc1da2660e56 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -13200,6 +13200,8 @@ struct llm_build_context {
                     LLM_NORM_RMS, cb, -1);
             cb(cur, "result_norm", -1);
         } else {
+            GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first");
+
             struct ggml_tensor * embd_enc       = llm_build_inp_embd_enc();
             struct ggml_tensor * pos_bucket_dec = llm_build_pos_bucket(true);
 

From 7a80710d93ee0f4e0d335d65984ae747e62c0337 Mon Sep 17 00:00:00 2001
From: Borislav Stanimirov <b.stanimirov@abv.bg>
Date: Wed, 10 Jul 2024 14:40:53 +0300
Subject: [PATCH 021/125] msvc : silence codecvt c++17 deprecation warnings
 (#8395)

---
 common/common.cpp | 4 ++++
 src/unicode.cpp   | 4 ++++
 2 files changed, 8 insertions(+)

diff --git a/common/common.cpp b/common/common.cpp
index fc0f3b350ec6e7..1e5fc30dd458aa 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1,3 +1,7 @@
+#if defined(_MSC_VER)
+#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
+#endif
+
 #include "common.h"
 // Change JSON_ASSERT from assert() to GGML_ASSERT:
 #define JSON_ASSERT GGML_ASSERT
diff --git a/src/unicode.cpp b/src/unicode.cpp
index 51daa15afa6692..e05fb9d1775ddd 100644
--- a/src/unicode.cpp
+++ b/src/unicode.cpp
@@ -1,3 +1,7 @@
+#if defined(_MSC_VER)
+#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
+#endif
+
 #include "unicode.h"
 #include "unicode-data.h"
 

From cc61948b1f97165b46990f4c2d9699bf9bb64443 Mon Sep 17 00:00:00 2001
From: Borislav Stanimirov <b.stanimirov@abv.bg>
Date: Wed, 10 Jul 2024 14:45:44 +0300
Subject: [PATCH 022/125] llama : C++20 compatibility for u8 strings (#8408)

---
 src/llama.cpp | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index 80cc1da2660e56..98a242081ca037 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -57,6 +57,12 @@
     #include <io.h>
 #endif
 
+#if __cplusplus >= 202000L
+    #define LU8(x) (const char*)(u8##x)
+#else
+    #define LU8(x) u8##x
+#endif
+
 #include <algorithm>
 #include <array>
 #include <cassert>
@@ -21511,12 +21517,12 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>";
         }
-    } else if (tmpl == "minicpm" || tmpl_contains(u8"<用户>")) {
+    } else if (tmpl == "minicpm" || tmpl_contains(LU8("<用户>"))) {
         // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
         for (auto message : chat) {
             std::string role(message->role);
             if (role == "user") {
-                ss << u8"<用户>";
+                ss << LU8("<用户>");
                 ss << trim(message->content);
                 ss << "<AI>";
             } else {
@@ -21532,7 +21538,7 @@ static int32_t llama_chat_apply_template_internal(
             } else if (role == "user") {
                 ss << "User: " << message->content << "\n\n";
             } else if (role == "assistant") {
-                ss << "Assistant: " << message->content << u8"<｜end▁of▁sentence｜>";
+                ss << "Assistant: " << message->content << LU8("<｜end▁of▁sentence｜>");
             }
         }
         if (add_ass) {

From 83321c6958acf20747d288031174cc1bf8816e33 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= <yusufsarigoz@gmail.com>
Date: Wed, 10 Jul 2024 15:12:35 +0300
Subject: [PATCH 023/125] gguf-py rel pipeline (#8410)

* Upd gguf-py/readme

* Bump patch version for release
---
 gguf-py/README.md      | 1 -
 gguf-py/pyproject.toml | 2 +-
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/gguf-py/README.md b/gguf-py/README.md
index bc46d6e1dd7082..9dd888f3180d14 100644
--- a/gguf-py/README.md
+++ b/gguf-py/README.md
@@ -79,5 +79,4 @@ python -m twine upload dist/*
 ```
 
 ## TODO
-- [ ] Add tests
 - [ ] Include conversion scripts as command line entry points in this package.
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 36e63ee3b7cd6e..62129126bdddcf 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.9.0"
+version = "0.9.1"
 description = "Read and write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [

From 0f1a39f3439825acf7e3a1663566d410be152170 Mon Sep 17 00:00:00 2001
From: Dibakar Gope <dibakar.gope@arm.com>
Date: Wed, 10 Jul 2024 07:14:51 -0500
Subject: [PATCH 024/125] ggml : add AArch64 optimized GEMV and GEMM Q4 kernels
 (#5780)

* Arm AArch64: optimized GEMV and GEMM kernels for q4_0_q8_0, and q8_0_q8_0 quantization

* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions

* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions

* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions

* Arm AArch64: add optimized GEMV and GEMM asm kernels for q4_0_q8_0 quantization and refactor code to address llama.cpp pr#5780 suggestions

* Arm AArch64: add copyright claim only to ggml-aarch64.cpp and ggml-aarch64.h files

* Arm AArch64: minor code refactoring for rebase

* Arm AArch64: minor code refactoring for resolving a build issue with cmake

* Arm AArch64: minor code refactoring to split the Q4_0_AARC64 type into three separate types: Q4_0_4_4, Q4_0_4_8, and Q4_0_8_8

* Arm AArch64: minor code change for resolving a build issue with server-windows

* retrigger checks

* Arm AArch64: minor code changes for rebase

* Arm AArch64: minor changes to skip the pr#7433 vec_dot code for arm cpus with SVE VL not equal to 256 bits

* Arm AArch64: remove stale LLAMA_QKK_64 from CMakeLists.txt and delete build.zig

* Arm AArch64: add reference scalar gemm and gemv, and avoid dynamic memory allocations during quantization for Q4_0_4_4, Q4_0_4_8, and Q4_0_8_8

* Arm AArch64: add multithreaded quantization support for the new types: Q4_0_4_4, Q4_0_4_8, and Q4_0_8_8

* Arm AArch64: minor code refactoring

* Arm AArch64: simplify logic for calling gemm and gemv functions in ggml_compute_forward_mul_mat

* Arm AArch64: minimize changes in ggml_compute_forward_mul_mat

* Arm AArch64: minor code refactoring, and add reference scalar code to quantize routines for new quant types

* Arm AArch64: minor code refactoring

* Arm AArch64: minor code refactoring

* Arm AArch64: minor code refactoring

* rebase on the latest master commit 3fd62a6 and adapt to the new directory structure

* Arm AArch64: remove a redundant comment

* Arm AArch64: add pragma in ggml-aarch64.c to turn -Woverlength-strings warning off

* Arm AArch64: use __aarch64__ check to guard 64-bit neon kernels

* Arm AArch64: update docs/build.md README to include compile time flags for buiilding the Q4_0_4_4 quant type
---
 Makefile                       |   10 +-
 Package.swift                  |    1 +
 docs/build.md                  |    2 +
 examples/quantize/quantize.cpp |    3 +
 ggml/include/ggml.h            |   17 +
 ggml/src/CMakeLists.txt        |    1 +
 ggml/src/ggml-aarch64.c        | 2187 ++++++++++++++++++++++++++++++++
 ggml/src/ggml-aarch64.h        |   39 +
 ggml/src/ggml-common.h         |   24 +
 ggml/src/ggml-impl.h           |    4 +
 ggml/src/ggml-quants.c         |  122 +-
 ggml/src/ggml.c                |  150 ++-
 include/llama.h                |    3 +
 src/llama.cpp                  |   24 +-
 14 files changed, 2534 insertions(+), 53 deletions(-)
 create mode 100644 ggml/src/ggml-aarch64.c
 create mode 100644 ggml/src/ggml-aarch64.h

diff --git a/Makefile b/Makefile
index 1041200905371b..b70ebaed546386 100644
--- a/Makefile
+++ b/Makefile
@@ -835,7 +835,8 @@ OBJ_GGML += \
 	ggml/src/ggml.o \
 	ggml/src/ggml-alloc.o \
 	ggml/src/ggml-backend.o \
-	ggml/src/ggml-quants.o
+	ggml/src/ggml-quants.o \
+	ggml/src/ggml-aarch64.o
 
 OBJ_LLAMA = \
 	src/llama.o \
@@ -969,6 +970,13 @@ ggml/src/ggml-quants.o: \
 	ggml/src/ggml-common.h
 	$(CC) $(CFLAGS)    -c $< -o $@
 
+ggml/src/ggml-aarch64.o: \
+	ggml/src/ggml-aarch64.c \
+	ggml/include/ggml.h \
+	ggml/src/ggml-aarch64.h \
+	ggml/src/ggml-common.h
+	$(CC) $(CFLAGS)    -c $< -o $@
+
 ggml/src/ggml-blas.o: \
 	ggml/src/ggml-blas.cpp \
 	ggml/include/ggml-blas.h
diff --git a/Package.swift b/Package.swift
index 77fed86df31055..d40a48385f8c7a 100644
--- a/Package.swift
+++ b/Package.swift
@@ -10,6 +10,7 @@ var sources = [
     "ggml/src/ggml-alloc.c",
     "ggml/src/ggml-backend.c",
     "ggml/src/ggml-quants.c",
+    "ggml/src/ggml-aarch64.c",
 ]
 
 var resources: [Resource] = []
diff --git a/docs/build.md b/docs/build.md
index bf41bfdf9c2f88..d70f72f4c7b82e 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -28,6 +28,7 @@ In order to build llama.cpp you have four different options.
         ```
 
   - Notes:
+    - For `Q4_0_4_4` quantization type build, add the `GGML_NO_LLAMAFILE=1` flag. For example, use `make GGML_NO_LLAMAFILE=1`.
     - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel.
     - For faster repeated compilation, install [ccache](https://ccache.dev/).
     - For debug builds, run `make LLAMA_DEBUG=1`
@@ -41,6 +42,7 @@ In order to build llama.cpp you have four different options.
 
   **Notes**:
 
+    - For `Q4_0_4_4` quantization type build, add the `-DGGML_LLAMAFILE=OFF` cmake option. For example, use `cmake -B build -DGGML_LLAMAFILE=OFF`.
     - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
     - For faster repeated compilation, install [ccache](https://ccache.dev/).
     - For debug builds, there are two cases:
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index 76e2052d55d791..1578c4afb5dfab 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -46,6 +46,9 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 5.33G, +0.0569 ppl @ Llama-3-8B",  },
     { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 6.14G, +0.0217 ppl @ Llama-3-8B",  },
     { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 7.96G, +0.0026 ppl @ Llama-3-8B",  },
+    { "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
+    { "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
+    { "Q4_0_8_8", LLAMA_FTYPE_MOSTLY_Q4_0_8_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
     { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "14.00G, +0.0020 ppl @ Mistral-7B",  },
     { "BF16",   LLAMA_FTYPE_MOSTLY_BF16,   "14.00G, -0.0050 ppl @ Mistral-7B",  },
     { "F32",    LLAMA_FTYPE_ALL_F32,       "26.00G              @ 7B",          },
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index d895c9acdb5963..1e367753738d9f 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -383,6 +383,9 @@ extern "C" {
         GGML_TYPE_F64     = 28,
         GGML_TYPE_IQ1_M   = 29,
         GGML_TYPE_BF16    = 30,
+        GGML_TYPE_Q4_0_4_4 = 31,
+        GGML_TYPE_Q4_0_4_8 = 32,
+        GGML_TYPE_Q4_0_8_8 = 33,
         GGML_TYPE_COUNT,
     };
 
@@ -424,6 +427,9 @@ extern "C" {
         GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
         GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0_4_4 = 25, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0_4_8 = 26, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0_8_8 = 27, // except 1d tensors
     };
 
     // available tensor operations:
@@ -2406,6 +2412,12 @@ extern "C" {
     typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int64_t k);
     typedef void (*ggml_vec_dot_t)   (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
                                       const void * GGML_RESTRICT y, size_t by, int nrc);
+    typedef void (*ggml_from_float_to_mat_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int64_t nr,
+                                             int64_t k, int64_t bx);
+    typedef void (*ggml_gemv_t)      (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
+                                      const void * GGML_RESTRICT y, int nr, int nc);
+    typedef void (*ggml_gemm_t)      (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
+                                      const void * GGML_RESTRICT y, int nr, int nc);
 
     typedef struct {
         const char      * type_name;
@@ -2418,6 +2430,11 @@ extern "C" {
         ggml_vec_dot_t    vec_dot;
         enum ggml_type    vec_dot_type;
         int64_t           nrows; // number of rows to process simultaneously;
+        int64_t           ncols; // number of columns to process simultaneously;
+        int64_t           interleave_blcksize; // interleave elements in blocks of interleave_blcksize;
+        ggml_from_float_to_mat_t from_float_to_mat;
+        ggml_gemv_t       gemv;
+        ggml_gemm_t       gemm;
     } ggml_type_traits_t;
 
     GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index c6694df678fff8..aae5b8e9fe35c4 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -1153,6 +1153,7 @@ add_library(ggml
             ${GGML_SOURCES_ROCM}      ${GGML_HEADERS_ROCM}
             ${GGML_SOURCES_BLAS}      ${GGML_HEADERS_BLAS}
             ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE}
+            ggml-aarch64.c            ggml-aarch64.h
             )
 
 if (EMSCRIPTEN)
diff --git a/ggml/src/ggml-aarch64.c b/ggml/src/ggml-aarch64.c
new file mode 100644
index 00000000000000..008718634fe0d2
--- /dev/null
+++ b/ggml/src/ggml-aarch64.c
@@ -0,0 +1,2187 @@
+// SPDX-FileCopyrightText: Copyright 2024 Arm Ltd.
+#define GGML_COMMON_IMPL_C
+#include "ggml-common.h"
+
+#include "ggml-quants.h"
+#include "ggml-impl.h"
+
+#include <math.h>
+#include <string.h>
+#include <assert.h>
+#include <float.h>
+#include <stdlib.h> // for qsort
+#include <stdio.h>  // for GGML_ASSERT
+
+#include "ggml-aarch64.h"
+
+#pragma GCC diagnostic ignored "-Woverlength-strings"
+
+#define UNUSED GGML_UNUSED
+
+// Functions to create the interleaved data layout formats
+
+// interleave 4 block_q4_0s in blocks of interleave_blcksize
+// returns an interleaved block_q4_0x4
+// in the interleaved block_q4_0x4, place deltas for 4 block_q4_0 blocks
+// first, then interleave quants from 4 block_q4_0s in blocks of interleave_blcksize
+//
+// - in                  : an array of block_q4_0 pointers
+// - interleave_blcksize : the block_q4_0 quants bytes are interleaved in blocks of
+//                         interleave_blcksize bytes
+// - xor_mask            : the mask to convert the nibbles in block_q4_0 quants bytes
+//                         from bias offset form to pure sign form (this saves subtract
+//                         operations durin unpacking)
+//
+static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int interleave_blcksize, unsigned int xor_mask) {
+    block_q4_0x4 out;
+
+    for (int i = 0; i < 4; i++) {
+        out.d[i] = in[i].d;
+    }
+
+    for (int i = 0; i < QK4_0 * 2; i++) {
+        int src_offset = (i / (4 * interleave_blcksize)) * interleave_blcksize;
+        int src_id = (i % (4 * interleave_blcksize)) / interleave_blcksize;
+        src_offset += (i % interleave_blcksize);
+
+        out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask;
+    }
+
+    return out;
+}
+
+// interleave 8 block_q4_0s in blocks of interleave_blcksize
+// returns an interleaved block_q4_0x8
+// in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks
+// first, then interleave quants from 8 block_q4_0s in blocks of interleave_blcksize
+static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int interleave_blcksize, unsigned int xor_mask) {
+    block_q4_0x8 out;
+
+    for (int i = 0; i < 8; i++) {
+        out.d[i] = in[i].d;
+    }
+
+    for (int i = 0; i < QK4_0 * 4; i++) {
+        int src_offset = (i / (8 * interleave_blcksize)) * interleave_blcksize;
+        int src_id = (i % (8 * interleave_blcksize)) / interleave_blcksize;
+        src_offset += (i % interleave_blcksize);
+
+        out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask;
+    }
+
+    return out;
+}
+
+void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k) {
+    assert(QK8_0 == 32);
+    assert(k % QK8_0 == 0);
+    const int nb = k / QK8_0;
+
+    block_q8_0x4 * restrict y = (block_q8_0x4 *) vy;
+
+#if defined(__ARM_NEON)
+    float32x4_t srcv[4][8];
+    float id[4];
+
+    for (int i = 0; i < nb; i++) {
+        float32x4_t asrcv[8];
+        float32x4_t amaxv[8];
+
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j);
+            for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]);
+
+            for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]);
+            for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]);
+            for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]);
+
+            const float amax = vmaxvq_f32(amaxv[0]);
+
+            const float d = amax / ((1 << 7) - 1);
+            id[row_iter] = d ? 1.0f / d : 0.0f;
+
+            y[i].d[row_iter] = GGML_FP32_TO_FP16(d);
+        }
+
+        for (int j = 0; j < 8; j++) {
+            float32x4_t v = vmulq_n_f32(srcv[0][j], id[0]);
+            int32x4_t vi = vcvtnq_s32_f32(v);
+            y[i].qs[16 * j + 0] = vgetq_lane_s32(vi, 0);
+            y[i].qs[16 * j + 1] = vgetq_lane_s32(vi, 1);
+            y[i].qs[16 * j + 2] = vgetq_lane_s32(vi, 2);
+            y[i].qs[16 * j + 3] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[1][j], id[1]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[16 * j + 4] = vgetq_lane_s32(vi, 0);
+            y[i].qs[16 * j + 5] = vgetq_lane_s32(vi, 1);
+            y[i].qs[16 * j + 6] = vgetq_lane_s32(vi, 2);
+            y[i].qs[16 * j + 7] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[2][j], id[2]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[16 * j + 8] = vgetq_lane_s32(vi, 0);
+            y[i].qs[16 * j + 9] = vgetq_lane_s32(vi, 1);
+            y[i].qs[16 * j + 10] = vgetq_lane_s32(vi, 2);
+            y[i].qs[16 * j + 11] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[3][j], id[3]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[16 * j + 12] = vgetq_lane_s32(vi, 0);
+            y[i].qs[16 * j + 13] = vgetq_lane_s32(vi, 1);
+            y[i].qs[16 * j + 14] = vgetq_lane_s32(vi, 2);
+            y[i].qs[16 * j + 15] = vgetq_lane_s32(vi, 3);
+        }
+    }
+#else
+    // scalar
+    const int interleave_blcksize = 4;
+    float srcv[4][QK8_0];
+    float id[4];
+
+    for (int i = 0; i < nb; i++) {
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            float amax = 0.0f; // absolute max
+
+            for (int j = 0; j < QK8_0; j++) {
+                srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j];
+                amax = MAX(amax, fabsf(srcv[row_iter][j]));
+            }
+
+            const float d = amax / ((1 << 7) - 1);
+            id[row_iter] = d ? 1.0f / d : 0.0f;
+
+            y[i].d[row_iter] = GGML_FP32_TO_FP16(d);
+        }
+
+        for (int j = 0; j < QK8_0 * 4; j++) {
+            int src_offset = (j / (4 * interleave_blcksize)) * interleave_blcksize;
+            int src_id = (j % (4 * interleave_blcksize)) / interleave_blcksize;
+            src_offset += (j % interleave_blcksize);
+
+            float x0 = srcv[src_id][src_offset] * id[src_id];
+            y[i].qs[j] = roundf(x0);;
+        }
+    }
+#endif
+}
+
+void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k) {
+    assert(QK8_0 == 32);
+    assert(k % QK8_0 == 0);
+    const int nb = k / QK8_0;
+
+    block_q8_0x4 * restrict y = (block_q8_0x4 *) vy;
+
+#if defined(__ARM_NEON)
+    float32x4_t srcv[4][8];
+    float id[4];
+
+    for (int i = 0; i < nb; i++) {
+        float32x4_t asrcv[8];
+        float32x4_t amaxv[8];
+
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            for (int j = 0; j < 8; j++) srcv[row_iter][j] = vld1q_f32(x + row_iter * k + i * 32 + 4 * j);
+            for (int j = 0; j < 8; j++) asrcv[j] = vabsq_f32(srcv[row_iter][j]);
+
+            for (int j = 0; j < 4; j++) amaxv[2 * j] = vmaxq_f32(asrcv[2 * j], asrcv[2 * j + 1]);
+            for (int j = 0; j < 2; j++) amaxv[4 * j] = vmaxq_f32(amaxv[4 * j], amaxv[4 * j + 2]);
+            for (int j = 0; j < 1; j++) amaxv[8 * j] = vmaxq_f32(amaxv[8 * j], amaxv[8 * j + 4]);
+
+            const float amax = vmaxvq_f32(amaxv[0]);
+
+            const float d = amax / ((1 << 7) - 1);
+            id[row_iter] = d ? 1.0f / d : 0.0f;
+
+            y[i].d[row_iter] = GGML_FP32_TO_FP16(d);
+        }
+
+        for (int j = 0; j < 4; j++) {
+            float32x4_t v = vmulq_n_f32(srcv[0][2 * j], id[0]);
+            int32x4_t vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 0] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 1] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 2] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 3] = vgetq_lane_s32(vi, 3);
+            v = vmulq_n_f32(srcv[0][2 * j + 1], id[0]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 4] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 5] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 6] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 7] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[1][2 * j], id[1]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 8] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 9] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 10] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 11] = vgetq_lane_s32(vi, 3);
+            v = vmulq_n_f32(srcv[1][2 * j + 1], id[1]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 12] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 13] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 14] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 15] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[2][2 * j], id[2]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 16] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 17] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 18] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 19] = vgetq_lane_s32(vi, 3);
+            v = vmulq_n_f32(srcv[2][2 * j + 1], id[2]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 20] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 21] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 22] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 23] = vgetq_lane_s32(vi, 3);
+
+            v = vmulq_n_f32(srcv[3][2 * j], id[3]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 24] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 25] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 26] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 27] = vgetq_lane_s32(vi, 3);
+            v = vmulq_n_f32(srcv[3][2 * j + 1], id[3]);
+            vi = vcvtnq_s32_f32(v);
+            y[i].qs[32 * j + 28] = vgetq_lane_s32(vi, 0);
+            y[i].qs[32 * j + 29] = vgetq_lane_s32(vi, 1);
+            y[i].qs[32 * j + 30] = vgetq_lane_s32(vi, 2);
+            y[i].qs[32 * j + 31] = vgetq_lane_s32(vi, 3);
+        }
+    }
+#else
+    // scalar
+    const int interleave_blcksize = 8;
+    float srcv[4][QK8_0];
+    float id[4];
+
+    for (int i = 0; i < nb; i++) {
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            float amax = 0.0f; // absolute max
+
+            for (int j = 0; j < QK8_0; j++) {
+                srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j];
+                amax = MAX(amax, fabsf(srcv[row_iter][j]));
+            }
+
+            const float d = amax / ((1 << 7) - 1);
+            id[row_iter] = d ? 1.0f / d : 0.0f;
+
+            y[i].d[row_iter] = GGML_FP32_TO_FP16(d);
+        }
+
+        for (int j = 0; j < QK8_0 * 4; j++) {
+            int src_offset = (j / (4 * interleave_blcksize)) * interleave_blcksize;
+            int src_id = (j % (4 * interleave_blcksize)) / interleave_blcksize;
+            src_offset += (j % interleave_blcksize);
+
+            float x0 = srcv[src_id][src_offset] * id[src_id];
+            y[i].qs[j] = roundf(x0);;
+        }
+    }
+#endif
+}
+
+void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t interleave_blcksize) {
+    assert(nrow == 4);
+    UNUSED(nrow);
+    if (interleave_blcksize == 4) quantize_q8_0_4x4(x, vy, n_per_row);
+    else if (interleave_blcksize == 8) quantize_q8_0_4x8(x, vy, n_per_row);
+    else assert(false);
+}
+
+static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, int nrows_interleaved, int interleave_blcksize) {
+    assert(n_per_row % QK4_0 == 0);
+    const int nb = n_per_row / QK4_0;
+
+    void * out_ptr = NULL;
+    if (nrows_interleaved == 8) {
+        out_ptr = (block_q4_0x8 *) dst;
+    }
+    else if (nrows_interleaved == 4) {
+        out_ptr = (block_q4_0x4 *) dst;
+    }
+    assert(nrows_interleaved <= 8);
+    block_q4_0 dst_tmp[8];
+
+    for (int b = 0; b < (nrow * n_per_row); b += nrows_interleaved * n_per_row) {
+
+        for (int64_t x = 0; x < nb; x++) {
+
+            for (int i  = 0; i < nrows_interleaved; i++ ) {
+                quantize_row_q4_0_reference(src + b + i * n_per_row + x * QK4_0, (block_q4_0 *) dst_tmp + i, QK4_0);
+            }
+
+            if (nrows_interleaved == 8) {
+                *(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, interleave_blcksize, 0x88);
+                out_ptr = (block_q4_0x8 *) out_ptr + 1;
+            }
+            else if (nrows_interleaved == 4) {
+                *(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, interleave_blcksize, 0x88);
+                out_ptr = (block_q4_0x4 *) out_ptr + 1;
+            }
+        }
+    }
+
+    return ((nrow * n_per_row) / QK4_0 * sizeof(block_q4_0));
+}
+
+size_t quantize_q4_0_4x4(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 4, 4);
+    }
+    else {
+        assert(false);
+        return 0;
+    }
+}
+
+size_t quantize_q4_0_4x8(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 4, 8);
+    }
+    else {
+        assert(false);
+        return 0;
+    }
+}
+
+size_t quantize_q4_0_8x8(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    if (!quant_weights) {
+        return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 8, 8);
+    }
+    else {
+        assert(false);
+        return 0;
+    }
+}
+
+void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 4;
+
+    assert (n % qk == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE)
+    if (svcntw() == 8) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (svcntw() == 8)) &&
+                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+    GGML_ASSERT(!(ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) &&
+                "__ARM_NEON and __ARM_FEATURE_MATMUL_INT8 defined, use the Q4_0_4_8 quantization format for optimal performance");
+#elif defined(__ARM_NEON) && defined(__aarch64__)
+    const void * b_ptr = vx;
+    const void * a_ptr = vy;
+    float * res_ptr = s;
+
+    __asm__ __volatile__(
+        "movi v31.16b, #0x4\n"
+        "movi v30.16b, #0xf0\n"
+        "add %x[b_ptr], %x[b_ptr], #0x8\n"
+        "1:"  // Column loop
+        "add x22, %x[a_ptr], #0x2\n"
+        "movi v29.16b, #0x0\n"
+        "mov x21, %x[nb]\n"
+        "2:"  // Block loop
+        "ldr q28, [%x[b_ptr], #0x0]\n"
+        "ldr q27, [x22, #0x0]\n"
+        "movi v26.4s, #0x0\n"
+        "sub x20, x22, #0x2\n"
+        "ldr q25, [x22, #0x10]\n"
+        "ldr q24, [%x[b_ptr], #0x10]\n"
+        "sub x21, x21, #0x1\n"
+        "add x22, x22, #0x22\n"
+        "ldr q23, [%x[b_ptr], #0x20]\n"
+        "ldr q22, [%x[b_ptr], #0x30]\n"
+        "ld1r { v21.8h }, [x20]\n"
+        "ldr q20, [%x[b_ptr], #-0x8]\n"
+        "sshl v16.16b, v28.16b, v31.16b\n"
+        "and v28.16b, v28.16b, v30.16b\n"
+        "sshl v19.16b, v24.16b, v31.16b\n"
+        "and v24.16b, v24.16b, v30.16b\n"
+        "add %x[b_ptr], %x[b_ptr], #0x48\n"
+        "sshl v18.16b, v23.16b, v31.16b\n"
+        "and v23.16b, v23.16b, v30.16b\n"
+        ".inst 0x4f9be21a  // sdot v26.4s, v16.16b, v27.4b[0]\n"
+        "sshl v17.16b, v22.16b, v31.16b\n"
+        "and v22.16b, v22.16b, v30.16b\n"
+        "fcvtl v21.4s, v21.4h\n"
+        "fcvtl v16.4s, v20.4h\n"
+        ".inst 0x4f99e39a  // sdot v26.4s, v28.16b, v25.4b[0]\n"
+        "fmul v16.4s, v16.4s, v21.4s\n"
+        ".inst 0x4fbbe27a  // sdot v26.4s, v19.16b, v27.4b[1]\n"
+        ".inst 0x4fb9e31a  // sdot v26.4s, v24.16b, v25.4b[1]\n"
+        ".inst 0x4f9bea5a  // sdot v26.4s, v18.16b, v27.4b[2]\n"
+        ".inst 0x4f99eafa  // sdot v26.4s, v23.16b, v25.4b[2]\n"
+        ".inst 0x4fbbea3a  // sdot v26.4s, v17.16b, v27.4b[3]\n"
+        ".inst 0x4fb9eada  // sdot v26.4s, v22.16b, v25.4b[3]\n"
+        "scvtf v26.4s, v26.4s, #0x4\n"
+        "fmla v29.4s, v26.4s, v16.4s\n"
+        "cbnz x21, 2b\n"
+        "sub %x[nc], %x[nc], #0x4\n"
+        "str q29, [%x[res_ptr], #0x0]\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "cbnz %x[nc], 1b\n"
+        : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc)
+        : [a_ptr] "r" (a_ptr), [nb] "r" (nb)
+        : "memory", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22"
+    );
+#else
+    float sumf[4];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
+                    }
+                    sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
+#endif
+}
+
+void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 8;
+
+    assert (n % qk == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE)
+    if (svcntw() == 8) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (svcntw() == 8)) &&
+                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+    const void * b_ptr = vx;
+    const void * a_ptr = vy;
+    float * res_ptr = s;
+
+    __asm__ __volatile__(
+        "movi v2.16b, #0x4\n"
+        "movi v1.16b, #0xf0\n"
+        "add %x[b_ptr], %x[b_ptr], #0x8\n"
+        "1:"  // Column loop
+        "add x23, %x[a_ptr], #0x2\n"
+        "movi v0.16b, #0x0\n"
+        "mov x22, %x[nb]\n"
+        "2:"  // Block loop
+        "ldr q31, [%x[b_ptr], #0x0]\n"
+        "ldr q30, [%x[b_ptr], #0x10]\n"
+        "mov x21, x23\n"
+        "movi v29.4s, #0x0\n"
+        "ldr q28, [%x[b_ptr], #0x20]\n"
+        "ldr q27, [%x[b_ptr], #0x30]\n"
+        "movi v26.4s, #0x0\n"
+        "sub x20, x23, #0x2\n"
+        "ld1r { v25.8h }, [x20]\n"
+        "ldr q24, [%x[b_ptr], #-0x8]\n"
+        "sub x22, x22, #0x1\n"
+        "add x23, x23, #0x22\n"
+        "ld1r { v23.2d }, [x21], #0x8\n"
+        "sshl v22.16b, v31.16b, v2.16b\n"
+        "sshl v16.16b, v30.16b, v2.16b\n"
+        "add %x[b_ptr], %x[b_ptr], #0x48\n"
+        "ld1r { v21.2d }, [x21], #0x8\n"
+        "sshl v20.16b, v28.16b, v2.16b\n"
+        "sshl v19.16b, v27.16b, v2.16b\n"
+        "ld1r { v18.2d }, [x21], #0x8\n"
+        "ld1r { v17.2d }, [x21], #0x8\n"
+        "and v31.16b, v31.16b, v1.16b\n"
+        "and v30.16b, v30.16b, v1.16b\n"
+        ".inst 0x4e9796dd  // sdot v29.4s, v22.16b, v23.16b\n"
+        ".inst 0x4e97961a  // sdot v26.4s, v16.16b, v23.16b\n"
+        "and v28.16b, v28.16b, v1.16b\n"
+        "and v27.16b, v27.16b, v1.16b\n"
+        "fcvtl v25.4s, v25.4h\n"
+        "fcvtl v16.4s, v24.4h\n"
+        ".inst 0x4e95969d  // sdot v29.4s, v20.16b, v21.16b\n"
+        ".inst 0x4e95967a  // sdot v26.4s, v19.16b, v21.16b\n"
+        "fmul v16.4s, v16.4s, v25.4s\n"
+        ".inst 0x4e9297fd  // sdot v29.4s, v31.16b, v18.16b\n"
+        ".inst 0x4e9297da  // sdot v26.4s, v30.16b, v18.16b\n"
+        ".inst 0x4e91979d  // sdot v29.4s, v28.16b, v17.16b\n"
+        ".inst 0x4e91977a  // sdot v26.4s, v27.16b, v17.16b\n"
+        "addp v29.4s, v29.4s, v26.4s\n"
+        "scvtf v29.4s, v29.4s, #0x4\n"
+        "fmla v0.4s, v29.4s, v16.4s\n"
+        "cbnz x22, 2b\n"
+        "sub %x[nc], %x[nc], #0x4\n"
+        "str q0, [%x[res_ptr], #0x0]\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "cbnz %x[nc], 1b\n"
+        : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc)
+        : [a_ptr] "r" (a_ptr), [nb] "r" (nb)
+        : "memory", "v0", "v1", "v2", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x20", "x21", "x22", "x23"
+    );
+#elif defined(__ARM_NEON) && defined(__aarch64__)
+    GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) &&
+                "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal "
+                "performance");
+#else
+    float sumf[4];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
+                    }
+                    sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
+#endif
+}
+
+void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 8;
+
+    assert (n % qk == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE)
+    if (svcntw() == 8) {
+        const void * b_ptr = vx;
+        const void * a_ptr = vy;
+        float * res_ptr = s;
+
+        __asm__ __volatile__(
+            "ptrue p0.b\n"
+            "add %x[b_ptr], %x[b_ptr], #0x10\n"
+            "1:"  // Column loop
+            "add x22, %x[a_ptr], #0x2\n"
+            "mov z31.b, #0x0\n"
+            "mov x21, %x[nb]\n"
+            "2:"  // Block loop
+            "ld1b { z30.b }, p0/Z, [%x[b_ptr]]\n"
+            "ld1b { z29.b }, p0/Z, [%x[b_ptr], #1, MUL VL]\n"
+            "mov z28.s, #0x0\n"
+            "mov z27.s, #0x0\n"
+            "ld1rd { z26.d }, p0/Z, [x22]\n"
+            "ld1b { z25.b }, p0/Z, [%x[b_ptr], #2, MUL VL]\n"
+            "sub x20, x22, #0x2\n"
+            "sub x21, x21, #0x1\n"
+            "ld1b { z24.b }, p0/Z, [%x[b_ptr], #3, MUL VL]\n"
+            "ld1rd { z23.d }, p0/Z, [x22, #8]\n"
+            "lsl z22.b, z30.b, #0x4\n"
+            "lsl z16.b, z29.b, #0x4\n"
+            "and z30.b, z30.b, #0xf0\n"
+            "and z29.b, z29.b, #0xf0\n"
+            "ld1rd { z21.d }, p0/Z, [x22, #16]\n"
+            "ld1rd { z20.d }, p0/Z, [x22, #24]\n"
+            "lsl z19.b, z25.b, #0x4\n"
+            "and z25.b, z25.b, #0xf0\n"
+            "ld1rh { z17.h }, p0/Z, [x20]\n"
+            "ld1h { z18.s }, p0/Z, [%x[b_ptr], #-1, MUL VL]\n"
+            "sdot z28.s, z22.b, z26.b\n"
+            "sdot z27.s, z16.b, z26.b\n"
+            "lsl z16.b, z24.b, #0x4\n"
+            "add x22, x22, #0x22\n"
+            "and z24.b, z24.b, #0xf0\n"
+            "add %x[b_ptr], %x[b_ptr], #0x90\n"
+            "fcvt z17.s, p0/m, z17.h\n"
+            "fcvt z18.s, p0/m, z18.h\n"
+            "sdot z28.s, z19.b, z23.b\n"
+            "sdot z27.s, z16.b, z23.b\n"
+            "fmul z18.s, z18.s, z17.s\n"
+            "sdot z28.s, z30.b, z21.b\n"
+            "sdot z27.s, z29.b, z21.b\n"
+            "sdot z28.s, z25.b, z20.b\n"
+            "sdot z27.s, z24.b, z20.b\n"
+            "uzp1 z17.s, z28.s, z27.s\n"
+            "uzp2 z16.s, z28.s, z27.s\n"
+            "add z17.s, z17.s, z16.s\n"
+            "asr z17.s, z17.s, #0x4\n"
+            "scvtf z17.s, p0/m, z17.s\n"
+            "fmla z31.s, p0/M, z17.s, z18.s\n"
+            "cbnz x21, 2b\n"
+            "sub %x[nc], %x[nc], #0x8\n"
+            "st1w { z31.s }, p0, [%x[res_ptr]]\n"
+            "add %x[res_ptr], %x[res_ptr], #0x20\n"
+            "cbnz %x[nc], 1b\n"
+            : [b_ptr] "+&r" (b_ptr), [res_ptr] "+&r" (res_ptr), [nc] "+&r" (nc)
+            : [a_ptr] "r" (a_ptr), [nb] "r" (nb)
+            : "memory", "p0", "x20", "x21", "x22", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31"
+        );
+        return;
+    }
+    else if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
+        GGML_ASSERT((ggml_cpu_has_sve() && (svcntw() == 8)) &&
+                    "__ARM_FEATURE_SVE for vector size of 256-bits not defined, use the Q4_0_4_8 quantization format for optimal "
+                    "performance");
+    }
+    else if (ggml_cpu_has_neon()) {
+        GGML_ASSERT(((ggml_cpu_has_sve() && (svcntw() == 8)) || ggml_cpu_has_matmul_int8()) &&
+                    "__ARM_FEATURE_SVE for vector size of 256-bits and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 "
+                    "quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+    GGML_ASSERT(ggml_cpu_has_sve() &&
+                "__ARM_FEATURE_SVE not defined, use the Q4_0_4_8 quantization format for optimal performance");
+#elif defined(__ARM_NEON) && defined(__aarch64__)
+    GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) &&
+                "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal "
+                "performance");
+#else
+    float sumf[8];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
+                    }
+                    sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
+#endif
+}
+
+void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 4;
+
+    assert (n % qk == 0);
+    assert (nr % 4 == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
+    if (svcntw() == 8) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (svcntw() == 8)) &&
+                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+    GGML_ASSERT(!(ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) &&
+                "__ARM_NEON and __ARM_FEATURE_MATMUL_INT8 defined, use the Q4_0_4_8 quantization format for optimal performance");
+#elif defined(__ARM_NEON) && defined(__aarch64__)
+    const void * b_ptr = vx;
+    const void * a_ptr = vy;
+    float * res_ptr = s;
+    size_t res_stride = bs * sizeof(float);
+
+    __asm__ __volatile__(
+        "mov x10, %x[nr]\n"
+        "mov x9, #0x88\n"
+        "cmp x10, #0x10\n"
+        "mul x9, %x[nb], x9\n"
+        "blt 4f\n"
+        "1:"  // Row loop
+        "add x28, %x[b_ptr], #0x8\n"
+        "mov x27, %x[nc]\n"
+        "add x26, %x[res_ptr], %x[res_stride], LSL #4\n"
+        "2:"  // Column loop
+        "add x25, %x[a_ptr], #0x8\n"
+        "movi v15.16b, #0x0\n"
+        "movi v19.16b, #0x0\n"
+        "mov x24, %x[nb]\n"
+        "add x23, x25, x9\n"
+        "movi v18.16b, #0x0\n"
+        "movi v14.16b, #0x0\n"
+        "add x22, x23, x9\n"
+        "movi v11.16b, #0x0\n"
+        "movi v13.16b, #0x0\n"
+        "add x21, x22, x9\n"
+        "movi v23.16b, #0x0\n"
+        "movi v16.16b, #0x0\n"
+        "movi v25.16b, #0x0\n"
+        "movi v7.16b, #0x0\n"
+        "movi v0.16b, #0x0\n"
+        "movi v4.16b, #0x0\n"
+        "movi v5.16b, #0x0\n"
+        "movi v21.16b, #0x0\n"
+        "movi v8.16b, #0x0\n"
+        "movi v1.16b, #0x0\n"
+        "3:"  // Block loop
+        "ldr q3, [x28, #0x0]\n"
+        "ldr q31, [x25, #0x0]\n"
+        "movi v28.16b, #0x4\n"
+        "movi v10.4s, #0x0\n"
+        "ldr q22, [x28, #0x10]\n"
+        "ldr q6, [x25, #0x10]\n"
+        "movi v29.4s, #0x0\n"
+        "movi v9.4s, #0x0\n"
+        "ldr q27, [x28, #0x20]\n"
+        "ldr q30, [x28, #0x30]\n"
+        "movi v20.4s, #0x0\n"
+        "movi v24.16b, #0xf0\n"
+        "ldr d2, [x25, #-0x8]\n"
+        "ldr d26, [x23, #-0x8]\n"
+        "sshl v12.16b, v3.16b, v28.16b\n"
+        "sub x20, x28, #0x8\n"
+        "ldr d17, [x20, #0x0]\n"
+        "and v3.16b, v3.16b, v24.16b\n"
+        "subs x24, x24, #0x1\n"
+        "add x28, x28, #0x48\n"
+        ".inst 0x4f9fe18a  // sdot v10.4s, v12.16b, v31.4b[0]\n"
+        ".inst 0x4fbfe19d  // sdot v29.4s, v12.16b, v31.4b[1]\n"
+        ".inst 0x4f9fe989  // sdot v9.4s, v12.16b, v31.4b[2]\n"
+        ".inst 0x4fbfe994  // sdot v20.4s, v12.16b, v31.4b[3]\n"
+        "sshl v31.16b, v22.16b, v28.16b\n"
+        "and v22.16b, v22.16b, v24.16b\n"
+        "fcvtl v17.4s, v17.4h\n"
+        "fcvtl v2.4s, v2.4h\n"
+        "fcvtl v26.4s, v26.4h\n"
+        ".inst 0x4f86e3ea  // sdot v10.4s, v31.16b, v6.4b[0]\n"
+        ".inst 0x4fa6e3fd  // sdot v29.4s, v31.16b, v6.4b[1]\n"
+        ".inst 0x4f86ebe9  // sdot v9.4s, v31.16b, v6.4b[2]\n"
+        ".inst 0x4fa6ebf4  // sdot v20.4s, v31.16b, v6.4b[3]\n"
+        "sshl v6.16b, v27.16b, v28.16b\n"
+        "sshl v28.16b, v30.16b, v28.16b\n"
+        "and v27.16b, v27.16b, v24.16b\n"
+        "and v30.16b, v30.16b, v24.16b\n"
+        "ldr q24, [x25, #0x20]\n"
+        ".inst 0x4f98e0ca  // sdot v10.4s, v6.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e0dd  // sdot v29.4s, v6.16b, v24.4b[1]\n"
+        ".inst 0x4f98e8c9  // sdot v9.4s, v6.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e8d4  // sdot v20.4s, v6.16b, v24.4b[3]\n"
+        "ldr q24, [x25, #0x30]\n"
+        ".inst 0x4f98e38a  // sdot v10.4s, v28.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e39d  // sdot v29.4s, v28.16b, v24.4b[1]\n"
+        ".inst 0x4f98eb89  // sdot v9.4s, v28.16b, v24.4b[2]\n"
+        ".inst 0x4fb8eb94  // sdot v20.4s, v28.16b, v24.4b[3]\n"
+        "ldr q24, [x25, #0x40]\n"
+        ".inst 0x4f98e06a  // sdot v10.4s, v3.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e07d  // sdot v29.4s, v3.16b, v24.4b[1]\n"
+        ".inst 0x4f98e869  // sdot v9.4s, v3.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e874  // sdot v20.4s, v3.16b, v24.4b[3]\n"
+        "ldr q24, [x25, #0x50]\n"
+        ".inst 0x4f98e2ca  // sdot v10.4s, v22.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e2dd  // sdot v29.4s, v22.16b, v24.4b[1]\n"
+        ".inst 0x4f98eac9  // sdot v9.4s, v22.16b, v24.4b[2]\n"
+        ".inst 0x4fb8ead4  // sdot v20.4s, v22.16b, v24.4b[3]\n"
+        "ldr q24, [x25, #0x60]\n"
+        ".inst 0x4f98e36a  // sdot v10.4s, v27.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e37d  // sdot v29.4s, v27.16b, v24.4b[1]\n"
+        ".inst 0x4f98eb69  // sdot v9.4s, v27.16b, v24.4b[2]\n"
+        ".inst 0x4fb8eb74  // sdot v20.4s, v27.16b, v24.4b[3]\n"
+        "ldr q24, [x25, #0x70]\n"
+        "add x25, x25, #0x88\n"
+        ".inst 0x4f98e3ca  // sdot v10.4s, v30.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e3dd  // sdot v29.4s, v30.16b, v24.4b[1]\n"
+        ".inst 0x4f98ebc9  // sdot v9.4s, v30.16b, v24.4b[2]\n"
+        ".inst 0x4fb8ebd4  // sdot v20.4s, v30.16b, v24.4b[3]\n"
+        "fmul v24.4s, v17.4s, v2.s[0]\n"
+        "scvtf v10.4s, v10.4s, #0x4\n"
+        "scvtf v29.4s, v29.4s, #0x4\n"
+        "scvtf v9.4s, v9.4s, #0x4\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "fmla v15.4s, v10.4s, v24.4s\n"
+        "ldr q24, [x23, #0x0]\n"
+        "fmul v10.4s, v17.4s, v2.s[1]\n"
+        "fmla v19.4s, v29.4s, v10.4s\n"
+        "ldr q10, [x23, #0x10]\n"
+        "fmul v29.4s, v17.4s, v2.s[2]\n"
+        "fmul v2.4s, v17.4s, v2.s[3]\n"
+        "fmla v18.4s, v9.4s, v29.4s\n"
+        "movi v9.4s, #0x0\n"
+        "movi v29.4s, #0x0\n"
+        ".inst 0x4f98e189  // sdot v9.4s, v12.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e19d  // sdot v29.4s, v12.16b, v24.4b[1]\n"
+        "fmla v14.4s, v20.4s, v2.4s\n"
+        "movi v20.4s, #0x0\n"
+        "movi v2.4s, #0x0\n"
+        ".inst 0x4f98e994  // sdot v20.4s, v12.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e982  // sdot v2.4s, v12.16b, v24.4b[3]\n"
+        "ldr q24, [x23, #0x20]\n"
+        ".inst 0x4f8ae3e9  // sdot v9.4s, v31.16b, v10.4b[0]\n"
+        ".inst 0x4faae3fd  // sdot v29.4s, v31.16b, v10.4b[1]\n"
+        ".inst 0x4f8aebf4  // sdot v20.4s, v31.16b, v10.4b[2]\n"
+        ".inst 0x4faaebe2  // sdot v2.4s, v31.16b, v10.4b[3]\n"
+        "ldr q10, [x23, #0x30]\n"
+        ".inst 0x4f98e0c9  // sdot v9.4s, v6.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e0dd  // sdot v29.4s, v6.16b, v24.4b[1]\n"
+        ".inst 0x4f98e8d4  // sdot v20.4s, v6.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e8c2  // sdot v2.4s, v6.16b, v24.4b[3]\n"
+        "ldr q24, [x23, #0x40]\n"
+        ".inst 0x4f8ae389  // sdot v9.4s, v28.16b, v10.4b[0]\n"
+        ".inst 0x4faae39d  // sdot v29.4s, v28.16b, v10.4b[1]\n"
+        ".inst 0x4f8aeb94  // sdot v20.4s, v28.16b, v10.4b[2]\n"
+        ".inst 0x4faaeb82  // sdot v2.4s, v28.16b, v10.4b[3]\n"
+        "ldr q10, [x23, #0x50]\n"
+        ".inst 0x4f98e069  // sdot v9.4s, v3.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e07d  // sdot v29.4s, v3.16b, v24.4b[1]\n"
+        ".inst 0x4f98e874  // sdot v20.4s, v3.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e862  // sdot v2.4s, v3.16b, v24.4b[3]\n"
+        "ldr q24, [x23, #0x60]\n"
+        ".inst 0x4f8ae2c9  // sdot v9.4s, v22.16b, v10.4b[0]\n"
+        ".inst 0x4faae2dd  // sdot v29.4s, v22.16b, v10.4b[1]\n"
+        ".inst 0x4f8aead4  // sdot v20.4s, v22.16b, v10.4b[2]\n"
+        ".inst 0x4faaeac2  // sdot v2.4s, v22.16b, v10.4b[3]\n"
+        "ldr q10, [x23, #0x70]\n"
+        "add x23, x23, #0x88\n"
+        ".inst 0x4f98e369  // sdot v9.4s, v27.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e37d  // sdot v29.4s, v27.16b, v24.4b[1]\n"
+        ".inst 0x4f98eb74  // sdot v20.4s, v27.16b, v24.4b[2]\n"
+        ".inst 0x4fb8eb62  // sdot v2.4s, v27.16b, v24.4b[3]\n"
+        "ldr q24, [x22, #0x0]\n"
+        ".inst 0x4f8ae3c9  // sdot v9.4s, v30.16b, v10.4b[0]\n"
+        ".inst 0x4faae3dd  // sdot v29.4s, v30.16b, v10.4b[1]\n"
+        ".inst 0x4f8aebd4  // sdot v20.4s, v30.16b, v10.4b[2]\n"
+        ".inst 0x4faaebc2  // sdot v2.4s, v30.16b, v10.4b[3]\n"
+        "fmul v10.4s, v17.4s, v26.s[0]\n"
+        "scvtf v9.4s, v9.4s, #0x4\n"
+        "scvtf v29.4s, v29.4s, #0x4\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "scvtf v2.4s, v2.4s, #0x4\n"
+        "fmla v11.4s, v9.4s, v10.4s\n"
+        "ldr q9, [x22, #0x10]\n"
+        "fmul v10.4s, v17.4s, v26.s[1]\n"
+        "fmla v13.4s, v29.4s, v10.4s\n"
+        "ldr d29, [x22, #-0x8]\n"
+        "fmul v10.4s, v17.4s, v26.s[2]\n"
+        "fmul v26.4s, v17.4s, v26.s[3]\n"
+        "fcvtl v29.4s, v29.4h\n"
+        "fmla v23.4s, v20.4s, v10.4s\n"
+        "movi v20.4s, #0x0\n"
+        "movi v10.4s, #0x0\n"
+        "fmla v16.4s, v2.4s, v26.4s\n"
+        "movi v26.4s, #0x0\n"
+        "movi v2.4s, #0x0\n"
+        ".inst 0x4f98e194  // sdot v20.4s, v12.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e18a  // sdot v10.4s, v12.16b, v24.4b[1]\n"
+        ".inst 0x4f98e99a  // sdot v26.4s, v12.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e982  // sdot v2.4s, v12.16b, v24.4b[3]\n"
+        "ldr q24, [x22, #0x20]\n"
+        ".inst 0x4f89e3f4  // sdot v20.4s, v31.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e3ea  // sdot v10.4s, v31.16b, v9.4b[1]\n"
+        ".inst 0x4f89ebfa  // sdot v26.4s, v31.16b, v9.4b[2]\n"
+        ".inst 0x4fa9ebe2  // sdot v2.4s, v31.16b, v9.4b[3]\n"
+        "ldr q9, [x22, #0x30]\n"
+        ".inst 0x4f98e0d4  // sdot v20.4s, v6.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e0ca  // sdot v10.4s, v6.16b, v24.4b[1]\n"
+        ".inst 0x4f98e8da  // sdot v26.4s, v6.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e8c2  // sdot v2.4s, v6.16b, v24.4b[3]\n"
+        "ldr q24, [x22, #0x40]\n"
+        ".inst 0x4f89e394  // sdot v20.4s, v28.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e38a  // sdot v10.4s, v28.16b, v9.4b[1]\n"
+        ".inst 0x4f89eb9a  // sdot v26.4s, v28.16b, v9.4b[2]\n"
+        ".inst 0x4fa9eb82  // sdot v2.4s, v28.16b, v9.4b[3]\n"
+        "ldr q9, [x22, #0x50]\n"
+        ".inst 0x4f98e074  // sdot v20.4s, v3.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e06a  // sdot v10.4s, v3.16b, v24.4b[1]\n"
+        ".inst 0x4f98e87a  // sdot v26.4s, v3.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e862  // sdot v2.4s, v3.16b, v24.4b[3]\n"
+        "ldr q24, [x22, #0x60]\n"
+        ".inst 0x4f89e2d4  // sdot v20.4s, v22.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e2ca  // sdot v10.4s, v22.16b, v9.4b[1]\n"
+        ".inst 0x4f89eada  // sdot v26.4s, v22.16b, v9.4b[2]\n"
+        ".inst 0x4fa9eac2  // sdot v2.4s, v22.16b, v9.4b[3]\n"
+        "ldr q9, [x22, #0x70]\n"
+        "add x22, x22, #0x88\n"
+        ".inst 0x4f98e374  // sdot v20.4s, v27.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e36a  // sdot v10.4s, v27.16b, v24.4b[1]\n"
+        ".inst 0x4f98eb7a  // sdot v26.4s, v27.16b, v24.4b[2]\n"
+        ".inst 0x4fb8eb62  // sdot v2.4s, v27.16b, v24.4b[3]\n"
+        "ldr q24, [x21, #0x0]\n"
+        ".inst 0x4f89e3d4  // sdot v20.4s, v30.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e3ca  // sdot v10.4s, v30.16b, v9.4b[1]\n"
+        ".inst 0x4f89ebda  // sdot v26.4s, v30.16b, v9.4b[2]\n"
+        ".inst 0x4fa9ebc2  // sdot v2.4s, v30.16b, v9.4b[3]\n"
+        "fmul v9.4s, v17.4s, v29.s[0]\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "scvtf v10.4s, v10.4s, #0x4\n"
+        "scvtf v26.4s, v26.4s, #0x4\n"
+        "scvtf v2.4s, v2.4s, #0x4\n"
+        "fmla v25.4s, v20.4s, v9.4s\n"
+        "ldr q9, [x21, #0x10]\n"
+        "fmul v20.4s, v17.4s, v29.s[1]\n"
+        "fmla v7.4s, v10.4s, v20.4s\n"
+        "ldr d20, [x21, #-0x8]\n"
+        "fmul v10.4s, v17.4s, v29.s[2]\n"
+        "fmul v29.4s, v17.4s, v29.s[3]\n"
+        "fcvtl v20.4s, v20.4h\n"
+        "fmla v0.4s, v26.4s, v10.4s\n"
+        "movi v26.4s, #0x0\n"
+        "movi v10.4s, #0x0\n"
+        "fmla v4.4s, v2.4s, v29.4s\n"
+        "movi v2.4s, #0x0\n"
+        "movi v29.4s, #0x0\n"
+        ".inst 0x4f98e19a  // sdot v26.4s, v12.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e18a  // sdot v10.4s, v12.16b, v24.4b[1]\n"
+        ".inst 0x4f98e982  // sdot v2.4s, v12.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e99d  // sdot v29.4s, v12.16b, v24.4b[3]\n"
+        "ldr q12, [x21, #0x20]\n"
+        "fmul v24.4s, v17.4s, v20.s[0]\n"
+        ".inst 0x4f89e3fa  // sdot v26.4s, v31.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e3ea  // sdot v10.4s, v31.16b, v9.4b[1]\n"
+        ".inst 0x4f89ebe2  // sdot v2.4s, v31.16b, v9.4b[2]\n"
+        ".inst 0x4fa9ebfd  // sdot v29.4s, v31.16b, v9.4b[3]\n"
+        "ldr q9, [x21, #0x30]\n"
+        "fmul v31.4s, v17.4s, v20.s[1]\n"
+        ".inst 0x4f8ce0da  // sdot v26.4s, v6.16b, v12.4b[0]\n"
+        ".inst 0x4face0ca  // sdot v10.4s, v6.16b, v12.4b[1]\n"
+        ".inst 0x4f8ce8c2  // sdot v2.4s, v6.16b, v12.4b[2]\n"
+        ".inst 0x4face8dd  // sdot v29.4s, v6.16b, v12.4b[3]\n"
+        "ldr q12, [x21, #0x40]\n"
+        "fmul v6.4s, v17.4s, v20.s[2]\n"
+        "fmul v20.4s, v17.4s, v20.s[3]\n"
+        ".inst 0x4f89e39a  // sdot v26.4s, v28.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e38a  // sdot v10.4s, v28.16b, v9.4b[1]\n"
+        ".inst 0x4f89eb82  // sdot v2.4s, v28.16b, v9.4b[2]\n"
+        ".inst 0x4fa9eb9d  // sdot v29.4s, v28.16b, v9.4b[3]\n"
+        "ldr q9, [x21, #0x50]\n"
+        ".inst 0x4f8ce07a  // sdot v26.4s, v3.16b, v12.4b[0]\n"
+        ".inst 0x4face06a  // sdot v10.4s, v3.16b, v12.4b[1]\n"
+        ".inst 0x4f8ce862  // sdot v2.4s, v3.16b, v12.4b[2]\n"
+        ".inst 0x4face87d  // sdot v29.4s, v3.16b, v12.4b[3]\n"
+        "ldr q12, [x21, #0x60]\n"
+        ".inst 0x4f89e2da  // sdot v26.4s, v22.16b, v9.4b[0]\n"
+        ".inst 0x4fa9e2ca  // sdot v10.4s, v22.16b, v9.4b[1]\n"
+        ".inst 0x4f89eac2  // sdot v2.4s, v22.16b, v9.4b[2]\n"
+        ".inst 0x4fa9eadd  // sdot v29.4s, v22.16b, v9.4b[3]\n"
+        "ldr q17, [x21, #0x70]\n"
+        "add x21, x21, #0x88\n"
+        ".inst 0x4f8ce37a  // sdot v26.4s, v27.16b, v12.4b[0]\n"
+        ".inst 0x4face36a  // sdot v10.4s, v27.16b, v12.4b[1]\n"
+        ".inst 0x4f8ceb62  // sdot v2.4s, v27.16b, v12.4b[2]\n"
+        ".inst 0x4faceb7d  // sdot v29.4s, v27.16b, v12.4b[3]\n"
+        ".inst 0x4f91e3da  // sdot v26.4s, v30.16b, v17.4b[0]\n"
+        ".inst 0x4fb1e3ca  // sdot v10.4s, v30.16b, v17.4b[1]\n"
+        ".inst 0x4f91ebc2  // sdot v2.4s, v30.16b, v17.4b[2]\n"
+        ".inst 0x4fb1ebdd  // sdot v29.4s, v30.16b, v17.4b[3]\n"
+        "scvtf v26.4s, v26.4s, #0x4\n"
+        "scvtf v10.4s, v10.4s, #0x4\n"
+        "fmla v5.4s, v26.4s, v24.4s\n"
+        "scvtf v2.4s, v2.4s, #0x4\n"
+        "scvtf v29.4s, v29.4s, #0x4\n"
+        "fmla v21.4s, v10.4s, v31.4s\n"
+        "fmla v8.4s, v2.4s, v6.4s\n"
+        "fmla v1.4s, v29.4s, v20.4s\n"
+        "bgt 3b\n"
+        "mov x20, %x[res_ptr]\n"
+        "subs x27, x27, #0x4\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "str q15, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q19, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q18, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q14, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q11, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q13, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q23, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q16, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q25, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q7, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q0, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q4, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q5, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q21, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q8, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q1, [x20, #0x0]\n"
+        "bne 2b\n"
+        "mov x20, #0x4\n"
+        "sub x10, x10, #0x10\n"
+        "cmp x10, #0x10\n"
+        "mov %x[res_ptr], x26\n"
+        "madd %x[a_ptr], x20, x9, %x[a_ptr]\n"
+        "bge 1b\n"
+        "4:"  // Row loop skip
+        "cbz x10, 9f\n"
+        "5:"  // Row tail: Row loop
+        "add x24, %x[b_ptr], #0x8\n"
+        "mov x23, %x[nc]\n"
+        "add x22, %x[res_ptr], %x[res_stride], LSL #2\n"
+        "6:"  // Row tail: Column loop
+        "movi v15.16b, #0x0\n"
+        "movi v19.16b, #0x0\n"
+        "add x25, %x[a_ptr], #0x8\n"
+        "mov x21, %x[nb]\n"
+        "movi v18.16b, #0x0\n"
+        "movi v14.16b, #0x0\n"
+        "7:"  // Row tail: Block loop
+        "ldr q7, [x24, #0x0]\n"
+        "ldr q5, [x25, #0x0]\n"
+        "movi v9.16b, #0x4\n"
+        "movi v4.4s, #0x0\n"
+        "ldr q3, [x24, #0x10]\n"
+        "ldr q2, [x25, #0x10]\n"
+        "movi v1.4s, #0x0\n"
+        "movi v0.4s, #0x0\n"
+        "ldr q13, [x24, #0x20]\n"
+        "ldr q31, [x25, #0x20]\n"
+        "movi v30.4s, #0x0\n"
+        "movi v29.16b, #0xf0\n"
+        "ldr q28, [x24, #0x30]\n"
+        "ldr q27, [x25, #0x30]\n"
+        "sshl v20.16b, v7.16b, v9.16b\n"
+        "sub x20, x24, #0x8\n"
+        "ldr q26, [x25, #0x40]\n"
+        "ldr q25, [x25, #0x50]\n"
+        "sshl v17.16b, v3.16b, v9.16b\n"
+        "and v7.16b, v7.16b, v29.16b\n"
+        "ldr q24, [x25, #0x60]\n"
+        "ldr q16, [x25, #0x70]\n"
+        "sshl v22.16b, v13.16b, v9.16b\n"
+        "and v3.16b, v3.16b, v29.16b\n"
+        "ldr d21, [x20, #0x0]\n"
+        "ldr d12, [x25, #-0x8]\n"
+        ".inst 0x4f85e284  // sdot v4.4s, v20.16b, v5.4b[0]\n"
+        ".inst 0x4fa5e281  // sdot v1.4s, v20.16b, v5.4b[1]\n"
+        ".inst 0x4f85ea80  // sdot v0.4s, v20.16b, v5.4b[2]\n"
+        ".inst 0x4fa5ea9e  // sdot v30.4s, v20.16b, v5.4b[3]\n"
+        "sshl v9.16b, v28.16b, v9.16b\n"
+        "subs x21, x21, #0x1\n"
+        "and v13.16b, v13.16b, v29.16b\n"
+        "and v28.16b, v28.16b, v29.16b\n"
+        "add x25, x25, #0x88\n"
+        "add x24, x24, #0x48\n"
+        "fcvtl v21.4s, v21.4h\n"
+        "fcvtl v12.4s, v12.4h\n"
+        ".inst 0x4f82e224  // sdot v4.4s, v17.16b, v2.4b[0]\n"
+        ".inst 0x4fa2e221  // sdot v1.4s, v17.16b, v2.4b[1]\n"
+        ".inst 0x4f82ea20  // sdot v0.4s, v17.16b, v2.4b[2]\n"
+        ".inst 0x4fa2ea3e  // sdot v30.4s, v17.16b, v2.4b[3]\n"
+        "fmul v11.4s, v21.4s, v12.s[0]\n"
+        "fmul v23.4s, v21.4s, v12.s[1]\n"
+        "fmul v17.4s, v21.4s, v12.s[2]\n"
+        ".inst 0x4f9fe2c4  // sdot v4.4s, v22.16b, v31.4b[0]\n"
+        "fmul v6.4s, v21.4s, v12.s[3]\n"
+        ".inst 0x4fbfe2c1  // sdot v1.4s, v22.16b, v31.4b[1]\n"
+        ".inst 0x4f9feac0  // sdot v0.4s, v22.16b, v31.4b[2]\n"
+        ".inst 0x4fbfeade  // sdot v30.4s, v22.16b, v31.4b[3]\n"
+        ".inst 0x4f9be124  // sdot v4.4s, v9.16b, v27.4b[0]\n"
+        ".inst 0x4fbbe121  // sdot v1.4s, v9.16b, v27.4b[1]\n"
+        ".inst 0x4f9be920  // sdot v0.4s, v9.16b, v27.4b[2]\n"
+        ".inst 0x4fbbe93e  // sdot v30.4s, v9.16b, v27.4b[3]\n"
+        ".inst 0x4f9ae0e4  // sdot v4.4s, v7.16b, v26.4b[0]\n"
+        ".inst 0x4fbae0e1  // sdot v1.4s, v7.16b, v26.4b[1]\n"
+        ".inst 0x4f9ae8e0  // sdot v0.4s, v7.16b, v26.4b[2]\n"
+        ".inst 0x4fbae8fe  // sdot v30.4s, v7.16b, v26.4b[3]\n"
+        ".inst 0x4f99e064  // sdot v4.4s, v3.16b, v25.4b[0]\n"
+        ".inst 0x4fb9e061  // sdot v1.4s, v3.16b, v25.4b[1]\n"
+        ".inst 0x4f99e860  // sdot v0.4s, v3.16b, v25.4b[2]\n"
+        ".inst 0x4fb9e87e  // sdot v30.4s, v3.16b, v25.4b[3]\n"
+        ".inst 0x4f98e1a4  // sdot v4.4s, v13.16b, v24.4b[0]\n"
+        ".inst 0x4fb8e1a1  // sdot v1.4s, v13.16b, v24.4b[1]\n"
+        ".inst 0x4f98e9a0  // sdot v0.4s, v13.16b, v24.4b[2]\n"
+        ".inst 0x4fb8e9be  // sdot v30.4s, v13.16b, v24.4b[3]\n"
+        ".inst 0x4f90e384  // sdot v4.4s, v28.16b, v16.4b[0]\n"
+        ".inst 0x4fb0e381  // sdot v1.4s, v28.16b, v16.4b[1]\n"
+        ".inst 0x4f90eb80  // sdot v0.4s, v28.16b, v16.4b[2]\n"
+        ".inst 0x4fb0eb9e  // sdot v30.4s, v28.16b, v16.4b[3]\n"
+        "scvtf v4.4s, v4.4s, #0x4\n"
+        "scvtf v1.4s, v1.4s, #0x4\n"
+        "scvtf v0.4s, v0.4s, #0x4\n"
+        "fmla v15.4s, v4.4s, v11.4s\n"
+        "scvtf v30.4s, v30.4s, #0x4\n"
+        "fmla v19.4s, v1.4s, v23.4s\n"
+        "fmla v18.4s, v0.4s, v17.4s\n"
+        "fmla v14.4s, v30.4s, v6.4s\n"
+        "bgt 7b\n"
+        "mov x20, %x[res_ptr]\n"
+        "cmp x10, #0x1\n"
+        "str q15, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "cmp x10, #0x2\n"
+        "str q19, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "cmp x10, #0x3\n"
+        "str q18, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "str q14, [x20, #0x0]\n"
+        "8:"  // Row tail: Accumulator store skip
+        "subs x23, x23, #0x4\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "bne 6b\n"
+        "subs x10, x10, #0x4\n"
+        "add %x[a_ptr], %x[a_ptr], x9\n"
+        "mov %x[res_ptr], x22\n"
+        "bgt 5b\n"
+        "9:"  // Row tail: Row loop skip
+        : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr)
+        : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc)
+        : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28"
+    );
+#else
+    float sumf[4][4];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
+                            }
+                            sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
+#endif
+}
+
+void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 4;
+    const int blocklen = 8;
+
+    assert (n % qk == 0);
+    assert (nr % 4 == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
+    if (svcntw() == 8) {
+        GGML_ASSERT(!(ggml_cpu_has_sve() && (svcntw() == 8)) &&
+                    "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+    const void * b_ptr = vx;
+    const void * a_ptr = vy;
+    float * res_ptr = s;
+    size_t res_stride = bs * sizeof(float);
+
+    __asm__ __volatile__(
+        "mov x10, %x[nr]\n"
+        "mov x9, #0x88\n"
+        "cmp x10, #0x10\n"
+        "mul x9, %x[nb], x9\n"
+        "blt 4f\n"
+        "1:"  // Row loop
+        "add x28, %x[b_ptr], #0x8\n"
+        "mov x27, %x[nc]\n"
+        "add x26, %x[res_ptr], %x[res_stride], LSL #4\n"
+        "2:"  // Column loop
+        "add x25, %x[a_ptr], #0x8\n"
+        "movi v2.16b, #0x0\n"
+        "movi v10.16b, #0x0\n"
+        "mov x24, %x[nb]\n"
+        "add x23, x25, x9\n"
+        "movi v12.16b, #0x0\n"
+        "movi v28.16b, #0x0\n"
+        "add x22, x23, x9\n"
+        "movi v11.16b, #0x0\n"
+        "movi v13.16b, #0x0\n"
+        "add x21, x22, x9\n"
+        "movi v22.16b, #0x0\n"
+        "movi v23.16b, #0x0\n"
+        "movi v25.16b, #0x0\n"
+        "movi v5.16b, #0x0\n"
+        "movi v7.16b, #0x0\n"
+        "movi v4.16b, #0x0\n"
+        "movi v6.16b, #0x0\n"
+        "movi v30.16b, #0x0\n"
+        "movi v24.16b, #0x0\n"
+        "movi v14.16b, #0x0\n"
+        "3:"  // Block loop
+        "ldr q21, [x28, #0x0]\n"
+        "ldr q16, [x28, #0x10]\n"
+        "movi v1.16b, #0x4\n"
+        "movi v19.4s, #0x0\n"
+        "ldr q27, [x25, #0x0]\n"
+        "ldr q15, [x25, #0x10]\n"
+        "movi v26.4s, #0x0\n"
+        "movi v18.4s, #0x0\n"
+        "ldr q29, [x28, #0x20]\n"
+        "ldr q3, [x28, #0x30]\n"
+        "movi v17.4s, #0x0\n"
+        "movi v0.16b, #0xf0\n"
+        "ldr d20, [x25, #-0x8]\n"
+        "ldr d9, [x23, #-0x8]\n"
+        "sshl v8.16b, v21.16b, v1.16b\n"
+        "sshl v31.16b, v16.16b, v1.16b\n"
+        "and v21.16b, v21.16b, v0.16b\n"
+        "and v16.16b, v16.16b, v0.16b\n"
+        "sub x20, x28, #0x8\n"
+        "subs x24, x24, #0x1\n"
+        "add x28, x28, #0x48\n"
+        ".inst 0x4e88a773  // smmla v19.4s, v27.16b, v8.16b\n"
+        ".inst 0x4e9fa77a  // smmla v26.4s, v27.16b, v31.16b\n"
+        "ldr q27, [x25, #0x20]\n"
+        ".inst 0x4e88a5f2  // smmla v18.4s, v15.16b, v8.16b\n"
+        ".inst 0x4e9fa5f1  // smmla v17.4s, v15.16b, v31.16b\n"
+        "sshl v15.16b, v29.16b, v1.16b\n"
+        "sshl v1.16b, v3.16b, v1.16b\n"
+        "and v29.16b, v29.16b, v0.16b\n"
+        "and v3.16b, v3.16b, v0.16b\n"
+        "ldr q0, [x25, #0x30]\n"
+        "fcvtl v20.4s, v20.4h\n"
+        ".inst 0x4e8fa773  // smmla v19.4s, v27.16b, v15.16b\n"
+        "fcvtl v9.4s, v9.4h\n"
+        ".inst 0x4e81a77a  // smmla v26.4s, v27.16b, v1.16b\n"
+        "ldr q27, [x25, #0x40]\n"
+        ".inst 0x4e8fa412  // smmla v18.4s, v0.16b, v15.16b\n"
+        ".inst 0x4e81a411  // smmla v17.4s, v0.16b, v1.16b\n"
+        "ldr q0, [x25, #0x50]\n"
+        ".inst 0x4e95a773  // smmla v19.4s, v27.16b, v21.16b\n"
+        ".inst 0x4e90a77a  // smmla v26.4s, v27.16b, v16.16b\n"
+        "ldr q27, [x25, #0x60]\n"
+        ".inst 0x4e95a412  // smmla v18.4s, v0.16b, v21.16b\n"
+        ".inst 0x4e90a411  // smmla v17.4s, v0.16b, v16.16b\n"
+        "ldr q0, [x25, #0x70]\n"
+        "add x25, x25, #0x88\n"
+        ".inst 0x4e9da773  // smmla v19.4s, v27.16b, v29.16b\n"
+        ".inst 0x4e83a77a  // smmla v26.4s, v27.16b, v3.16b\n"
+        "ldr d27, [x20, #0x0]\n"
+        ".inst 0x4e9da412  // smmla v18.4s, v0.16b, v29.16b\n"
+        ".inst 0x4e83a411  // smmla v17.4s, v0.16b, v3.16b\n"
+        "fcvtl v27.4s, v27.4h\n"
+        "uzp1 v0.2d, v19.2d, v26.2d\n"
+        "uzp2 v26.2d, v19.2d, v26.2d\n"
+        "fmul v19.4s, v27.4s, v20.s[0]\n"
+        "scvtf v0.4s, v0.4s, #0x4\n"
+        "scvtf v26.4s, v26.4s, #0x4\n"
+        "fmla v2.4s, v0.4s, v19.4s\n"
+        "ldr q19, [x23, #0x0]\n"
+        "uzp1 v0.2d, v18.2d, v17.2d\n"
+        "uzp2 v18.2d, v18.2d, v17.2d\n"
+        "fmul v17.4s, v27.4s, v20.s[1]\n"
+        "scvtf v0.4s, v0.4s, #0x4\n"
+        "scvtf v18.4s, v18.4s, #0x4\n"
+        "fmla v10.4s, v26.4s, v17.4s\n"
+        "ldr q17, [x23, #0x10]\n"
+        "fmul v26.4s, v27.4s, v20.s[2]\n"
+        "fmul v20.4s, v27.4s, v20.s[3]\n"
+        "fmla v12.4s, v0.4s, v26.4s\n"
+        "ldr d0, [x22, #-0x8]\n"
+        "ldr d26, [x21, #-0x8]\n"
+        "fcvtl v0.4s, v0.4h\n"
+        "fmla v28.4s, v18.4s, v20.4s\n"
+        "movi v20.4s, #0x0\n"
+        "movi v18.4s, #0x0\n"
+        ".inst 0x4e88a674  // smmla v20.4s, v19.16b, v8.16b\n"
+        ".inst 0x4e9fa672  // smmla v18.4s, v19.16b, v31.16b\n"
+        "ldr q19, [x23, #0x20]\n"
+        "fcvtl v26.4s, v26.4h\n"
+        ".inst 0x4e8fa674  // smmla v20.4s, v19.16b, v15.16b\n"
+        ".inst 0x4e81a672  // smmla v18.4s, v19.16b, v1.16b\n"
+        "ldr q19, [x23, #0x40]\n"
+        ".inst 0x4e95a674  // smmla v20.4s, v19.16b, v21.16b\n"
+        ".inst 0x4e90a672  // smmla v18.4s, v19.16b, v16.16b\n"
+        "ldr q19, [x23, #0x60]\n"
+        ".inst 0x4e9da674  // smmla v20.4s, v19.16b, v29.16b\n"
+        ".inst 0x4e83a672  // smmla v18.4s, v19.16b, v3.16b\n"
+        "uzp1 v19.2d, v20.2d, v18.2d\n"
+        "scvtf v19.4s, v19.4s, #0x4\n"
+        "uzp2 v20.2d, v20.2d, v18.2d\n"
+        "fmul v18.4s, v27.4s, v9.s[0]\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "fmla v11.4s, v19.4s, v18.4s\n"
+        "ldr q18, [x22, #0x0]\n"
+        "fmul v19.4s, v27.4s, v9.s[1]\n"
+        "fmla v13.4s, v20.4s, v19.4s\n"
+        "movi v19.4s, #0x0\n"
+        "movi v20.4s, #0x0\n"
+        ".inst 0x4e88a633  // smmla v19.4s, v17.16b, v8.16b\n"
+        ".inst 0x4e9fa634  // smmla v20.4s, v17.16b, v31.16b\n"
+        "ldr q17, [x23, #0x30]\n"
+        ".inst 0x4e8fa633  // smmla v19.4s, v17.16b, v15.16b\n"
+        ".inst 0x4e81a634  // smmla v20.4s, v17.16b, v1.16b\n"
+        "ldr q17, [x23, #0x50]\n"
+        ".inst 0x4e95a633  // smmla v19.4s, v17.16b, v21.16b\n"
+        ".inst 0x4e90a634  // smmla v20.4s, v17.16b, v16.16b\n"
+        "ldr q17, [x23, #0x70]\n"
+        "add x23, x23, #0x88\n"
+        ".inst 0x4e9da633  // smmla v19.4s, v17.16b, v29.16b\n"
+        ".inst 0x4e83a634  // smmla v20.4s, v17.16b, v3.16b\n"
+        "uzp1 v17.2d, v19.2d, v20.2d\n"
+        "scvtf v17.4s, v17.4s, #0x4\n"
+        "uzp2 v20.2d, v19.2d, v20.2d\n"
+        "fmul v19.4s, v27.4s, v9.s[2]\n"
+        "fmul v9.4s, v27.4s, v9.s[3]\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "fmla v22.4s, v17.4s, v19.4s\n"
+        "ldr q17, [x22, #0x10]\n"
+        "movi v19.4s, #0x0\n"
+        ".inst 0x4e88a653  // smmla v19.4s, v18.16b, v8.16b\n"
+        "fmla v23.4s, v20.4s, v9.4s\n"
+        "movi v20.4s, #0x0\n"
+        "movi v9.4s, #0x0\n"
+        ".inst 0x4e9fa654  // smmla v20.4s, v18.16b, v31.16b\n"
+        "ldr q18, [x22, #0x20]\n"
+        ".inst 0x4e88a629  // smmla v9.4s, v17.16b, v8.16b\n"
+        ".inst 0x4e8fa653  // smmla v19.4s, v18.16b, v15.16b\n"
+        ".inst 0x4e81a654  // smmla v20.4s, v18.16b, v1.16b\n"
+        "ldr q18, [x22, #0x40]\n"
+        ".inst 0x4e95a653  // smmla v19.4s, v18.16b, v21.16b\n"
+        ".inst 0x4e90a654  // smmla v20.4s, v18.16b, v16.16b\n"
+        "ldr q18, [x22, #0x60]\n"
+        ".inst 0x4e9da653  // smmla v19.4s, v18.16b, v29.16b\n"
+        ".inst 0x4e83a654  // smmla v20.4s, v18.16b, v3.16b\n"
+        "movi v18.4s, #0x0\n"
+        ".inst 0x4e9fa632  // smmla v18.4s, v17.16b, v31.16b\n"
+        "ldr q17, [x22, #0x30]\n"
+        ".inst 0x4e8fa629  // smmla v9.4s, v17.16b, v15.16b\n"
+        ".inst 0x4e81a632  // smmla v18.4s, v17.16b, v1.16b\n"
+        "ldr q17, [x22, #0x50]\n"
+        ".inst 0x4e95a629  // smmla v9.4s, v17.16b, v21.16b\n"
+        ".inst 0x4e90a632  // smmla v18.4s, v17.16b, v16.16b\n"
+        "ldr q17, [x22, #0x70]\n"
+        "add x22, x22, #0x88\n"
+        ".inst 0x4e9da629  // smmla v9.4s, v17.16b, v29.16b\n"
+        ".inst 0x4e83a632  // smmla v18.4s, v17.16b, v3.16b\n"
+        "uzp1 v17.2d, v19.2d, v20.2d\n"
+        "uzp2 v20.2d, v19.2d, v20.2d\n"
+        "fmul v19.4s, v27.4s, v0.s[0]\n"
+        "scvtf v17.4s, v17.4s, #0x4\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "fmla v25.4s, v17.4s, v19.4s\n"
+        "ldr q19, [x21, #0x0]\n"
+        "fmul v17.4s, v27.4s, v0.s[1]\n"
+        "fmla v5.4s, v20.4s, v17.4s\n"
+        "ldr q17, [x21, #0x10]\n"
+        "uzp1 v20.2d, v9.2d, v18.2d\n"
+        "uzp2 v9.2d, v9.2d, v18.2d\n"
+        "fmul v18.4s, v27.4s, v0.s[2]\n"
+        "fmul v0.4s, v27.4s, v0.s[3]\n"
+        "scvtf v20.4s, v20.4s, #0x4\n"
+        "scvtf v9.4s, v9.4s, #0x4\n"
+        "fmla v7.4s, v20.4s, v18.4s\n"
+        "movi v20.4s, #0x0\n"
+        "movi v18.4s, #0x0\n"
+        ".inst 0x4e88a674  // smmla v20.4s, v19.16b, v8.16b\n"
+        ".inst 0x4e9fa672  // smmla v18.4s, v19.16b, v31.16b\n"
+        "ldr q19, [x21, #0x20]\n"
+        "fmla v4.4s, v9.4s, v0.4s\n"
+        "movi v9.4s, #0x0\n"
+        "movi v0.4s, #0x0\n"
+        ".inst 0x4e88a629  // smmla v9.4s, v17.16b, v8.16b\n"
+        "fmul v8.4s, v27.4s, v26.s[0]\n"
+        ".inst 0x4e9fa620  // smmla v0.4s, v17.16b, v31.16b\n"
+        "ldr q17, [x21, #0x30]\n"
+        ".inst 0x4e8fa674  // smmla v20.4s, v19.16b, v15.16b\n"
+        "fmul v31.4s, v27.4s, v26.s[1]\n"
+        ".inst 0x4e81a672  // smmla v18.4s, v19.16b, v1.16b\n"
+        "ldr q19, [x21, #0x40]\n"
+        ".inst 0x4e8fa629  // smmla v9.4s, v17.16b, v15.16b\n"
+        "fmul v15.4s, v27.4s, v26.s[2]\n"
+        "fmul v27.4s, v27.4s, v26.s[3]\n"
+        ".inst 0x4e81a620  // smmla v0.4s, v17.16b, v1.16b\n"
+        "ldr q1, [x21, #0x50]\n"
+        ".inst 0x4e95a674  // smmla v20.4s, v19.16b, v21.16b\n"
+        ".inst 0x4e90a672  // smmla v18.4s, v19.16b, v16.16b\n"
+        "ldr q26, [x21, #0x60]\n"
+        ".inst 0x4e95a429  // smmla v9.4s, v1.16b, v21.16b\n"
+        ".inst 0x4e90a420  // smmla v0.4s, v1.16b, v16.16b\n"
+        "ldr q21, [x21, #0x70]\n"
+        "add x21, x21, #0x88\n"
+        ".inst 0x4e9da754  // smmla v20.4s, v26.16b, v29.16b\n"
+        ".inst 0x4e83a752  // smmla v18.4s, v26.16b, v3.16b\n"
+        ".inst 0x4e9da6a9  // smmla v9.4s, v21.16b, v29.16b\n"
+        ".inst 0x4e83a6a0  // smmla v0.4s, v21.16b, v3.16b\n"
+        "uzp1 v29.2d, v20.2d, v18.2d\n"
+        "uzp2 v21.2d, v20.2d, v18.2d\n"
+        "scvtf v29.4s, v29.4s, #0x4\n"
+        "uzp1 v18.2d, v9.2d, v0.2d\n"
+        "uzp2 v16.2d, v9.2d, v0.2d\n"
+        "scvtf v21.4s, v21.4s, #0x4\n"
+        "fmla v6.4s, v29.4s, v8.4s\n"
+        "scvtf v18.4s, v18.4s, #0x4\n"
+        "scvtf v16.4s, v16.4s, #0x4\n"
+        "fmla v30.4s, v21.4s, v31.4s\n"
+        "fmla v24.4s, v18.4s, v15.4s\n"
+        "fmla v14.4s, v16.4s, v27.4s\n"
+        "bgt 3b\n"
+        "mov x20, %x[res_ptr]\n"
+        "subs x27, x27, #0x4\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "str q2, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q10, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q12, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q28, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q11, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q13, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q22, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q23, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q25, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q5, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q7, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q4, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q6, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q30, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q24, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "str q14, [x20, #0x0]\n"
+        "bne 2b\n"
+        "mov x20, #0x4\n"
+        "sub x10, x10, #0x10\n"
+        "cmp x10, #0x10\n"
+        "mov %x[res_ptr], x26\n"
+        "madd %x[a_ptr], x20, x9, %x[a_ptr]\n"
+        "bge 1b\n"
+        "4:"  // Row loop skip
+        "cbz x10, 9f\n"
+        "5:"  // Row tail: Row loop
+        "add x24, %x[b_ptr], #0x8\n"
+        "mov x23, %x[nc]\n"
+        "add x22, %x[res_ptr], %x[res_stride], LSL #2\n"
+        "6:"  // Row tail: Column loop
+        "movi v2.16b, #0x0\n"
+        "movi v10.16b, #0x0\n"
+        "add x25, %x[a_ptr], #0x8\n"
+        "mov x21, %x[nb]\n"
+        "movi v12.16b, #0x0\n"
+        "movi v28.16b, #0x0\n"
+        "7:"  // Row tail: Block loop
+        "ldr q6, [x24, #0x0]\n"
+        "ldr q5, [x24, #0x10]\n"
+        "movi v17.16b, #0x4\n"
+        "movi v8.4s, #0x0\n"
+        "ldr q4, [x25, #0x0]\n"
+        "ldr q13, [x25, #0x10]\n"
+        "movi v27.4s, #0x0\n"
+        "movi v0.4s, #0x0\n"
+        "ldr q31, [x24, #0x20]\n"
+        "ldr q14, [x24, #0x30]\n"
+        "movi v29.4s, #0x0\n"
+        "movi v22.16b, #0xf0\n"
+        "ldr q11, [x25, #0x20]\n"
+        "ldr q23, [x25, #0x30]\n"
+        "sshl v21.16b, v6.16b, v17.16b\n"
+        "sshl v16.16b, v5.16b, v17.16b\n"
+        "ldr q20, [x25, #0x40]\n"
+        "ldr q26, [x25, #0x50]\n"
+        "and v6.16b, v6.16b, v22.16b\n"
+        "and v5.16b, v5.16b, v22.16b\n"
+        "ldr q25, [x25, #0x60]\n"
+        "ldr q3, [x25, #0x70]\n"
+        "sshl v19.16b, v31.16b, v17.16b\n"
+        "sshl v18.16b, v14.16b, v17.16b\n"
+        "ldr d17, [x25, #-0x8]\n"
+        ".inst 0x4e95a488  // smmla v8.4s, v4.16b, v21.16b\n"
+        ".inst 0x4e90a49b  // smmla v27.4s, v4.16b, v16.16b\n"
+        "and v31.16b, v31.16b, v22.16b\n"
+        ".inst 0x4e95a5a0  // smmla v0.4s, v13.16b, v21.16b\n"
+        ".inst 0x4e90a5bd  // smmla v29.4s, v13.16b, v16.16b\n"
+        "and v14.16b, v14.16b, v22.16b\n"
+        "sub x20, x24, #0x8\n"
+        "ldr d16, [x20, #0x0]\n"
+        "subs x21, x21, #0x1\n"
+        "add x25, x25, #0x88\n"
+        "fcvtl v17.4s, v17.4h\n"
+        "add x24, x24, #0x48\n"
+        ".inst 0x4e93a568  // smmla v8.4s, v11.16b, v19.16b\n"
+        ".inst 0x4e92a57b  // smmla v27.4s, v11.16b, v18.16b\n"
+        ".inst 0x4e93a6e0  // smmla v0.4s, v23.16b, v19.16b\n"
+        ".inst 0x4e92a6fd  // smmla v29.4s, v23.16b, v18.16b\n"
+        "fcvtl v16.4s, v16.4h\n"
+        ".inst 0x4e86a688  // smmla v8.4s, v20.16b, v6.16b\n"
+        ".inst 0x4e85a69b  // smmla v27.4s, v20.16b, v5.16b\n"
+        "fmul v23.4s, v16.4s, v17.s[0]\n"
+        "fmul v21.4s, v16.4s, v17.s[1]\n"
+        "fmul v1.4s, v16.4s, v17.s[2]\n"
+        "fmul v20.4s, v16.4s, v17.s[3]\n"
+        ".inst 0x4e86a740  // smmla v0.4s, v26.16b, v6.16b\n"
+        ".inst 0x4e85a75d  // smmla v29.4s, v26.16b, v5.16b\n"
+        ".inst 0x4e9fa728  // smmla v8.4s, v25.16b, v31.16b\n"
+        ".inst 0x4e8ea73b  // smmla v27.4s, v25.16b, v14.16b\n"
+        ".inst 0x4e9fa460  // smmla v0.4s, v3.16b, v31.16b\n"
+        ".inst 0x4e8ea47d  // smmla v29.4s, v3.16b, v14.16b\n"
+        "uzp1 v19.2d, v8.2d, v27.2d\n"
+        "uzp2 v18.2d, v8.2d, v27.2d\n"
+        "scvtf v19.4s, v19.4s, #0x4\n"
+        "uzp1 v17.2d, v0.2d, v29.2d\n"
+        "uzp2 v16.2d, v0.2d, v29.2d\n"
+        "scvtf v18.4s, v18.4s, #0x4\n"
+        "fmla v2.4s, v19.4s, v23.4s\n"
+        "scvtf v17.4s, v17.4s, #0x4\n"
+        "scvtf v16.4s, v16.4s, #0x4\n"
+        "fmla v10.4s, v18.4s, v21.4s\n"
+        "fmla v12.4s, v17.4s, v1.4s\n"
+        "fmla v28.4s, v16.4s, v20.4s\n"
+        "bgt 7b\n"
+        "mov x20, %x[res_ptr]\n"
+        "cmp x10, #0x1\n"
+        "str q2, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "cmp x10, #0x2\n"
+        "str q10, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "cmp x10, #0x3\n"
+        "str q12, [x20, #0x0]\n"
+        "add x20, x20, %x[res_stride]\n"
+        "ble 8f\n"
+        "str q28, [x20, #0x0]\n"
+        "8:"  // Row tail: Accumulator store skip
+        "subs x23, x23, #0x4\n"
+        "add %x[res_ptr], %x[res_ptr], #0x10\n"
+        "bne 6b\n"
+        "subs x10, x10, #0x4\n"
+        "add %x[a_ptr], %x[a_ptr], x9\n"
+        "mov %x[res_ptr], x22\n"
+        "bgt 5b\n"
+        "9:"  // Row tail: Row loop skip
+        : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr)
+        : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc)
+        : "cc", "memory", "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7", "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15", "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23", "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31", "x9", "x10", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28"
+    );
+#elif defined(__ARM_NEON) && defined(__aarch64__)
+    GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) &&
+                "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal "
+                "performance");
+#else
+    float sumf[4][4];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
+                            }
+                            sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
+#endif
+}
+
+void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) {
+    const int qk = QK8_0;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 8;
+
+    assert (n % qk == 0);
+    assert (nr % 4 == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(s);
+    UNUSED(bs);
+    UNUSED(vx);
+    UNUSED(vy);
+    UNUSED(nr);
+    UNUSED(nc);
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
+    if (svcntw() == 8) {
+        const void * b_ptr = vx;
+        const void * a_ptr = vy;
+        float * res_ptr = s;
+        size_t res_stride = bs * sizeof(float);
+
+        __asm__ __volatile__(
+            "mov x20, #0x4\n"
+            "mov x13, %x[nr]\n"
+            "mov z28.s, #-0x4\n"
+            "mov x12, #0x88\n"
+            "ptrue p1.b\n"
+            "whilelt p0.s, XZR, x20\n"
+            "cmp x13, #0x10\n"
+            "mul x12, %x[nb], x12\n"
+            "blt 4f\n"
+            "1:"  // Row loop
+            "add x11, %x[b_ptr], #0x10\n"
+            "mov x10, %x[nc]\n"
+            "add x9, %x[res_ptr], %x[res_stride], LSL #4\n"
+            "2:"  // Column loop
+            "add x28, %x[a_ptr], #0x8\n"
+            "mov z24.b, #0x0\n"
+            "mov z15.b, #0x0\n"
+            "mov x27, %x[nb]\n"
+            "add x26, x28, x12\n"
+            "mov z12.b, #0x0\n"
+            "mov z0.b, #0x0\n"
+            "add x25, x26, x12\n"
+            "mov z13.b, #0x0\n"
+            "mov z1.b, #0x0\n"
+            "add x24, x25, x12\n"
+            "mov z20.b, #0x0\n"
+            "mov z25.b, #0x0\n"
+            "mov z11.b, #0x0\n"
+            "mov z16.b, #0x0\n"
+            "mov z19.b, #0x0\n"
+            "mov z26.b, #0x0\n"
+            "mov z8.b, #0x0\n"
+            "mov z29.b, #0x0\n"
+            "mov z27.b, #0x0\n"
+            "mov z10.b, #0x0\n"
+            "3:"  // Block loop
+            "ld1b { z30.b }, p1/Z, [x11]\n"
+            "ld1b { z21.b }, p1/Z, [x11, #1, MUL VL]\n"
+            "mov z18.s, #0x0\n"
+            "mov z7.s, #0x0\n"
+            "ld1rqb { z3.b }, p1/Z, [x28]\n"
+            "ld1rqb { z5.b }, p1/Z, [x28, #16]\n"
+            "mov z9.s, #0x0\n"
+            "mov z22.s, #0x0\n"
+            "ld1b { z4.b }, p1/Z, [x11, #2, MUL VL]\n"
+            "ld1b { z17.b }, p1/Z, [x11, #3, MUL VL]\n"
+            "sub x20, x11, #0x10\n"
+            "sub x23, x28, #0x8\n"
+            "lsl z31.b, z30.b, #0x4\n"
+            "lsl z6.b, z21.b, #0x4\n"
+            "ld1h { z23.s }, p1/Z, [x20]\n"
+            "sub x22, x26, #0x8\n"
+            "and z30.b, z30.b, #0xf0\n"
+            "and z21.b, z21.b, #0xf0\n"
+            "sub x21, x25, #0x8\n"
+            "sub x20, x24, #0x8\n"
+            "lsl z14.b, z4.b, #0x4\n"
+            "lsl z2.b, z17.b, #0x4\n"
+            "subs x27, x27, #0x1\n"
+            "add x11, x11, #0x90\n"
+            ".inst 0x451f9872  // smmla z18.s, z3.b, z31.b\n"
+            ".inst 0x45069867  // smmla z7.s, z3.b, z6.b\n"
+            "ld1rqb { z3.b }, p1/Z, [x28, #32]\n"
+            "and z4.b, z4.b, #0xf0\n"
+            ".inst 0x451f98a9  // smmla z9.s, z5.b, z31.b\n"
+            ".inst 0x450698b6  // smmla z22.s, z5.b, z6.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x28, #48]\n"
+            "and z17.b, z17.b, #0xf0\n"
+            "fcvt z23.s, p1/m, z23.h\n"
+            ".inst 0x450e9872  // smmla z18.s, z3.b, z14.b\n"
+            ".inst 0x45029867  // smmla z7.s, z3.b, z2.b\n"
+            "ld1rqb { z3.b }, p1/Z, [x28, #64]\n"
+            ".inst 0x450e98a9  // smmla z9.s, z5.b, z14.b\n"
+            ".inst 0x450298b6  // smmla z22.s, z5.b, z2.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x28, #80]\n"
+            "fscale z23.s, p1/m, z23.s, z28.s\n"
+            ".inst 0x451e9872  // smmla z18.s, z3.b, z30.b\n"
+            ".inst 0x45159867  // smmla z7.s, z3.b, z21.b\n"
+            "ld1rqb { z3.b }, p1/Z, [x28, #96]\n"
+            ".inst 0x451e98a9  // smmla z9.s, z5.b, z30.b\n"
+            ".inst 0x451598b6  // smmla z22.s, z5.b, z21.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x28, #112]\n"
+            "add x28, x28, #0x88\n"
+            ".inst 0x45049872  // smmla z18.s, z3.b, z4.b\n"
+            ".inst 0x45119867  // smmla z7.s, z3.b, z17.b\n"
+            "ld1h { z3.s }, p0/Z, [x23]\n"
+            ".inst 0x450498a9  // smmla z9.s, z5.b, z4.b\n"
+            ".inst 0x451198b6  // smmla z22.s, z5.b, z17.b\n"
+            "fcvt z3.s, p1/m, z3.h\n"
+            "uzp1 z5.d, z18.d, z7.d\n"
+            "uzp2 z18.d, z18.d, z7.d\n"
+            "mov z3.q, z3.q[0]\n"
+            "uzp1 z7.d, z9.d, z22.d\n"
+            "uzp2 z22.d, z9.d, z22.d\n"
+            "fmul z9.s, z23.s, z3.s[0]\n"
+            "scvtf z5.s, p1/m, z5.s\n"
+            "scvtf z18.s, p1/m, z18.s\n"
+            "scvtf z7.s, p1/m, z7.s\n"
+            "scvtf z22.s, p1/m, z22.s\n"
+            "fmla z24.s, p1/M, z5.s, z9.s\n"
+            "ld1rqb { z5.b }, p1/Z, [x26]\n"
+            "fmul z9.s, z23.s, z3.s[1]\n"
+            "fmla z15.s, p1/M, z18.s, z9.s\n"
+            "ld1rqb { z18.b }, p1/Z, [x26, #16]\n"
+            "fmul z9.s, z23.s, z3.s[2]\n"
+            "fmul z3.s, z23.s, z3.s[3]\n"
+            "fmla z12.s, p1/M, z7.s, z9.s\n"
+            "mov z9.s, #0x0\n"
+            "ld1h { z7.s }, p0/Z, [x22]\n"
+            ".inst 0x451f98a9  // smmla z9.s, z5.b, z31.b\n"
+            "fmla z0.s, p1/M, z22.s, z3.s\n"
+            "mov z22.s, #0x0\n"
+            "ld1h { z3.s }, p0/Z, [x21]\n"
+            ".inst 0x450698b6  // smmla z22.s, z5.b, z6.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x26, #32]\n"
+            "fcvt z7.s, p1/m, z7.h\n"
+            "fcvt z3.s, p1/m, z3.h\n"
+            ".inst 0x450e98a9  // smmla z9.s, z5.b, z14.b\n"
+            ".inst 0x450298b6  // smmla z22.s, z5.b, z2.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x26, #64]\n"
+            "mov z7.q, z7.q[0]\n"
+            "mov z3.q, z3.q[0]\n"
+            ".inst 0x451e98a9  // smmla z9.s, z5.b, z30.b\n"
+            ".inst 0x451598b6  // smmla z22.s, z5.b, z21.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x26, #96]\n"
+            ".inst 0x450498a9  // smmla z9.s, z5.b, z4.b\n"
+            ".inst 0x451198b6  // smmla z22.s, z5.b, z17.b\n"
+            "uzp1 z5.d, z9.d, z22.d\n"
+            "scvtf z5.s, p1/m, z5.s\n"
+            "uzp2 z22.d, z9.d, z22.d\n"
+            "fmul z9.s, z23.s, z7.s[0]\n"
+            "scvtf z22.s, p1/m, z22.s\n"
+            "fmla z13.s, p1/M, z5.s, z9.s\n"
+            "ld1rqb { z9.b }, p1/Z, [x25]\n"
+            "fmul z5.s, z23.s, z7.s[1]\n"
+            "fmla z1.s, p1/M, z22.s, z5.s\n"
+            "mov z5.s, #0x0\n"
+            "mov z22.s, #0x0\n"
+            ".inst 0x451f9a45  // smmla z5.s, z18.b, z31.b\n"
+            ".inst 0x45069a56  // smmla z22.s, z18.b, z6.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x26, #48]\n"
+            ".inst 0x450e9a45  // smmla z5.s, z18.b, z14.b\n"
+            ".inst 0x45029a56  // smmla z22.s, z18.b, z2.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x26, #80]\n"
+            ".inst 0x451e9a45  // smmla z5.s, z18.b, z30.b\n"
+            ".inst 0x45159a56  // smmla z22.s, z18.b, z21.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x26, #112]\n"
+            "add x26, x26, #0x88\n"
+            ".inst 0x45049a45  // smmla z5.s, z18.b, z4.b\n"
+            ".inst 0x45119a56  // smmla z22.s, z18.b, z17.b\n"
+            "uzp1 z18.d, z5.d, z22.d\n"
+            "scvtf z18.s, p1/m, z18.s\n"
+            "uzp2 z22.d, z5.d, z22.d\n"
+            "fmul z5.s, z23.s, z7.s[2]\n"
+            "fmul z7.s, z23.s, z7.s[3]\n"
+            "scvtf z22.s, p1/m, z22.s\n"
+            "fmla z20.s, p1/M, z18.s, z5.s\n"
+            "ld1rqb { z18.b }, p1/Z, [x25, #16]\n"
+            "ld1h { z5.s }, p0/Z, [x20]\n"
+            "fcvt z5.s, p1/m, z5.h\n"
+            "fmla z25.s, p1/M, z22.s, z7.s\n"
+            "mov z22.s, #0x0\n"
+            "mov z7.s, #0x0\n"
+            ".inst 0x451f9936  // smmla z22.s, z9.b, z31.b\n"
+            ".inst 0x45069927  // smmla z7.s, z9.b, z6.b\n"
+            "ld1rqb { z9.b }, p1/Z, [x25, #32]\n"
+            "mov z5.q, z5.q[0]\n"
+            ".inst 0x450e9936  // smmla z22.s, z9.b, z14.b\n"
+            ".inst 0x45029927  // smmla z7.s, z9.b, z2.b\n"
+            "ld1rqb { z9.b }, p1/Z, [x25, #64]\n"
+            ".inst 0x451e9936  // smmla z22.s, z9.b, z30.b\n"
+            ".inst 0x45159927  // smmla z7.s, z9.b, z21.b\n"
+            "ld1rqb { z9.b }, p1/Z, [x25, #96]\n"
+            ".inst 0x45049936  // smmla z22.s, z9.b, z4.b\n"
+            ".inst 0x45119927  // smmla z7.s, z9.b, z17.b\n"
+            "uzp1 z9.d, z22.d, z7.d\n"
+            "scvtf z9.s, p1/m, z9.s\n"
+            "uzp2 z22.d, z22.d, z7.d\n"
+            "fmul z7.s, z23.s, z3.s[0]\n"
+            "scvtf z22.s, p1/m, z22.s\n"
+            "fmla z11.s, p1/M, z9.s, z7.s\n"
+            "ld1rqb { z9.b }, p1/Z, [x24]\n"
+            "fmul z7.s, z23.s, z3.s[1]\n"
+            "fmla z16.s, p1/M, z22.s, z7.s\n"
+            "mov z22.s, #0x0\n"
+            "mov z7.s, #0x0\n"
+            ".inst 0x451f9a56  // smmla z22.s, z18.b, z31.b\n"
+            ".inst 0x45069a47  // smmla z7.s, z18.b, z6.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x25, #48]\n"
+            ".inst 0x450e9a56  // smmla z22.s, z18.b, z14.b\n"
+            ".inst 0x45029a47  // smmla z7.s, z18.b, z2.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x25, #80]\n"
+            ".inst 0x451e9a56  // smmla z22.s, z18.b, z30.b\n"
+            ".inst 0x45159a47  // smmla z7.s, z18.b, z21.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x25, #112]\n"
+            "add x25, x25, #0x88\n"
+            ".inst 0x45049a56  // smmla z22.s, z18.b, z4.b\n"
+            ".inst 0x45119a47  // smmla z7.s, z18.b, z17.b\n"
+            "uzp1 z18.d, z22.d, z7.d\n"
+            "scvtf z18.s, p1/m, z18.s\n"
+            "uzp2 z7.d, z22.d, z7.d\n"
+            "fmul z22.s, z23.s, z3.s[2]\n"
+            "fmul z3.s, z23.s, z3.s[3]\n"
+            "scvtf z7.s, p1/m, z7.s\n"
+            "fmla z19.s, p1/M, z18.s, z22.s\n"
+            "ld1rqb { z18.b }, p1/Z, [x24, #16]\n"
+            "fmul z22.s, z23.s, z5.s[0]\n"
+            "fmla z26.s, p1/M, z7.s, z3.s\n"
+            "mov z3.s, #0x0\n"
+            "mov z7.s, #0x0\n"
+            ".inst 0x451f9923  // smmla z3.s, z9.b, z31.b\n"
+            ".inst 0x45069927  // smmla z7.s, z9.b, z6.b\n"
+            "ld1rqb { z9.b }, p1/Z, [x24, #32]\n"
+            ".inst 0x450e9923  // smmla z3.s, z9.b, z14.b\n"
+            ".inst 0x45029927  // smmla z7.s, z9.b, z2.b\n"
+            "mov z9.s, #0x0\n"
+            ".inst 0x451f9a49  // smmla z9.s, z18.b, z31.b\n"
+            "mov z31.s, #0x0\n"
+            ".inst 0x45069a5f  // smmla z31.s, z18.b, z6.b\n"
+            "ld1rqb { z6.b }, p1/Z, [x24, #48]\n"
+            "ld1rqb { z18.b }, p1/Z, [x24, #64]\n"
+            ".inst 0x450e98c9  // smmla z9.s, z6.b, z14.b\n"
+            "fmul z14.s, z23.s, z5.s[1]\n"
+            ".inst 0x450298df  // smmla z31.s, z6.b, z2.b\n"
+            "ld1rqb { z6.b }, p1/Z, [x24, #80]\n"
+            "fmul z2.s, z23.s, z5.s[2]\n"
+            "fmul z23.s, z23.s, z5.s[3]\n"
+            ".inst 0x451e9a43  // smmla z3.s, z18.b, z30.b\n"
+            ".inst 0x45159a47  // smmla z7.s, z18.b, z21.b\n"
+            "ld1rqb { z5.b }, p1/Z, [x24, #96]\n"
+            ".inst 0x451e98c9  // smmla z9.s, z6.b, z30.b\n"
+            ".inst 0x451598df  // smmla z31.s, z6.b, z21.b\n"
+            "ld1rqb { z18.b }, p1/Z, [x24, #112]\n"
+            "add x24, x24, #0x88\n"
+            ".inst 0x450498a3  // smmla z3.s, z5.b, z4.b\n"
+            ".inst 0x451198a7  // smmla z7.s, z5.b, z17.b\n"
+            ".inst 0x45049a49  // smmla z9.s, z18.b, z4.b\n"
+            ".inst 0x45119a5f  // smmla z31.s, z18.b, z17.b\n"
+            "uzp1 z18.d, z3.d, z7.d\n"
+            "uzp2 z5.d, z3.d, z7.d\n"
+            "scvtf z18.s, p1/m, z18.s\n"
+            "uzp1 z6.d, z9.d, z31.d\n"
+            "uzp2 z9.d, z9.d, z31.d\n"
+            "scvtf z5.s, p1/m, z5.s\n"
+            "fmla z8.s, p1/M, z18.s, z22.s\n"
+            "scvtf z6.s, p1/m, z6.s\n"
+            "scvtf z9.s, p1/m, z9.s\n"
+            "fmla z29.s, p1/M, z5.s, z14.s\n"
+            "fmla z27.s, p1/M, z6.s, z2.s\n"
+            "fmla z10.s, p1/M, z9.s, z23.s\n"
+            "bgt 3b\n"
+            "mov x20, %x[res_ptr]\n"
+            "subs x10, x10, #0x8\n"
+            "add %x[res_ptr], %x[res_ptr], #0x20\n"
+            "st1w { z24.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z15.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z12.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z0.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z13.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z1.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z20.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z25.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z11.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z16.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z19.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z26.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z8.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z29.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z27.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "st1w { z10.s }, p1, [x20]\n"
+            "bne 2b\n"
+            "mov x20, #0x4\n"
+            "sub x13, x13, #0x10\n"
+            "cmp x13, #0x10\n"
+            "mov %x[res_ptr], x9\n"
+            "madd %x[a_ptr], x20, x12, %x[a_ptr]\n"
+            "bge 1b\n"
+            "4:"  // Row loop skip
+            "cbz x13, 9f\n"
+            "5:"  // Row tail: Row loop
+            "add x25, %x[b_ptr], #0x10\n"
+            "mov x24, %x[nc]\n"
+            "add x23, %x[res_ptr], %x[res_stride], LSL #2\n"
+            "6:"  // Row tail: Column loop
+            "mov z24.b, #0x0\n"
+            "mov z15.b, #0x0\n"
+            "add x28, %x[a_ptr], #0x8\n"
+            "mov x22, %x[nb]\n"
+            "mov z12.b, #0x0\n"
+            "mov z0.b, #0x0\n"
+            "7:"  // Row tail: Block loop
+            "ld1b { z3.b }, p1/Z, [x25]\n"
+            "ld1b { z6.b }, p1/Z, [x25, #1, MUL VL]\n"
+            "mov z2.s, #0x0\n"
+            "mov z25.s, #0x0\n"
+            "ld1rqb { z26.b }, p1/Z, [x28]\n"
+            "ld1rqb { z21.b }, p1/Z, [x28, #16]\n"
+            "mov z27.s, #0x0\n"
+            "mov z19.s, #0x0\n"
+            "ld1b { z29.b }, p1/Z, [x25, #2, MUL VL]\n"
+            "ld1b { z16.b }, p1/Z, [x25, #3, MUL VL]\n"
+            "sub x21, x25, #0x10\n"
+            "sub x20, x28, #0x8\n"
+            "lsl z20.b, z3.b, #0x4\n"
+            "lsl z4.b, z6.b, #0x4\n"
+            "ld1rqb { z10.b }, p1/Z, [x28, #32]\n"
+            "ld1rqb { z23.b }, p1/Z, [x28, #48]\n"
+            "and z3.b, z3.b, #0xf0\n"
+            "and z6.b, z6.b, #0xf0\n"
+            "ld1rqb { z11.b }, p1/Z, [x28, #64]\n"
+            "ld1rqb { z7.b }, p1/Z, [x28, #80]\n"
+            "lsl z8.b, z29.b, #0x4\n"
+            "lsl z14.b, z16.b, #0x4\n"
+            "ld1rqb { z18.b }, p1/Z, [x28, #96]\n"
+            "ld1rqb { z30.b }, p1/Z, [x28, #112]\n"
+            ".inst 0x45149b42  // smmla z2.s, z26.b, z20.b\n"
+            ".inst 0x45049b59  // smmla z25.s, z26.b, z4.b\n"
+            "and z29.b, z29.b, #0xf0\n"
+            "ld1h { z17.s }, p1/Z, [x21]\n"
+            ".inst 0x45149abb  // smmla z27.s, z21.b, z20.b\n"
+            ".inst 0x45049ab3  // smmla z19.s, z21.b, z4.b\n"
+            "and z16.b, z16.b, #0xf0\n"
+            "ld1h { z4.s }, p0/Z, [x20]\n"
+            "subs x22, x22, #0x1\n"
+            "add x28, x28, #0x88\n"
+            "fcvt z17.s, p1/m, z17.h\n"
+            "add x25, x25, #0x90\n"
+            ".inst 0x45089942  // smmla z2.s, z10.b, z8.b\n"
+            ".inst 0x450e9959  // smmla z25.s, z10.b, z14.b\n"
+            "fcvt z4.s, p1/m, z4.h\n"
+            ".inst 0x45089afb  // smmla z27.s, z23.b, z8.b\n"
+            ".inst 0x450e9af3  // smmla z19.s, z23.b, z14.b\n"
+            "fscale z17.s, p1/m, z17.s, z28.s\n"
+            "mov z4.q, z4.q[0]\n"
+            ".inst 0x45039962  // smmla z2.s, z11.b, z3.b\n"
+            ".inst 0x45069979  // smmla z25.s, z11.b, z6.b\n"
+            "fmul z23.s, z17.s, z4.s[0]\n"
+            "fmul z9.s, z17.s, z4.s[1]\n"
+            "fmul z21.s, z17.s, z4.s[2]\n"
+            "fmul z4.s, z17.s, z4.s[3]\n"
+            ".inst 0x450398fb  // smmla z27.s, z7.b, z3.b\n"
+            ".inst 0x450698f3  // smmla z19.s, z7.b, z6.b\n"
+            ".inst 0x451d9a42  // smmla z2.s, z18.b, z29.b\n"
+            ".inst 0x45109a59  // smmla z25.s, z18.b, z16.b\n"
+            ".inst 0x451d9bdb  // smmla z27.s, z30.b, z29.b\n"
+            ".inst 0x45109bd3  // smmla z19.s, z30.b, z16.b\n"
+            "uzp1 z31.d, z2.d, z25.d\n"
+            "uzp2 z13.d, z2.d, z25.d\n"
+            "scvtf z31.s, p1/m, z31.s\n"
+            "uzp1 z17.d, z27.d, z19.d\n"
+            "uzp2 z18.d, z27.d, z19.d\n"
+            "scvtf z13.s, p1/m, z13.s\n"
+            "fmla z24.s, p1/M, z31.s, z23.s\n"
+            "scvtf z17.s, p1/m, z17.s\n"
+            "scvtf z18.s, p1/m, z18.s\n"
+            "fmla z15.s, p1/M, z13.s, z9.s\n"
+            "fmla z12.s, p1/M, z17.s, z21.s\n"
+            "fmla z0.s, p1/M, z18.s, z4.s\n"
+            "bgt 7b\n"
+            "mov x20, %x[res_ptr]\n"
+            "cmp x13, #0x1\n"
+            "st1w { z24.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "ble 8f\n"
+            "cmp x13, #0x2\n"
+            "st1w { z15.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "ble 8f\n"
+            "cmp x13, #0x3\n"
+            "st1w { z12.s }, p1, [x20]\n"
+            "add x20, x20, %x[res_stride]\n"
+            "ble 8f\n"
+            "st1w { z0.s }, p1, [x20]\n"
+            "8:"  // Row tail: Accumulator store skip
+            "subs x24, x24, #0x8\n"
+            "add %x[res_ptr], %x[res_ptr], #0x20\n"
+            "bne 6b\n"
+            "subs x13, x13, #0x4\n"
+            "add %x[a_ptr], %x[a_ptr], x12\n"
+            "mov %x[res_ptr], x23\n"
+            "bgt 5b\n"
+            "9:"  // Row tail: Row loop skip
+            : [a_ptr] "+&r" (a_ptr), [res_ptr] "+&r" (res_ptr)
+            : [b_ptr] "r" (b_ptr), [nr] "r" (nr), [nb] "r" (nb), [res_stride] "r" (res_stride), [nc] "r" (nc)
+            : "cc", "memory", "p0", "p1", "x9", "x10", "x11", "x12", "x13", "x20", "x21", "x22", "x23", "x24", "x25", "x26", "x27", "x28", "z0", "z1", "z2", "z3", "z4", "z5", "z6", "z7", "z8", "z9", "z10", "z11", "z12", "z13", "z14", "z15", "z16", "z17", "z18", "z19", "z20", "z21", "z22", "z23", "z24", "z25", "z26", "z27", "z28", "z29", "z30", "z31"
+        );
+        return;
+    }
+    else if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
+        GGML_ASSERT((ggml_cpu_has_sve() && (svcntw() == 8)) &&
+                    "__ARM_FEATURE_SVE for vector size of 256-bits not defined, use the Q4_0_4_8 quantization format for optimal "
+                    "performance");
+    }
+    else if (ggml_cpu_has_neon()) {
+        GGML_ASSERT(((ggml_cpu_has_sve() && (svcntw() == 8)) || ggml_cpu_has_matmul_int8()) &&
+                    "__ARM_FEATURE_SVE for vector size of 256-bits and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 "
+                    "quantization format for optimal performance");
+    }
+#endif
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+    GGML_ASSERT(ggml_cpu_has_sve() &&
+                "__ARM_FEATURE_SVE not defined, use the Q4_0_4_8 quantization format for optimal performance");
+#elif defined(__ARM_NEON) && defined(__aarch64__)
+    GGML_ASSERT((ggml_cpu_has_sve() || ggml_cpu_has_matmul_int8()) &&
+                "__ARM_FEATURE_SVE and __ARM_FEATURE_MATMUL_INT8 not defined, use the Q4_0_4_4 quantization format for optimal "
+                "performance");
+#else
+    float sumf[4][8];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
+                            }
+                            sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
+#endif
+}
diff --git a/ggml/src/ggml-aarch64.h b/ggml/src/ggml-aarch64.h
new file mode 100644
index 00000000000000..65ead1efed5728
--- /dev/null
+++ b/ggml/src/ggml-aarch64.h
@@ -0,0 +1,39 @@
+// SPDX-FileCopyrightText: Copyright 2024 Arm Ltd.
+#pragma once
+
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+
+#include "ggml.h"
+
+// GGML internal header
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// Quantization
+void quantize_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+
+void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t interleave_blcksize);
+
+// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
+size_t quantize_q4_0_4x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q4_0_4x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q4_0_8x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+
+// GEMV
+void ggml_gemv_q4_0_4x4_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_0_4x8_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_0_8x8_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+
+// GEMM
+void ggml_gemm_q4_0_4x4_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_0_4x8_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_0_8x8_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+
+#ifdef __cplusplus
+}
+#endif
+
diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h
index c74060cc4b9919..fafd5fa7ae000d 100644
--- a/ggml/src/ggml-common.h
+++ b/ggml/src/ggml-common.h
@@ -199,6 +199,30 @@ typedef struct {
 } block_q8_1;
 static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_half) + QK8_1, "wrong q8_1 block size/padding");
 
+typedef struct {
+    ggml_half d[4];        // deltas for 4 q4_0 blocks
+    uint8_t qs[QK4_0 * 2]; // nibbles / quants for 4 q4_0 blocks
+} block_q4_0x4;
+static_assert(sizeof(block_q4_0x4) == 4 * sizeof(ggml_half) + QK4_0 * 2, "wrong q4_0x4 block size/padding");
+
+typedef struct {
+    ggml_half d[8];        // deltas for 8 q4_0 blocks
+    uint8_t qs[QK4_0 * 4]; // nibbles / quants for 8 q4_0 blocks
+} block_q4_0x8;
+static_assert(sizeof(block_q4_0x8) == 8 * sizeof(ggml_half) + QK4_0 * 4, "wrong q4_0x8 block size/padding");
+
+typedef struct {
+    ggml_half d[4];        // deltas for 4 q8_0 blocks
+    int8_t qs[QK8_0 * 4];  // quants for 4 q8_0 blocks
+} block_q8_0x4;
+static_assert(sizeof(block_q8_0x4) == 4 * sizeof(ggml_half) + QK8_0 * 4, "wrong q8_0x4 block size/padding");
+
+typedef struct {
+    ggml_half d[8];        // deltas for 8 q8_0 blocks
+    int8_t qs[QK8_0 * 8];  // quants for 8 q8_0 blocks
+} block_q8_0x8;
+static_assert(sizeof(block_q8_0x8) == 8 * sizeof(ggml_half) + QK8_0 * 8, "wrong q8_0x8 block size/padding");
+
 //
 // Super-block quantization structures
 //
diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h
index 1d23361906c34d..a2c8dbec0824f0 100644
--- a/ggml/src/ggml-impl.h
+++ b/ggml/src/ggml-impl.h
@@ -609,6 +609,10 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
 
 #endif // defined(__ARM_NEON) && (!defined(__MSC_VER)
 
+#ifdef __ARM_FEATURE_SVE
+#include <arm_sve.h>
+#endif // __ARM_FEATURE_SVE
+
 // precomputed f32 table for f16 (256 KB)
 // defined in ggml.c, initialized in ggml_init()
 extern float ggml_table_f32_f16[1 << 16];
diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index 0eb52e485089f9..cbe377cf5caee6 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -3814,43 +3814,47 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     }
 #endif
 #if defined(__ARM_FEATURE_SVE)
-    const svbool_t ptrueh = svptrue_pat_b8(SV_VL16);
-    const svbool_t ptruel = svnot_b_z(svptrue_b8(), ptrueh);
+    if (svcntb() == QK8_0) {
+        const svbool_t ptrueh = svptrue_pat_b8(SV_VL16);
+        const svbool_t ptruel = svnot_b_z(svptrue_b8(), ptrueh);
 
-    svfloat32_t sumv0 = svdup_n_f32(0.0f);
-    svfloat32_t sumv1 = svdup_n_f32(0.0f);
+        svfloat32_t sumv0 = svdup_n_f32(0.0f);
+        svfloat32_t sumv1 = svdup_n_f32(0.0f);
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
+        assert(nb % 2 == 0); // TODO: handle odd nb
 
-    for (int i = 0; i < nb; i += 2) {
-        const block_q4_0 * restrict x0 = &x[i + 0];
-        const block_q4_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i + 0];
-        const block_q8_0 * restrict y1 = &y[i + 1];
+        for (int i = 0; i < nb; i += 2) {
+            const block_q4_0 * restrict x0 = &x[i + 0];
+            const block_q4_0 * restrict x1 = &x[i + 1];
+            const block_q8_0 * restrict y0 = &y[i + 0];
+            const block_q8_0 * restrict y1 = &y[i + 1];
 
-        // load x
-        const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs);
-        const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs);
+            // load x
+            const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs);
+            const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs);
 
-        // 4-bit -> 8-bit
-        const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx0r, 0x0F), 0x04));
-        const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx1r, 0x0F), 0x04));
+            // 4-bit -> 8-bit
+            const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx0r, 0x0F), 0x04));
+            const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx1r, 0x0F), 0x04));
 
-        // sub 8
-        const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8);
-        const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8);
+            // sub 8
+            const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8);
+            const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8);
 
-        // load y
-        const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs);
-        const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs);
+            // load y
+            const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs);
+            const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs);
 
-        // dot product
-        sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
-        sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
-    }
+            // dot product
+            sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+            sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+        }
 
-    *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
-#elif defined(__ARM_NEON)
+        *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
+        return;
+    }
+#endif
+#if defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
     float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
@@ -5422,31 +5426,35 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     }
 #endif
 #if defined(__ARM_FEATURE_SVE)
-    svfloat32_t sumv0 = svdup_n_f32(0.0f);
-    svfloat32_t sumv1 = svdup_n_f32(0.0f);
+    if (svcntb() == QK8_0) {
+        svfloat32_t sumv0 = svdup_n_f32(0.0f);
+        svfloat32_t sumv1 = svdup_n_f32(0.0f);
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
+        assert(nb % 2 == 0); // TODO: handle odd nb
 
-    for (int i = 0; i < nb; i += 2) {
-        const block_q8_0 * restrict x0 = &x[i + 0];
-        const block_q8_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i + 0];
-        const block_q8_0 * restrict y1 = &y[i + 1];
+        for (int i = 0; i < nb; i += 2) {
+            const block_q8_0 * restrict x0 = &x[i + 0];
+            const block_q8_0 * restrict x1 = &x[i + 1];
+            const block_q8_0 * restrict y0 = &y[i + 0];
+            const block_q8_0 * restrict y1 = &y[i + 1];
 
-        // load x
-        const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs);
-        const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs);
+            // load x
+            const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs);
+            const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs);
 
-        // load y
-        const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs);
-        const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs);
+            // load y
+            const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs);
+            const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs);
 
-        sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
-        sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
-    }
+            sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d));
+            sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
+        }
 
-    *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
-#elif defined(__ARM_NEON)
+        *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
+        return;
+    }
+#endif
+#if defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
     float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
@@ -14760,6 +14768,16 @@ static bool validate_fp16(ggml_fp16_t f, size_t i) {
         } \
     }
 
+#define VALIDATE_ROW_DATA_DVEC_F16_IMPL(type, data, nb, nr) \
+    const type * q = (const type *) (data); \
+    for (size_t i = 0; i < (nb); ++i) { \
+        for (size_t j = 0; j < (nr); ++j) { \
+            if (!validate_fp16(q[i].d[j], i)) { \
+                return false; \
+            } \
+        } \
+    }
+
 bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes) {
     if (type < 0 || type >= GGML_TYPE_COUNT) {
         fprintf(stderr, "%s: invalid type %d\n", __func__, type);
@@ -14977,6 +14995,16 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
             {
                 VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb);
             } break;
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+            {
+                VALIDATE_ROW_DATA_DVEC_F16_IMPL(block_q4_0x4, data, nbytes / sizeof(block_q4_0x4), 4);
+            } break;
+        case GGML_TYPE_Q4_0_8_8:
+            {
+                VALIDATE_ROW_DATA_DVEC_F16_IMPL(block_q4_0x8, data, nbytes / sizeof(block_q4_0x8), 8);
+            } break;
+
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index bc91ac3a726abd..c0aced3d2d0692 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -4,6 +4,7 @@
 #include "ggml-impl.h"
 #include "ggml-quants.h"
 #include "ggml.h"
+#include "ggml-aarch64.h"
 
 
 #if defined(_MSC_VER) || defined(__MINGW32__)
@@ -37,7 +38,7 @@
 #include <unistd.h>
 #endif
 
-#ifdef __ARM_FEATURE_MATMUL_INT8
+#if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8)
 #undef GGML_USE_LLAMAFILE
 #endif
 
@@ -692,6 +693,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
 #else
         .nrows                    = 1,
 #endif
+        .from_float_to_mat        = quantize_mat_q8_0,
     },
     [GGML_TYPE_Q8_1] = {
         .type_name                = "q8_1",
@@ -889,6 +891,54 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_bf16,
         .vec_dot_type             = GGML_TYPE_BF16,
         .nrows                    = 1,
+    },
+    [GGML_TYPE_Q4_0_4_4] = {
+        .type_name                = "q4_0_4x4",
+        .blck_size                = QK4_0,
+        .type_size                = sizeof(block_q4_0),
+        .is_quantized             = true,
+        .to_float                 = NULL,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
+        .vec_dot                  = NULL,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+        .ncols                    = 4,
+        .interleave_blcksize      = 4,
+        .gemv                     = ggml_gemv_q4_0_4x4_q8_0,
+        .gemm                     = ggml_gemm_q4_0_4x4_q8_0,
+    },
+    [GGML_TYPE_Q4_0_4_8] = {
+        .type_name                = "q4_0_4x8",
+        .blck_size                = QK4_0,
+        .type_size                = sizeof(block_q4_0),
+        .is_quantized             = true,
+        .to_float                 = NULL,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
+        .vec_dot                  = NULL,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+        .ncols                    = 4,
+        .interleave_blcksize      = 8,
+        .gemv                     = ggml_gemv_q4_0_4x8_q8_0,
+        .gemm                     = ggml_gemm_q4_0_4x8_q8_0,
+    },
+    [GGML_TYPE_Q4_0_8_8] = {
+        .type_name                = "q4_0_8x8",
+        .blck_size                = QK4_0,
+        .type_size                = sizeof(block_q4_0),
+        .is_quantized             = true,
+        .to_float                 = NULL,
+        .from_float               = NULL,
+        .from_float_reference     = NULL,
+        .vec_dot                  = NULL,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+        .ncols                    = 8,
+        .interleave_blcksize      = 8,
+        .gemv                     = ggml_gemv_q4_0_8x8_q8_0,
+        .gemm                     = ggml_gemm_q4_0_8x8_q8_0,
     }
 };
 
@@ -3188,6 +3238,9 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
         case GGML_FTYPE_MOSTLY_IQ4_XS:        wtype = GGML_TYPE_IQ4_XS;   break;
         case GGML_FTYPE_MOSTLY_IQ3_S:         wtype = GGML_TYPE_IQ3_S;    break;
         case GGML_FTYPE_MOSTLY_IQ2_S:         wtype = GGML_TYPE_IQ2_S;    break;
+        case GGML_FTYPE_MOSTLY_Q4_0_4_4:      wtype = GGML_TYPE_Q4_0_4_4; break;
+        case GGML_FTYPE_MOSTLY_Q4_0_4_8:      wtype = GGML_TYPE_Q4_0_4_8; break;
+        case GGML_FTYPE_MOSTLY_Q4_0_8_8:      wtype = GGML_TYPE_Q4_0_8_8; break;
         case GGML_FTYPE_UNKNOWN:              wtype = GGML_TYPE_COUNT; break;
         case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
     }
@@ -9432,6 +9485,9 @@ static void ggml_compute_forward_add(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
             {
                 ggml_compute_forward_add_q_f32(params, dst);
             } break;
@@ -9807,6 +9863,9 @@ static void ggml_compute_forward_add1(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
             {
                 ggml_compute_forward_add1_q_f32(params, dst);
             } break;
@@ -9932,6 +9991,9 @@ static void ggml_compute_forward_acc(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
         default:
             {
                 GGML_ASSERT(false);
@@ -12134,6 +12196,12 @@ static void ggml_compute_forward_mul_mat(
     enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
     ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
     int64_t           const vec_dot_num_rows      = type_traits[type].nrows;
+    int64_t           const matmul_num_cols       = type_traits[type].ncols;
+    int64_t           const interleave_blcksize   = type_traits[type].interleave_blcksize;
+    ggml_from_float_to_mat_t const from_float_to_mat
+                                                  = type_traits[vec_dot_type].from_float_to_mat;
+    ggml_gemv_t       const gemv                  = type_traits[type].gemv;
+    ggml_gemm_t       const gemm                  = type_traits[type].gemm;
 
     GGML_ASSERT(ne0 == ne01);
     GGML_ASSERT(ne1 == ne11);
@@ -12192,7 +12260,16 @@ UseGgmlGemm1:;
 
         for (int64_t i13 = 0; i13 < ne13; ++i13) {
             for (int64_t i12 = 0; i12 < ne12; ++i12) {
-                for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
+                int64_t i11_processed = 0;
+                if ((ggml_n_dims(src1) == 2) && from_float_to_mat && gemm) {
+                    for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
+                        from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
+                                          (void *)               (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
+                                          4, ne10, interleave_blcksize);
+                    }
+                    i11_processed = ne11 - ne11 % 4;
+                }
+                for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) {
                     from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
                                           (void *)               (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
                                            ne10);
@@ -12273,6 +12350,28 @@ UseGgmlGemm2:;
     const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
     const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1;
 
+    if ((ggml_n_dims(src0) == 2) && gemv) {
+        const void * src1_wdata      = (src1->type == vec_dot_type) ? src1->data : params->wdata;
+        const size_t src1_col_stride = ggml_is_contiguous(src1) || src1->type != vec_dot_type ? ggml_row_size(vec_dot_type, ne10) : nb11;
+        int64_t src0_start = (ith * ne01) / nth;
+        int64_t src0_end   = ((ith + 1) * ne01) / nth;
+        src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start;
+        src0_end   = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end;
+        if (src0_start >= src0_end) return;
+
+        // If there are more than three rows in src1, use gemm; otherwise, use gemv.
+        if (gemm && (ne11 > 3)) {
+            gemm(ne00, (float *)((char *) dst->data) + src0_start, ne01, (const char *) src0->data + src0_start * nb01,
+                 (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start);
+        }
+        for (int iter = gemm ? ne11 - ne11 % 4 : 0; iter < ne11; iter++) {
+            gemv(ne00, (float *)((char *) dst->data + (iter * nb1)) + src0_start, ne01,
+                 (const char *) src0->data + src0_start * nb01, (const char *) src1_wdata + (src1_col_stride * iter), 1,
+                 src0_end - src0_start);
+        }
+        return;
+    }
+
     // The first chunk comes from our thread_id, the rest will get auto-assigned.
     int current_chunk = ith;
 
@@ -12318,6 +12417,8 @@ static void ggml_compute_forward_mul_mat_id(
     ggml_vec_dot_t    const vec_dot               = type_traits[type].vec_dot;
     enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
     ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
+    int64_t           const matmul_num_cols       = type_traits[type].ncols;
+    ggml_gemv_t       const gemv                  = type_traits[type].gemv;
 
     // we don't support permuted src0 or src1
     GGML_ASSERT(nb00 == ggml_type_size(type));
@@ -12403,6 +12504,34 @@ static void ggml_compute_forward_mul_mat_id(
         const int64_t nr0 = ne01; // src0 rows
         const int64_t nr1 = cne1; // src1 rows
 
+        if (((ggml_n_dims(src0) - 1) == 2) && gemv) {
+            int64_t src0_cur_start = (ith * ne01) / nth;
+            int64_t src0_cur_end   = ((ith + 1) * ne01) / nth;
+            src0_cur_start = (src0_cur_start % matmul_num_cols) ? src0_cur_start + matmul_num_cols - (src0_cur_start % matmul_num_cols): src0_cur_start;
+            src0_cur_end   = (src0_cur_end % matmul_num_cols) ? src0_cur_end + matmul_num_cols - (src0_cur_end % matmul_num_cols): src0_cur_end;
+            if (src0_cur_start >= src0_cur_end) return;
+
+            for (int ir1 = 0; ir1 < nr1; ir1++) {
+                struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1);
+                const int id       = row_mapping.i1; // selected expert index
+
+                const int64_t  i11 = id % ne11;
+                const int64_t  i12 = row_mapping.i2; // row index in src1
+
+                const int64_t  i1 = id;  // selected expert index
+                const int64_t  i2 = i12; // row
+
+                const char * src1_col = (const char *) wdata +
+                    (src1_cont || src1->type != vec_dot_type
+                    ? (i11        + i12 * ne11) * row_size
+                    : (i11 * nb11 + i12 * nb12));
+
+                gemv(ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01,
+                     (const char *) src0_cur + src0_cur_start * nb01, src1_col, 1, src0_cur_end - src0_cur_start);
+            }
+            continue;
+        }
+
         // distribute the thread work across the inner or outer loop based on which one is larger
 
         const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows
@@ -12704,6 +12833,9 @@ static void ggml_compute_forward_out_prod(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
             {
                 ggml_compute_forward_out_prod_q_f32(params, dst);
             } break;
@@ -12889,6 +13021,9 @@ static void ggml_compute_forward_set(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
         default:
             {
                 GGML_ASSERT(false);
@@ -13148,6 +13283,9 @@ static void ggml_compute_forward_get_rows(
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
             {
                 ggml_compute_forward_get_rows_q(params, dst);
             } break;
@@ -13734,6 +13872,9 @@ static void ggml_compute_forward_clamp(
         case GGML_TYPE_IQ3_S:
         case GGML_TYPE_IQ2_S:
         case GGML_TYPE_Q8_K:
+        case GGML_TYPE_Q4_0_4_4:
+        case GGML_TYPE_Q4_0_4_8:
+        case GGML_TYPE_Q4_0_8_8:
         case GGML_TYPE_I8:
         case GGML_TYPE_I16:
         case GGML_TYPE_I32:
@@ -20457,6 +20598,9 @@ size_t ggml_quantize_chunk(
         case GGML_TYPE_IQ1_M:   result = quantize_iq1_m  (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_NL:  result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_IQ4_XS:  result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_0_4_4: result = quantize_q4_0_4x4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_0_4_8: result = quantize_q4_0_4x8(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_Q4_0_8_8: result = quantize_q4_0_8x8(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
         case GGML_TYPE_F16:
             {
                 size_t elemsize = sizeof(ggml_fp16_t);
@@ -21759,8 +21903,6 @@ int ggml_cpu_has_neon(void) {
 
 int ggml_cpu_has_sve(void) {
 #if defined(__ARM_FEATURE_SVE)
-    // TODO: Currently, SVE 256 bit is only supported.
-    GGML_ASSERT(svcntb() == QK8_0);
     return 1;
 #else
     return 0;
diff --git a/include/llama.h b/include/llama.h
index bb4b05ba636711..3970c3aebcd623 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -162,6 +162,9 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_IQ4_XS        = 30, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_IQ1_M         = 31, // except 1d tensors
         LLAMA_FTYPE_MOSTLY_BF16          = 32, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0_4_4      = 33, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0_4_8      = 34, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0_8_8      = 35, // except 1d tensors
 
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };
diff --git a/src/llama.cpp b/src/llama.cpp
index 98a242081ca037..b19d786e23643b 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -3788,6 +3788,9 @@ struct llama_model_loader {
                 case GGML_TYPE_IQ4_NL:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL;  break;
                 case GGML_TYPE_IQ4_XS:  ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS;  break;
                 case GGML_TYPE_IQ3_S:   ftype = LLAMA_FTYPE_MOSTLY_IQ3_S;   break;
+                case GGML_TYPE_Q4_0_4_4: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_4; break;
+                case GGML_TYPE_Q4_0_4_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_8; break;
+                case GGML_TYPE_Q4_0_8_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_8_8; break;
                 default:
                     {
                         LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
@@ -4481,6 +4484,9 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
         case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ3_S:  return "IQ3_S - 3.4375 bpw";
         case LLAMA_FTYPE_MOSTLY_IQ3_M:  return "IQ3_S mix - 3.66 bpw";
+        case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: return "Q4_0_4_4";
+        case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: return "Q4_0_4_8";
+        case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: return "Q4_0_8_8";
 
         default: return "unknown, may not work";
     }
@@ -17768,6 +17774,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
             else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
                 new_type = GGML_TYPE_IQ3_S;
             }
+            else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 ||
+                     new_type == GGML_TYPE_Q4_0_8_8) {
+                new_type = GGML_TYPE_Q4_0;
+            }
         }
     } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
                ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
@@ -18080,6 +18090,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         case LLAMA_FTYPE_MOSTLY_IQ4_XS:  default_type = GGML_TYPE_IQ4_XS;  break;
         case LLAMA_FTYPE_MOSTLY_IQ3_S:   default_type = GGML_TYPE_IQ3_S;   break;
         case LLAMA_FTYPE_MOSTLY_IQ3_M:   default_type = GGML_TYPE_IQ3_S;   break;
+        case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: default_type = GGML_TYPE_Q4_0_4_4; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: default_type = GGML_TYPE_Q4_0_4_8; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: default_type = GGML_TYPE_Q4_0_8_8; break;
 
         default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
     }
@@ -18390,6 +18403,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
                 f32_data = (float *) f32_conv_buf.data();
             }
 
+            int chunk_size_multiplier = 1;
+            if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 || new_type == GGML_TYPE_Q4_0_8_8) {
+                if ((new_type == GGML_TYPE_Q4_0_8_8) && (tensor->ne[1] % 8 != 0)) new_type = GGML_TYPE_Q4_0;
+                else if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_0;
+                if (new_type == GGML_TYPE_Q4_0_8_8) chunk_size_multiplier = 8;
+                else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8) chunk_size_multiplier = 4;
+            }
+
             LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
             fflush(stdout);
 
@@ -18402,7 +18423,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             const int64_t nrows = tensor->ne[1];
 
             static const int64_t min_chunk_size = 32 * 512;
-            const int64_t chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row);
+            const int64_t chunk_size = (n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row)) *
+                                       chunk_size_multiplier;
 
             const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1];
             const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;

From 6b2a849d1f43d46b82d2f9c08c3275137b528784 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 10 Jul 2024 15:23:29 +0300
Subject: [PATCH 025/125] ggml : move sgemm sources to llamafile subfolder
 (#8394)

ggml-ci
---
 Makefile                           | 8 ++++----
 ggml/CMakeLists.txt                | 2 +-
 ggml/src/CMakeLists.txt            | 6 +++---
 ggml/src/ggml.c                    | 3 +--
 ggml/src/{ => llamafile}/sgemm.cpp | 0
 ggml/src/{ => llamafile}/sgemm.h   | 0
 6 files changed, 9 insertions(+), 10 deletions(-)
 rename ggml/src/{ => llamafile}/sgemm.cpp (100%)
 rename ggml/src/{ => llamafile}/sgemm.h (100%)

diff --git a/Makefile b/Makefile
index b70ebaed546386..68197fef800199 100644
--- a/Makefile
+++ b/Makefile
@@ -554,7 +554,7 @@ endif # GGML_BLIS
 
 ifndef GGML_NO_LLAMAFILE
 	MK_CPPFLAGS += -DGGML_USE_LLAMAFILE
-	OBJ_GGML    += ggml/src/sgemm.o
+	OBJ_GGML    += ggml/src/llamafile/sgemm.o
 endif
 
 ifdef GGML_RPC
@@ -983,9 +983,9 @@ ggml/src/ggml-blas.o: \
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 ifndef GGML_NO_LLAMAFILE
-ggml/src/sgemm.o: \
-	ggml/src/sgemm.cpp \
-	ggml/src/sgemm.h \
+ggml/src/llamafile/sgemm.o: \
+	ggml/src/llamafile/sgemm.cpp \
+	ggml/src/llamafile/sgemm.h \
 	ggml/include/ggml.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 endif # GGML_NO_LLAMAFILE
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 0d0d52d5759710..649ac3dcc4f63a 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -104,7 +104,7 @@ option(GGML_ACCELERATE                      "ggml: enable Accelerate framework"
 option(GGML_BLAS                            "ggml: use BLAS"                                  ${GGML_BLAS_DEFAULT})
 set(GGML_BLAS_VENDOR ${GGML_BLAS_VENDOR_DEFAULT} CACHE STRING
                                             "ggml: BLAS library vendor")
-option(GGML_LLAMAFILE                       "ggml: use ggml SGEMM"                            OFF)
+option(GGML_LLAMAFILE                       "ggml: use LLAMAFILE"                             OFF)
 
 option(GGML_CUDA                            "ggml: use CUDA"                                  OFF)
 option(GGML_CUDA_FORCE_DMMV                 "ggml: use dmmv instead of mmvq CUDA kernels"     OFF)
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index aae5b8e9fe35c4..c5ee7e4255ee5f 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -238,12 +238,12 @@ if (GGML_BLAS)
 endif()
 
 if (GGML_LLAMAFILE)
-    message(STATUS "Using ggml SGEMM")
+    message(STATUS "Using llamafile")
 
     add_compile_definitions(GGML_USE_LLAMAFILE)
 
-    set(GGML_HEADERS_LLAMAFILE sgemm.h)
-    set(GGML_SOURCES_LLAMAFILE sgemm.cpp)
+    set(GGML_HEADERS_LLAMAFILE llamafile/sgemm.h)
+    set(GGML_SOURCES_LLAMAFILE llamafile/sgemm.cpp)
 endif()
 
 if (GGML_CUDA)
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index c0aced3d2d0692..1bb731e16d51dd 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -6,7 +6,6 @@
 #include "ggml.h"
 #include "ggml-aarch64.h"
 
-
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <malloc.h> // using malloc.h with MSC/MINGW
 #elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
@@ -43,7 +42,7 @@
 #endif
 
 #ifdef GGML_USE_LLAMAFILE
-#include "sgemm.h"
+#include <llamafile/sgemm.h>
 #endif
 
 #if defined(_MSC_VER)
diff --git a/ggml/src/sgemm.cpp b/ggml/src/llamafile/sgemm.cpp
similarity index 100%
rename from ggml/src/sgemm.cpp
rename to ggml/src/llamafile/sgemm.cpp
diff --git a/ggml/src/sgemm.h b/ggml/src/llamafile/sgemm.h
similarity index 100%
rename from ggml/src/sgemm.h
rename to ggml/src/llamafile/sgemm.h

From f4444d992c16b6b9442f4770c7c3a10b19a08343 Mon Sep 17 00:00:00 2001
From: AidanBeltonS <aidan.belton@codeplay.com>
Date: Wed, 10 Jul 2024 16:10:49 +0100
Subject: [PATCH 026/125] [SYCL] Use multi_ptr to clean up deprecated warnings
 (#8256)

---
 ggml/src/ggml-sycl/common.hpp  |   6 ++
 ggml/src/ggml-sycl/convert.cpp |   2 +-
 ggml/src/ggml-sycl/mmq.cpp     | 184 ++++++++++++++++-----------------
 ggml/src/ggml-sycl/norm.cpp    |   6 +-
 ggml/src/ggml-sycl/softmax.cpp |   2 +-
 5 files changed, 103 insertions(+), 97 deletions(-)

diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index 9a1c161b69db57..68d41411b5ece6 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -346,4 +346,10 @@ inline sycl::vec<Tp, n> vec_aligned_load(const Tp* aligned_ptr) {
     return *reinterpret_cast<const sycl::vec<Tp, n>*>(aligned_ptr);
 }
 
+// Helper for accessing pointers with no warnings
+template <typename Tp, int dim>
+static __dpct_inline__ Tp* get_pointer(sycl::local_accessor<Tp, dim> acc) {
+    return acc.template get_multi_ptr<sycl::access::decorated::no>().get();
+}
+
 #endif // GGML_SYCL_COMMON_HPP
diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp
index a15271b516fa80..39c28753cf85c2 100644
--- a/ggml/src/ggml-sycl/convert.cpp
+++ b/ggml/src/ggml-sycl/convert.cpp
@@ -158,7 +158,7 @@ static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k,
                                                    sycl::range<3>(1, 1, 32),
                                                sycl::range<3>(1, 1, 32)),
                              [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q4_K(vx, y, scale_local_acc.get_pointer(), item_ct1);
+                                 dequantize_block_q4_K(vx, y, get_pointer(scale_local_acc), item_ct1);
                              });
         });
     }
diff --git a/ggml/src/ggml-sycl/mmq.cpp b/ggml/src/ggml-sycl/mmq.cpp
index b514f00404e1f1..3107ba91948c64 100644
--- a/ggml/src/ggml-sycl/mmq.cpp
+++ b/ggml/src/ggml-sycl/mmq.cpp
@@ -1835,10 +1835,10 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q4_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_qs_q4_0_acc_ct1.get_pointer(),
-                            tile_x_d_q4_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_qs_q4_0_acc_ct1),
+                            get_pointer(tile_x_d_q4_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -1870,10 +1870,10 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q4_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_qs_q4_0_acc_ct1.get_pointer(),
-                            tile_x_d_q4_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_qs_q4_0_acc_ct1),
+                            get_pointer(tile_x_d_q4_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -1950,10 +1950,10 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q4_1<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_qs_q4_1_acc_ct1.get_pointer(),
-                            tile_x_dm_q4_1_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_qs_q4_1_acc_ct1),
+                            get_pointer(tile_x_dm_q4_1_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -1985,10 +1985,10 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q4_1<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_qs_q4_1_acc_ct1.get_pointer(),
-                            tile_x_dm_q4_1_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_qs_q4_1_acc_ct1),
+                            get_pointer(tile_x_dm_q4_1_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2065,10 +2065,10 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q5_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q5_0_acc_ct1.get_pointer(),
-                            tile_x_d_q5_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q5_0_acc_ct1),
+                            get_pointer(tile_x_d_q5_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2100,10 +2100,10 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q5_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q5_0_acc_ct1.get_pointer(),
-                            tile_x_d_q5_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q5_0_acc_ct1),
+                            get_pointer(tile_x_d_q5_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2180,10 +2180,10 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q5_1<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q5_1_acc_ct1.get_pointer(),
-                            tile_x_dm_q5_1_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q5_1_acc_ct1),
+                            get_pointer(tile_x_dm_q5_1_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2215,10 +2215,10 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q5_1<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q5_1_acc_ct1.get_pointer(),
-                            tile_x_dm_q5_1_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q5_1_acc_ct1),
+                            get_pointer(tile_x_dm_q5_1_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2295,10 +2295,10 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q8_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_qs_q8_0_acc_ct1.get_pointer(),
-                            tile_x_d_q8_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_qs_q8_0_acc_ct1),
+                            get_pointer(tile_x_d_q8_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2330,10 +2330,10 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q8_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_qs_q8_0_acc_ct1.get_pointer(),
-                            tile_x_d_q8_0_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_qs_q8_0_acc_ct1),
+                            get_pointer(tile_x_d_q8_0_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2412,11 +2412,11 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q2_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q2_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q2_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q2_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q2_K_acc_ct1),
+                            get_pointer(tile_x_dm_q2_K_acc_ct1),
+                            get_pointer(tile_x_sc_q2_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2450,11 +2450,11 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q2_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q2_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q2_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q2_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q2_K_acc_ct1),
+                            get_pointer(tile_x_dm_q2_K_acc_ct1),
+                            get_pointer(tile_x_sc_q2_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2537,12 +2537,12 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q3_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q3_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q3_K_acc_ct1.get_pointer(),
-                            tile_x_qh_q3_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q3_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q3_K_acc_ct1),
+                            get_pointer(tile_x_dm_q3_K_acc_ct1),
+                            get_pointer(tile_x_qh_q3_K_acc_ct1),
+                            get_pointer(tile_x_sc_q3_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2578,12 +2578,12 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q3_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q3_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q3_K_acc_ct1.get_pointer(),
-                            tile_x_qh_q3_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q3_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q3_K_acc_ct1),
+                            get_pointer(tile_x_dm_q3_K_acc_ct1),
+                            get_pointer(tile_x_qh_q3_K_acc_ct1),
+                            get_pointer(tile_x_sc_q3_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2663,11 +2663,11 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q4_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q4_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q4_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q4_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q4_K_acc_ct1),
+                            get_pointer(tile_x_dm_q4_K_acc_ct1),
+                            get_pointer(tile_x_sc_q4_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2701,11 +2701,11 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q4_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q4_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q4_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q4_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q4_K_acc_ct1),
+                            get_pointer(tile_x_dm_q4_K_acc_ct1),
+                            get_pointer(tile_x_sc_q4_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2784,11 +2784,11 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q5_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q5_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q5_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q5_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q5_K_acc_ct1),
+                            get_pointer(tile_x_dm_q5_K_acc_ct1),
+                            get_pointer(tile_x_sc_q5_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2822,11 +2822,11 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q5_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_q5_K_acc_ct1.get_pointer(),
-                            tile_x_dm_q5_K_acc_ct1.get_pointer(),
-                            tile_x_sc_q5_K_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_q5_K_acc_ct1),
+                            get_pointer(tile_x_dm_q5_K_acc_ct1),
+                            get_pointer(tile_x_sc_q5_K_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2905,11 +2905,11 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q6_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_acc_ct1.get_pointer(),
-                            tile_x_dm_acc_ct1.get_pointer(),
-                            tile_x_sc_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_acc_ct1),
+                            get_pointer(tile_x_dm_acc_ct1),
+                            get_pointer(tile_x_sc_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
@@ -2943,11 +2943,11 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
                         mul_mat_q6_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
-                            tile_x_ql_acc_ct1.get_pointer(),
-                            tile_x_dm_acc_ct1.get_pointer(),
-                            tile_x_sc_acc_ct1.get_pointer(),
-                            tile_y_qs_acc_ct1.get_pointer(),
-                            tile_y_ds_acc_ct1.get_pointer());
+                            get_pointer(tile_x_ql_acc_ct1),
+                            get_pointer(tile_x_dm_acc_ct1),
+                            get_pointer(tile_x_sc_acc_ct1),
+                            get_pointer(tile_y_qs_acc_ct1),
+                            get_pointer(tile_y_ds_acc_ct1));
                     });
             });
         }
diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp
index e0c5dfeca96ca6..cccf87d069a31a 100644
--- a/ggml/src/ggml-sycl/norm.cpp
+++ b/ggml/src/ggml-sycl/norm.cpp
@@ -218,7 +218,7 @@ static void norm_f32_sycl(const float* x, float* dst, const int ncols,
                 [=](sycl::nd_item<3> item_ct1)
                 [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                     norm_f32(x, dst, ncols, eps, item_ct1,
-                        s_sum_acc_ct1.get_pointer(), work_group_size);
+                        get_pointer(s_sum_acc_ct1), work_group_size);
                 });
             });
     }
@@ -265,7 +265,7 @@ static void group_norm_f32_sycl(const float* x, float* dst,
                 [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                     group_norm_f32(x, dst, group_size, ne_elements,
                         eps_ct4, item_ct1,
-                        s_sum_acc_ct1.get_pointer(), work_group_size);
+                        get_pointer(s_sum_acc_ct1), work_group_size);
                 });
             });
     }
@@ -306,7 +306,7 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols,
                 [=](sycl::nd_item<3> item_ct1)
                 [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                     rms_norm_f32(x, dst, ncols, eps, item_ct1,
-                        s_sum_acc_ct1.get_pointer(), work_group_size);
+                        get_pointer(s_sum_acc_ct1), work_group_size);
                 });
             });
     }
diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp
index e624b6ba31b0dd..c5d9a837eb7941 100644
--- a/ggml/src/ggml-sycl/softmax.cpp
+++ b/ggml/src/ggml-sycl/softmax.cpp
@@ -136,7 +136,7 @@ static void soft_max_f32_submitter(const float * x, const float * mask, float *
                 soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, dst, ncols_par,
                                                                              nrows_y, scale, max_bias, m0,
                                                                              m1, n_head_log2, item_ct1,
-                                                                             local_buf_acc.get_pointer());
+                                                                             get_pointer(local_buf_acc));
             });
     });
 }

From dd07a123b79f9bd9e8a4ba0447427b3083e9347a Mon Sep 17 00:00:00 2001
From: Clint Herron <hanclinto@gmail.com>
Date: Wed, 10 Jul 2024 12:35:18 -0400
Subject: [PATCH 027/125] Name Migration: Build the deprecation-warning 'main'
 binary every time (#8404)

* Modify the deprecation-warning 'main' binary to build every time, instead of only when a legacy binary is present. This is to help users of tutorials and other instruction sets from knowing what to do when the 'main' binary is missing and they are trying to follow instructions.

* Adjusting 'server' name-deprecation binary to build all the time, similar to the 'main' legacy name binary.
---
 Makefile | 24 ++++++++----------------
 1 file changed, 8 insertions(+), 16 deletions(-)

diff --git a/Makefile b/Makefile
index 68197fef800199..668b38b99c3129 100644
--- a/Makefile
+++ b/Makefile
@@ -1513,15 +1513,17 @@ llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \
 # Mark legacy binary targets as .PHONY so that they are always checked.
 .PHONY: main quantize perplexity embedding server finetune
 
+# NOTE: We currently will always build the deprecation-warning `main` and `server` binaries to help users migrate.
+#  Eventually we will want to remove these target from building all the time.
 main: examples/deprecation-warning/deprecation-warning.cpp
-ifneq (,$(wildcard main))
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-	@echo "#########"
-	@echo "WARNING: The 'main' binary is deprecated. Please use 'llama-cli' instead."
-	@echo "  Remove the 'main' binary to remove this warning."
-	@echo "#########"
-endif
+	@echo "NOTICE: The 'main' binary is deprecated. Please use 'llama-cli' instead."
+
+server: examples/deprecation-warning/deprecation-warning.cpp
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+	@echo "NOTICE: The 'server' binary is deprecated. Please use 'llama-server' instead."
 
 quantize: examples/deprecation-warning/deprecation-warning.cpp
 ifneq (,$(wildcard quantize))
@@ -1553,16 +1555,6 @@ ifneq (,$(wildcard embedding))
 	@echo "#########"
 endif
 
-server: examples/deprecation-warning/deprecation-warning.cpp
-ifneq (,$(wildcard server))
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-	@echo "#########"
-	@echo "WARNING: The 'server' binary is deprecated. Please use 'llama-server' instead."
-	@echo "  Remove the 'server' binary to remove this warning."
-	@echo "#########"
-endif
-
 finetune: examples/deprecation-warning/deprecation-warning.cpp
 ifneq (,$(wildcard finetune))
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)

From 278d0e18469aacf505be18ce790a63c7cc31be26 Mon Sep 17 00:00:00 2001
From: Clint Herron <hanclinto@gmail.com>
Date: Wed, 10 Jul 2024 20:08:17 -0400
Subject: [PATCH 028/125] Initialize default slot sampling parameters from the
 global context. (#8418)

---
 examples/server/server.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 8feff67029da1a..efd426289a54cb 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -737,6 +737,8 @@ struct server_context {
             slot.ga_n = ga_n;
             slot.ga_w = ga_w;
 
+            slot.sparams = params.sparams;
+
             slot.reset();
 
             slots.push_back(slot);

From 7a221b672e49dfae459b1af27210ba3f2b5419b6 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 11 Jul 2024 10:21:30 +0300
Subject: [PATCH 029/125] llama : use F32 precision in Qwen2 attention and no
 FA (#8412)

---
 src/llama.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index b19d786e23643b..ed77ed918e554e 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -8134,7 +8134,7 @@ static struct ggml_tensor * llm_build_kqv(
         struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
         cb(kq, "kq", il);
 
-        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) {
+        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2) {
             // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
             // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
             ggml_mul_mat_set_prec(kq, GGML_PREC_F32);

From 9a55ffe6fbf7f19c865f8f277ca7cd585cc0b094 Mon Sep 17 00:00:00 2001
From: compilade <git@compilade.net>
Date: Thu, 11 Jul 2024 03:41:48 -0400
Subject: [PATCH 030/125] tokenize : add --no-parse-special option (#8423)

This should allow more easily explaining
how parse_special affects tokenization.
---
 examples/tokenize/tokenize.cpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp
index 0180c87d863bfe..2afb6024ce1bfd 100644
--- a/examples/tokenize/tokenize.cpp
+++ b/examples/tokenize/tokenize.cpp
@@ -29,6 +29,7 @@ static void print_usage_information(const char * argv0, FILE * stream) {
     fprintf(stream, "    -p PROMPT, --prompt PROMPT           read prompt from the argument.\n");
     fprintf(stream, "    --stdin                              read prompt from standard input.\n");
     fprintf(stream, "    --no-bos                             do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n");
+    fprintf(stream, "    --no-parse-special                   do not parse control tokens.\n");
     fprintf(stream, "    --log-disable                        disable logs. Makes stderr quiet when loading the model.\n");
     fprintf(stream, "    --show-count                         print the total number of tokens.\n");
 }
@@ -195,6 +196,7 @@ int main(int raw_argc, char ** raw_argv) {
     // variables where to put any arguments we see.
     bool printing_ids = false;
     bool no_bos = false;
+    bool no_parse_special = false;
     bool disable_logging = false;
     bool show_token_count = false;
     const char * model_path = NULL;
@@ -229,6 +231,9 @@ int main(int raw_argc, char ** raw_argv) {
         else if (arg == "--no-bos") {
             no_bos = true;
         }
+        else if (arg == "--no-parse-special") {
+            no_parse_special = true;
+        }
         else if (arg == "-p" || arg == "--prompt") {
             if (prompt_set) {
                 fprintf(stderr, "Error: -p or --prompt specified multiple times.\n");
@@ -359,9 +364,10 @@ int main(int raw_argc, char ** raw_argv) {
 
     const bool model_wants_add_bos = llama_should_add_bos_token(model);
     const bool add_bos = model_wants_add_bos && !no_bos;
+    const bool parse_special = !no_parse_special;
 
     std::vector<llama_token> tokens;
-    tokens = ::llama_tokenize(model, prompt, add_bos, true);
+    tokens = ::llama_tokenize(model, prompt, add_bos, parse_special);
 
     if (printing_ids) {
         printf("[");

From a977c115448e40856fb9cbe3ceb6d8ce802553b0 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 11 Jul 2024 11:20:40 +0300
Subject: [PATCH 031/125] gitignore : deprecated binaries

---
 .gitignore | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/.gitignore b/.gitignore
index 4866f612277f7e..7c7dee0c607aae 100644
--- a/.gitignore
+++ b/.gitignore
@@ -61,6 +61,11 @@ llama-batched-swift
 out/
 tmp/
 
+# Deprecated
+
+/main
+/server
+
 # CI
 
 !.github/workflows/*.yml

From 808aba39161e5d7ca2ff24110b5aa14d2e536988 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Thu, 11 Jul 2024 16:47:47 +0200
Subject: [PATCH 032/125] CUDA: optimize and refactor MMQ (#8416)

* CUDA: optimize and refactor MMQ

* explicit q8_1 memory layouts, add documentation
---
 ggml/src/ggml-cuda/mma.cuh      |    4 +
 ggml/src/ggml-cuda/mmq.cuh      | 1319 +++++++++++++++++--------------
 ggml/src/ggml-cuda/quantize.cu  |  107 ++-
 ggml/src/ggml-cuda/quantize.cuh |    6 +-
 ggml/src/ggml-cuda/vecdotq.cuh  |   72 +-
 5 files changed, 844 insertions(+), 664 deletions(-)

diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh
index 5d87dd8e64c0aa..a452a3cc3d152f 100644
--- a/ggml/src/ggml-cuda/mma.cuh
+++ b/ggml/src/ggml-cuda/mma.cuh
@@ -70,6 +70,10 @@ struct mma_int_A_I16K8 {
         }
 #endif // defined(INT8_MMA_AVAILABLE)
     }
+
+    __device__ __forceinline__ void load_low(const int * __restrict__ xs0, const int & stride) {
+        ((mma_int_A_I16K4 *) x)[0].load(xs0, stride);
+    }
 };
 
 struct mma_int_B_J8K4 {
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index 118e34d2847fc8..51c44d857a4908 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -8,18 +8,70 @@
 #include <cstdint>
 
 #define MMQ_DP4A_MAX_BATCH_SIZE 64 // Max. batch size to use for dp4a MMQ kernels when FP16 tensor cores are available.
+#define MMQ_ITER_K 256
+#define MMQ_NWARPS 8
 
 typedef void (*load_tiles_mmq_t)(const char * __restrict__ x, int * x_tile, const int & kbx0, const int & i_max, const int & stride);
-typedef void (*vec_dot_mmq_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0);
+typedef void (*vec_dot_mmq_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00);
 typedef void (*mmq_write_back_t)(const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max);
 
+enum mmq_q8_1_ds_layout {
+    MMQ_Q8_1_DS_LAYOUT_D4,
+    MMQ_Q8_1_DS_LAYOUT_DS4,
+    MMQ_Q8_1_DS_LAYOUT_D2S6,
+};
+
 struct block_q8_1_mmq {
-    half2  ds[4];
-    int8_t qs[4*QK8_1];
+    // The y float data is converted to a data layout that can simply be copied to shared memory as a contiguous block.
+    // The y float data is first grouped as blocks of 128 values.
+    // These blocks are then treated as individual data values and transposed.
+    //
+    // To avoid shared memory bank conflicts each block is padded with 16 bytes.
+    // This padding is also used to store block scales/partial sums.
+    // The scales multiplied with the quantized data are equal to the unquantized values.
+    // The partial sums are obtained by summing up a subgroup of the contained values (prior to quantization)
+    //     and are only needed for performance reasons.
+    //
+    // The exact data stored depends on the x data type.
+    union {
+        float d4[4];    // 1 32 bit scale per 32 values, stored as d0,d1,d2,d3
+        half2 ds4[4];   // 1 16 bit scale + 1 16 bit partial sum per 32 values, stored as d0,s0,d1,s1,d2,s2,d3,s3
+        half  d2s6[8];  // 1 16 bit scale per 64 values + 1 16 bit partial sum per 16 values for the first 96 values,
+                        //     stored as d0,d1,s1,s2,s3,s4,s5
+    };
+    int8_t qs[4*QK8_1]; // 128 values quantized to 8 bit each
 };
 static_assert(sizeof(block_q8_1_mmq) == 4*QK8_1 + 4*sizeof(half2), "Unexpected block_q8_1_mmq size");
 static_assert(sizeof(block_q8_1_mmq) == 4*sizeof(block_q8_1),      "Unexpected block_q8_1_mmq size");
 
+static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) {
+    switch (type_x) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q4_1:
+            return MMQ_Q8_1_DS_LAYOUT_DS4;
+        case GGML_TYPE_Q5_0:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
+        case GGML_TYPE_Q5_1:
+            return MMQ_Q8_1_DS_LAYOUT_DS4;
+        case GGML_TYPE_Q8_0:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
+        case GGML_TYPE_Q2_K:
+            return MMQ_Q8_1_DS_LAYOUT_D2S6;
+        case GGML_TYPE_Q3_K:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
+        case GGML_TYPE_Q4_K:
+        case GGML_TYPE_Q5_K:
+            return MMQ_Q8_1_DS_LAYOUT_DS4;
+        case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_XS:
+        case GGML_TYPE_IQ4_NL:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+}
+
 struct tile_x_sizes {
     int qs;
     int dm;
@@ -79,49 +131,46 @@ static constexpr __device__ int get_mmq_y_device() {
 #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
 }
 
-#define MMQ_DP4A_TXS_Q4_0 tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_0 + mmq_y/QI4_0, 0}
-#define MMQ_DP4A_TXS_Q4_1 tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_1 + mmq_y/QI4_1, 0}
-#define MMQ_DP4A_TXS_Q5_0 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_0 + mmq_y/QI5_0, 0}
-#define MMQ_DP4A_TXS_Q5_1 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_1 + mmq_y/QI5_1, 0}
-#define MMQ_DP4A_TXS_Q8_0 tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI8_0 + mmq_y/QI8_0, 0}
-#define MMQ_DP4A_TXS_Q2_K tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE       + mmq_y,       0}
-#define MMQ_DP4A_TXS_Q3_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI3_K + mmq_y/QI3_K, mmq_y*WARP_SIZE/4 + mmq_y/4}
-#define MMQ_DP4A_TXS_Q4_K tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_K + mmq_y/QI4_K, mmq_y*WARP_SIZE/8 + mmq_y/8}
-#define MMQ_DP4A_TXS_Q5_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_K + mmq_y/QI5_K, mmq_y*WARP_SIZE/8 + mmq_y/8}
-#define MMQ_DP4A_TXS_Q6_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI6_K + mmq_y/QI6_K, mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q4_0 tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_0   + mmq_y/QI4_0,     0}
+#define MMQ_DP4A_TXS_Q4_1 tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_1   + mmq_y/QI4_1,     0}
+#define MMQ_DP4A_TXS_Q8_0 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE*2/QI8_0 + mmq_y/(QI8_0/2), 0}
+#define MMQ_DP4A_TXS_Q8_1 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE*2/QI8_1 + mmq_y/(QI8_1/2), 0}
+#define MMQ_DP4A_TXS_Q2_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE         + mmq_y,           0}
+#define MMQ_DP4A_TXS_Q3_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y,                                     mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q4_K tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_K   + mmq_y/QI4_K,     mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q5_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_K   + mmq_y/QI5_K,     mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q6_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI6_K   + mmq_y/QI6_K,     mmq_y*WARP_SIZE/8 + mmq_y/8}
 
 static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml_type type, int mmq_y) {
     return type == GGML_TYPE_Q4_0 ? MMQ_DP4A_TXS_Q4_0 :
         type == GGML_TYPE_Q4_1    ? MMQ_DP4A_TXS_Q4_1 :
-        type == GGML_TYPE_Q5_0    ? MMQ_DP4A_TXS_Q5_0 :
-        type == GGML_TYPE_Q5_1    ? MMQ_DP4A_TXS_Q5_1 :
+        type == GGML_TYPE_Q5_0    ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_Q5_1    ? MMQ_DP4A_TXS_Q8_1 :
         type == GGML_TYPE_Q8_0    ? MMQ_DP4A_TXS_Q8_0 :
         type == GGML_TYPE_Q2_K    ? MMQ_DP4A_TXS_Q2_K :
         type == GGML_TYPE_Q3_K    ? MMQ_DP4A_TXS_Q3_K :
         type == GGML_TYPE_Q4_K    ? MMQ_DP4A_TXS_Q4_K :
         type == GGML_TYPE_Q5_K    ? MMQ_DP4A_TXS_Q5_K :
         type == GGML_TYPE_Q6_K    ? MMQ_DP4A_TXS_Q6_K :
-        type == GGML_TYPE_IQ4_XS  ? MMQ_DP4A_TXS_Q5_0 :
-        type == GGML_TYPE_IQ4_NL  ? MMQ_DP4A_TXS_Q5_0 :
+        type == GGML_TYPE_IQ4_XS  ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_IQ4_NL  ? MMQ_DP4A_TXS_Q8_0 :
         tile_x_sizes{0, 0, 0};
 }
 
-#define MMQ_MMA_TILE_X_K_Q4_0 (1*WARP_SIZE + WARP_SIZE/QI4_0               + 4)
-#define MMQ_MMA_TILE_X_K_Q4_1 (1*WARP_SIZE + WARP_SIZE/QI4_1               + 4)
-#define MMQ_MMA_TILE_X_K_Q5_0 (2*WARP_SIZE + WARP_SIZE/QI5_0               + 4)
-#define MMQ_MMA_TILE_X_K_Q5_1 (2*WARP_SIZE + WARP_SIZE/QI5_1               + 4)
-#define MMQ_MMA_TILE_X_K_Q8_0 (1*WARP_SIZE + WARP_SIZE/QI8_0               + 0)
-#define MMQ_MMA_TILE_X_K_Q2_K (1*WARP_SIZE + WARP_SIZE                     + 4)
-#define MMQ_MMA_TILE_X_K_Q3_K (2*WARP_SIZE + WARP_SIZE/QI3_K + WARP_SIZE/4 + 2)
-#define MMQ_MMA_TILE_X_K_Q4_K (1*WARP_SIZE + WARP_SIZE/QI4_K + WARP_SIZE/8 + 7)
-#define MMQ_MMA_TILE_X_K_Q5_K (2*WARP_SIZE + WARP_SIZE/QI5_K + WARP_SIZE/8 + 7)
-#define MMQ_MMA_TILE_X_K_Q6_K (2*WARP_SIZE + WARP_SIZE/QI6_K + WARP_SIZE/8 + 7)
+#define MMQ_MMA_TILE_X_K_Q4_0 (1*WARP_SIZE + WARP_SIZE/QI4_0                   + 4)
+#define MMQ_MMA_TILE_X_K_Q4_1 (1*WARP_SIZE + WARP_SIZE/QI4_1                   + 4)
+#define MMQ_MMA_TILE_X_K_Q8_0 (2*WARP_SIZE + 2*WARP_SIZE/QI8_0                 + 4)
+#define MMQ_MMA_TILE_X_K_Q8_1 (2*WARP_SIZE + 2*WARP_SIZE/QI8_0                 + 4)
+#define MMQ_MMA_TILE_X_K_Q2_K (2*WARP_SIZE + WARP_SIZE                         + 4)
+#define MMQ_MMA_TILE_X_K_Q3_K (2*WARP_SIZE + WARP_SIZE/(2*QI3_K) + WARP_SIZE/8 + 7)
+#define MMQ_MMA_TILE_X_K_Q4_K (1*WARP_SIZE + WARP_SIZE/QI4_K     + WARP_SIZE/8 + 7)
+#define MMQ_MMA_TILE_X_K_Q5_K (2*WARP_SIZE + WARP_SIZE/QI5_K     + WARP_SIZE/8 + 7)
+#define MMQ_MMA_TILE_X_K_Q6_K (2*WARP_SIZE + WARP_SIZE/QI6_K     + WARP_SIZE/8 + 7)
 
 static_assert(MMQ_MMA_TILE_X_K_Q4_0 % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q4_1 % 8 == 4, "Wrong padding.");
-static_assert(MMQ_MMA_TILE_X_K_Q5_0 % 8 == 4, "Wrong padding.");
-static_assert(MMQ_MMA_TILE_X_K_Q5_1 % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q8_0 % 8 == 4, "Wrong padding.");
+static_assert(MMQ_MMA_TILE_X_K_Q8_1 % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q2_K % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q3_K % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q4_K % 8 == 4, "Wrong padding.");
@@ -131,21 +180,20 @@ static_assert(MMQ_MMA_TILE_X_K_Q6_K % 8 == 4, "Wrong padding.");
 static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
     return type == GGML_TYPE_Q4_0 ? MMQ_MMA_TILE_X_K_Q4_0 :
         type == GGML_TYPE_Q4_1    ? MMQ_MMA_TILE_X_K_Q4_1 :
-        type == GGML_TYPE_Q5_0    ? MMQ_MMA_TILE_X_K_Q5_0 :
-        type == GGML_TYPE_Q5_1    ? MMQ_MMA_TILE_X_K_Q5_1 :
+        type == GGML_TYPE_Q5_0    ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_Q5_1    ? MMQ_MMA_TILE_X_K_Q8_1 :
         type == GGML_TYPE_Q8_0    ? MMQ_MMA_TILE_X_K_Q8_0 :
         type == GGML_TYPE_Q2_K    ? MMQ_MMA_TILE_X_K_Q2_K :
         type == GGML_TYPE_Q3_K    ? MMQ_MMA_TILE_X_K_Q3_K :
         type == GGML_TYPE_Q4_K    ? MMQ_MMA_TILE_X_K_Q4_K :
         type == GGML_TYPE_Q5_K    ? MMQ_MMA_TILE_X_K_Q5_K :
         type == GGML_TYPE_Q6_K    ? MMQ_MMA_TILE_X_K_Q6_K :
-        type == GGML_TYPE_IQ4_XS  ? MMQ_MMA_TILE_X_K_Q5_0 :
-        type == GGML_TYPE_IQ4_NL  ? MMQ_MMA_TILE_X_K_Q5_0 :
+        type == GGML_TYPE_IQ4_XS  ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_IQ4_NL  ? MMQ_MMA_TILE_X_K_Q8_0 :
         0;
 }
 
 #define MMQ_TILE_Y_K (WARP_SIZE + WARP_SIZE/QI8_1)
-#define MMQ_NWARPS 8
 
 static int mmq_get_granularity_host(const int mmq_x, const int cc) {
     return int8_mma_available(cc) && mmq_x >= 48 ? 16 : 8;
@@ -218,7 +266,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_0, mmq_y);
     const int   * x_qs = (const int   *) x;
@@ -226,34 +274,39 @@ static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a(
     const int   * y_qs = (const int   *) y + 4;
     const half2 * y_ds = (const half2 *) y;
 
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_0*VDR_Q4_0_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
 #pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
 
 #pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
 
-            const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2));
+                const int kyqs = QI8_1 * ((k01/2) / (QI8_1/2)) + (k01/2) % (QI8_1/2);
 
-            int u[2*VDR_Q4_0_Q8_1_MMQ];
+                int u[2*VDR_Q4_0_Q8_1_MMQ];
 
 #pragma unroll
-            for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
-                u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l)         % WARP_SIZE];
-                u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI4_0) % WARP_SIZE];
-            }
+                for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
+                    u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + kyqs +  l];
+                    u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + kyqs + (l + QI4_0)];
+                }
 
-            sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
-                (&x_qs[i*(WARP_SIZE + 1) + k0], u, x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0],
-                y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
+                    (&x_qs[i*(WARP_SIZE + 1) + k0/QR4_0], u,
+                     x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/(QR4_0*QI4_0)], y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
         }
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 #ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K8 mma_A;
@@ -271,52 +324,60 @@ static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mma(
     const int   * y_qs = (const int   *) y + 4;
     const half2 * y_ds = (const half2 *) y;
 
-    mma_A A[ntx];
-    float dA[ntx][mma_C::ne/2];
+    mma_A A[ntx][4];
+    float dA[ntx][mma_C::ne/2][4];
 
     const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
 
 #pragma unroll
     for (int n = 0; n < ntx; ++n) {
 #pragma unroll
-        for (int l = 0; l < mma_A::ne; ++l) {
-            const int i     = i0 + n*mma_A::I + mma_A::get_i(l);
-            const int k     = k0              + mma_A::get_k(l) % QI4_0;
-            const int shift =                4*(mma_A::get_k(l) / QI4_0);
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_0*QI4_0) {
+            const int k0 = k00 + k01;
 
-            A[n].x[l] = __vsubss4((x_qs[i*MMQ_MMA_TILE_X_K_Q4_0 + k] >> shift) & 0x0F0F0F0F, 0x08080808);
-        }
+#pragma unroll
+            for (int l = 0; l < mma_A::ne; ++l) {
+                const int i     = i0 + n*mma_A::I + mma_A::get_i(l);
+                const int k     = k0/QR4_0        + mma_A::get_k(l) % QI4_0;
+                const int shift =                4*(mma_A::get_k(l) / QI4_0);
+
+                A[n][k01/(QR4_0*QI4_0)].x[l] = __vsubss4((x_qs[i*MMQ_MMA_TILE_X_K_Q4_0 + k] >> shift) & 0x0F0F0F0F, 0x08080808);
+            }
 
 #pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
 
-            dA[n][l] = x_df[i*MMQ_MMA_TILE_X_K_Q4_0 + k0/QI4_0];
+                dA[n][l][k01/(QR4_0*QI4_0)] = x_df[i*MMQ_MMA_TILE_X_K_Q4_0 + k0/(QR4_0*QI4_0)];
+            }
         }
     }
 
 #pragma unroll
     for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-        mma_B  B;
-        float dB[mma_C::ne/2];
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_0*QI4_0) {
+            mma_B  B;
+            float dB[mma_C::ne/2];
 
-        B.load(y_qs + j0*MMQ_TILE_Y_K + (2*k0) % WARP_SIZE, MMQ_TILE_Y_K);
+            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
 
 #pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int j = j0 + mma_C::get_j(l);
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
 
-            dB[l] = __low2float(y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
-        }
+                dB[l] = __low2float(y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
 
 #pragma unroll
-    for (int n = 0; n < ntx; ++n) {
-            mma_C C;
-            C.mma_K8(A[n], B);
+            for (int n = 0; n < ntx; ++n) {
+                mma_C C;
+                C.mma_K8(A[n][k01/(QR4_0*QI4_0)], B);
 
 #pragma unroll
-            for (int l = 0; l < mma_C::ne; ++l) {
-                sum[(j0/mma_C::J + n)*mma_C::ne + l] += dA[n][l/2]*dB[l%2]*C.x[l];
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += dA[n][l/2][k01/(QR4_0*QI4_0)]*dB[l%2]*C.x[l];
+                }
             }
         }
     }
@@ -381,7 +442,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_1, mmq_y);
     const int   * x_qs = (const int   *) x;
@@ -389,34 +450,39 @@ static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a(
     const int   * y_qs = (const int   *) y + 4;
     const half2 * y_ds = (const half2 *) y;
 
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_1*VDR_Q4_1_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
 #pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
 
 #pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
 
-            const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2));
+                const int kyqs = QI8_1 * ((k01/2) / (QI8_1/2)) + (k01/2) % (QI8_1/2);
 
-            int u[2*VDR_Q4_1_Q8_1_MMQ];
+                int u[2*VDR_Q4_1_Q8_1_MMQ];
 
 #pragma unroll
-            for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
-                u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l)         % WARP_SIZE];
-                u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI4_1) % WARP_SIZE];
-            }
+                for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
+                    u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + kyqs +  l];
+                    u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + kyqs + (l + QI4_1)];
+                }
 
-            sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
-                (&x_qs[i*(WARP_SIZE + 1) + k0], u, x_dm[i*(WARP_SIZE/QI4_1) + i/QI4_1 + k0/QI4_1],
-                y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
+                    (&x_qs[i*(WARP_SIZE + 1) + k0/QR4_1], u,
+                     x_dm[i*(WARP_SIZE/QI4_1) + i/QI4_1 + k0/(QR4_1*QI4_1)], y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
         }
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 #ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K8 mma_A;
@@ -435,50 +501,58 @@ static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mma(
     const int   * y_qs = (const int   *) y + 4;
     const half2 * y_ds = (const half2 *) y;
 
-    mma_A A[ntx];
-    half2 dmA[ntx][mma_C::ne/2];
+    mma_A A[ntx][4];
+    half2 dmA[ntx][mma_C::ne/2][4];
 
     const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
 
 #pragma unroll
     for (int n = 0; n < ntx; ++n) {
-        ((mma_A_K4 *) &A[n])[0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q4_1 + k0, MMQ_MMA_TILE_X_K_Q4_1);
-        A[n].x[2]  = (A[n].x[0] >> 4) & 0x0F0F0F0F;
-        A[n].x[3]  = (A[n].x[1] >> 4) & 0x0F0F0F0F;
-        A[n].x[0] &= 0x0F0F0F0F;
-        A[n].x[1] &= 0x0F0F0F0F;
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_1*QI4_1) {
+            const int k0 = k00 + k01;
+
+            A[n][k01/(QR4_1*QI4_1)].load_low(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q4_1 + k0/QR4_1, MMQ_MMA_TILE_X_K_Q4_1);
+            A[n][k01/(QR4_1*QI4_1)].x[2]  = (A[n][k01/(QR4_1*QI4_1)].x[0] >> 4) & 0x0F0F0F0F;
+            A[n][k01/(QR4_1*QI4_1)].x[3]  = (A[n][k01/(QR4_1*QI4_1)].x[1] >> 4) & 0x0F0F0F0F;
+            A[n][k01/(QR4_1*QI4_1)].x[0] &= 0x0F0F0F0F;
+            A[n][k01/(QR4_1*QI4_1)].x[1] &= 0x0F0F0F0F;
 
 #pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
 
-            dmA[n][l] = x_dm[i*MMQ_MMA_TILE_X_K_Q4_1 + k0/QI4_1];
+                dmA[n][l][k01/(QR4_1*QI4_1)] = x_dm[i*MMQ_MMA_TILE_X_K_Q4_1 + k0/(QR4_1*QI4_1)];
+            }
         }
     }
 
 #pragma unroll
     for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-        mma_B B;
-        half2 dsB[mma_C::ne/2];
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_1*QI4_1) {
+            mma_B B;
+            half2 dsB[mma_C::ne/2];
 
-        B.load(y_qs + j0*MMQ_TILE_Y_K + (2*k0) % WARP_SIZE, MMQ_TILE_Y_K);
+            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
 
 #pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int j = j0 + mma_C::get_j(l);
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
 
-            dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
-        }
+                dsB[l] = y_ds[j*MMQ_TILE_Y_K + k01/QI8_1];
+            }
 
 #pragma unroll
-        for (int n = 0; n < ntx; ++n) {
-            mma_C C;
-            C.mma_K8(A[n], B);
+            for (int n = 0; n < ntx; ++n) {
+                mma_C C;
+                C.mma_K8(A[n][k01/(QR4_1*QI4_1)], B);
 
 #pragma unroll
-            for (int l = 0; l < mma_C::ne; ++l) {
-                const half2 dmA_dsB = dmA[n][l/2]*dsB[l%2];
-                sum[(j0/mma_C::J + n)*mma_C::ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB);
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    const half2 dmA_dsB = dmA[n][l/2][k01/(QR4_1*QI4_1)]*dsB[l%2];
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB);
+                }
             }
         }
     }
@@ -531,8 +605,8 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         qs1     = __vsubss4(qs1, 0x10101010); // subtract 16
 
 #ifdef INT8_MMA_AVAILABLE
-        x_qs[i*MMQ_MMA_TILE_X_K_Q5_0 + kbx*(2*QI5_0) + kqsx + 0]     = qs0;
-        x_qs[i*MMQ_MMA_TILE_X_K_Q5_0 + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + 0]     = qs0;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_0) + kqsx + 0]     = qs0;
         x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
@@ -553,106 +627,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbxd;
 
 #ifdef INT8_MMA_AVAILABLE
-        x_df[i*MMQ_MMA_TILE_X_K_Q5_0       + kbxd] = bxi->d;
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0       + kbxd] = bxi->d;
 #else
         x_df[i*(WARP_SIZE/QI5_0) + i/QI5_0 + kbxd] = bxi->d;
 #endif // INT8_MMA_AVAILABLE
     }
 }
 
-template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q5_0_q8_1_dp4a(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
-
-    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_0, mmq_y);
-    const int   * x_qs = (const int   *) x;
-    const float * x_df = (const float *) x_qs + txs.qs;
-    const int   * y_qs = (const int   *) y + 4;
-    const float * y_df = (const float *) y;
-
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
-
-#pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
-
-            sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_q8_1_impl<float, QR5_0*VDR_Q5_0_Q8_1_MMQ>
-                (&x_qs[i*(2*WARP_SIZE + 1) + 2*k0], &y_qs[j*MMQ_TILE_Y_K + (2*k0) % WARP_SIZE],
-                 x_df[i*(WARP_SIZE/QI5_0) + i/QI5_0 + k0/QI5_0], y_df[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
-        }
-    }
-}
-
-template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
-#ifdef INT8_MMA_AVAILABLE
-
-    typedef mma_int_A_I16K8 mma_A;
-    typedef mma_int_B_J8K8  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
-
-    constexpr int granularity = mmq_get_granularity_device(mmq_x);
-    constexpr int rows_per_warp = 2 * granularity;
-    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
-
-    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
-
-    const int   * x_qs = (const int   *) x;
-    const float * x_df = (const float *) x_qs + WARP_SIZE*2;
-    const int   * y_qs = (const int   *) y + 4;
-    const float * y_df = (const float *) y;
-
-    mma_A A[ntx];
-    float dA[ntx][mma_C::ne/2];
-
-    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
-
-#pragma unroll
-    for (int n = 0; n < ntx; ++n) {
-        A[n].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q5_0 + QR5_1*k0, MMQ_MMA_TILE_X_K_Q5_0);
-
-#pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int i = i0 + mma_C::get_i(2*l) + n*mma_C::I;
-
-            dA[n][l] = x_df[i*MMQ_MMA_TILE_X_K_Q5_0 + k0/QI5_0];
-        }
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-        mma_B B;
-        float dB[mma_C::ne/2];
-
-        B.load(y_qs + j0*MMQ_TILE_Y_K + (2*k0) % WARP_SIZE, MMQ_TILE_Y_K);
-
-#pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int j = j0 + mma_C::get_j(l);
-
-            dB[l] = y_df[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
-        }
-
-#pragma unroll
-        for (int n = 0; n < ntx; ++n) {
-            mma_C C;
-            C.mma_K8(A[n], B);
-
-#pragma unroll
-            for (int l = 0; l < mma_C::ne; ++l) {
-                sum[(j0/mma_C::J + n)*mma_C::ne + l] += dA[n][l/2]*dB[l%2]*C.x[l];
-            }
-        }
-    }
-#else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
-    NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
-}
-
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
@@ -694,8 +675,8 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         qs1    |= (qh <<  9) & 0x10000000; // 19 -> 28
 
 #ifdef INT8_MMA_AVAILABLE
-        x_qs[i*MMQ_MMA_TILE_X_K_Q5_1 + kbx*(2*QI5_1) + kqsx + 0]     = qs0;
-        x_qs[i*MMQ_MMA_TILE_X_K_Q5_1 + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + 0]     = qs0;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_1) + kqsx + 0]     = qs0;
         x_qs[i*(2*WARP_SIZE + 1)     + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
@@ -716,41 +697,101 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbxd;
 
 #ifdef INT8_MMA_AVAILABLE
-        x_dm[i*MMQ_MMA_TILE_X_K_Q5_1       + kbxd] = bxi->dm;
+        x_dm[i*MMQ_MMA_TILE_X_K_Q8_1       + kbxd] = bxi->dm;
 #else
         x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + kbxd] = bxi->dm;
 #endif // INT8_MMA_AVAILABLE
     }
 }
 
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_tile + 2*WARP_SIZE);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kbx  = threadIdx.x / QI8_0;
+    const int kqsx = threadIdx.x % QI8_0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbx;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 0         + threadIdx.x] = get_int_b2(bxi[0].qs,               kqsx);
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + WARP_SIZE + threadIdx.x] = get_int_b2(bxi[WARP_SIZE/QI8_0].qs, kqsx);
+#else
+        x_qs[i*(2*WARP_SIZE + 1)     + 0         + threadIdx.x] = get_int_b2(bxi[0].qs,               kqsx);
+        x_qs[i*(2*WARP_SIZE + 1)     + WARP_SIZE + threadIdx.x] = get_int_b2(bxi[WARP_SIZE/QI8_0].qs, kqsx);
+#endif // INT8_MMA_AVAILABLE
+    }
+
+    const int blocks_per_tile_x_row = 2*WARP_SIZE / QI8_0;
+    const int kbxd = threadIdx.x % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0/2) {
+        int i = i0 + threadIdx.y * (QI8_0/2) + threadIdx.x / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbxd;
+
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0             + kbxd] = bxi->d;
+#else
+        x_df[i*(2*WARP_SIZE/QI8_0) + i/(QI8_0/2) + kbxd] = bxi->d;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q5_1_q8_1_dp4a(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
-    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_1, mmq_y);
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y);
     const int   * x_qs = (const int   *) x;
-    const half2 * x_dm = (const half2 *) x_qs + txs.qs;
+    const float * x_df = (const float *) x_qs + txs.qs;
     const int   * y_qs = (const int   *) y + 4;
-    const half2 * y_ds = (const half2 *) y;
+    const float * y_df = (const float *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += VDR_Q8_0_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
 
 #pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
 
 #pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
 
-            sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
-                (&x_qs[i*(2*WARP_SIZE + 1) + 2*k0], &y_qs[j*MMQ_TILE_Y_K + (2*k0) % WARP_SIZE],
-                 x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + k0/QI5_1], y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]);
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_q8_1_impl<float, VDR_Q8_0_Q8_1_MMQ>
+                    (&x_qs[i*(2*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k0 % WARP_SIZE],
+                     x_df[i*(2*WARP_SIZE/QI8_0) + i/(QI8_0/2) + k0/QI8_0], y_df[j*MMQ_TILE_Y_K + (k0/QI8_1) % (WARP_SIZE/QI8_1)]);
+            }
         }
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 #ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K8 mma_A;
@@ -764,140 +805,106 @@ static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mma(
     y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
 
     const int   * x_qs = (const int   *) x;
-    const half2 * x_dm = (const half2 *) x_qs + 2*WARP_SIZE;
+    const float * x_df = (const float *) x_qs + 2*WARP_SIZE;
     const int   * y_qs = (const int   *) y + 4;
-    const half2 * y_ds = (const half2 *) y;
+    const float * y_df = (const float *) y;
 
-    mma_A A[ntx];
-    half2 dmA[ntx][mma_C::ne/2];
+    mma_A A[ntx][WARP_SIZE/QI8_0];
+    float dA[ntx][mma_C::ne/2][WARP_SIZE/QI8_0];
 
-    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
+    const int i0 = (threadIdx.y/ntx)*rows_per_warp;
 
 #pragma unroll
     for (int n = 0; n < ntx; ++n) {
-        A[n].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q5_1 + QR5_1*k0, MMQ_MMA_TILE_X_K_Q5_1);
-
 #pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int i = i0 + mma_C::get_i(2*l) + n*mma_C::I;
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
+            const int k0 = k00 + k01;
 
-            dmA[n][l] = x_dm[i*MMQ_MMA_TILE_X_K_Q5_1 + k0/QI5_1];
+            A[n][k01/QI8_0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_0 + k0, MMQ_MMA_TILE_X_K_Q8_0);
         }
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-        mma_B B;
-        half2 dsB[mma_C::ne/2];
-
-        B.load(y_qs + j0*MMQ_TILE_Y_K + (2*k0) % WARP_SIZE, MMQ_TILE_Y_K);
 
 #pragma unroll
         for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int j = j0 + mma_C::get_j(l);
-
-            dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)];
-        }
+            const int i = i0 + n*mma_A::I + mma_C::get_i(2*l);
 
 #pragma unroll
-        for (int n = 0; n < ntx; ++n) {
-            mma_C C;
-            C.mma_K8(A[n], B);
+            for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
+                const int k0 = k00 + k01;
 
-#pragma unroll
-            for (int l = 0; l < mma_C::ne; ++l) {
-                const half2 dmA_dsB = dmA[n][l/2]*dsB[l%2];
-                sum[(j0/mma_C::J + n)*mma_C::ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB);
+                dA[n][l][k01/QI8_0] = x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + k0/QI8_0];
             }
         }
     }
-#else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
-    NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
-}
 
-template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
-    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
+            const int k0 = k00 + k01;
 
-#ifdef INT8_MMA_AVAILABLE
-    int   * x_qs = (int   *)  x_tile;
-    float * x_df = (float *) (x_tile + WARP_SIZE);
-#else
-    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y);
-    int   * x_qs = (int   *)  x_tile;
-    float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+            mma_B B;
+            float dB[mma_C::ne/2];
 
-    const int kbx  = threadIdx.x / QI8_0;
-    const int kqsx = threadIdx.x % QI8_0;
+            B.load(y_qs + j0*MMQ_TILE_Y_K + k0 % WARP_SIZE, MMQ_TILE_Y_K);
 
 #pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + threadIdx.y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbx;
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
 
-#ifdef INT8_MMA_AVAILABLE
-        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + threadIdx.x] = get_int_b2(bxi->qs, kqsx);
-#else
-        x_qs[i*(WARP_SIZE + 1)       + threadIdx.x] = get_int_b2(bxi->qs, kqsx);
-#endif // INT8_MMA_AVAILABLE
-    }
+                dB[l] = y_df[j*MMQ_TILE_Y_K + (k0/QI8_1) % (WARP_SIZE/QI8_1)];
+            }
 
-    const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
-    const int kbxd = threadIdx.x % blocks_per_tile_x_row;
+#pragma unroll
+            for (int n = 0; n < ntx; ++n) {
+                mma_C C;
+                C.mma_K8(A[n][k01/QI8_0], B);
 
 #pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
-        int i = i0 + threadIdx.y * QI8_0 + threadIdx.x / blocks_per_tile_x_row;
-
-        if (need_check) {
-            i = min(i, i_max);
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += C.x[l]*dA[n][l/2][k01/QI8_0]*dB[l%2];
+                }
+            }
         }
-
-        const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbxd;
-
-#ifdef INT8_MMA_AVAILABLE
-        x_df[i*MMQ_MMA_TILE_X_K_Q8_0         + kbxd] = bxi->d;
+    }
 #else
-        x_df[i*(WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
+    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
+    NO_DEVICE_CODE;
 #endif // INT8_MMA_AVAILABLE
-    }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+static __device__ __forceinline__ void vec_dot_q8_1_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
-    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y);
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_1, mmq_y);
     const int   * x_qs = (const int   *) x;
-    const float * x_df = (const float *) x_qs + txs.qs;
+    const half2 * x_dm = (const half2 *) x_qs + txs.qs;
     const int   * y_qs = (const int   *) y + 4;
-    const float * y_df = (const float *) y;
+    const half2 * y_ds = (const half2 *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += VDR_Q8_0_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
 
 #pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
 
 #pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
 
-            sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_q8_1_impl<float, VDR_Q8_0_Q8_1_MMQ>
-                (&x_qs[i*(WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k0], x_df[i*(WARP_SIZE/QI8_0) + i/QI8_0 + k0/QI8_0],
-                y_df[j*MMQ_TILE_Y_K + k0/QI8_1]);
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
+                    (&x_qs[i*(2*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01],
+                    x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + k0/QI8_1], y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
         }
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 #ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K8 mma_A;
@@ -911,49 +918,65 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
     y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
 
     const int   * x_qs = (const int   *) x;
-    const float * x_df = (const float *) x_qs + WARP_SIZE;
+    const half2 * x_dm = (const half2 *) x_qs + 2*WARP_SIZE;
     const int   * y_qs = (const int   *) y + 4;
-    const float * y_df = (const float *) y;
+    const half2 * y_dm = (const half2 *) y;
 
-    mma_A A[ntx];
-    float dA[ntx][mma_C::ne/2];
+    mma_A   A[ntx][WARP_SIZE/QI8_1];
+    half2 dmA[ntx][mma_C::ne/2][WARP_SIZE/QI8_1];
 
     const int i0 = (threadIdx.y/ntx)*rows_per_warp;
 
 #pragma unroll
     for (int n = 0; n < ntx; ++n) {
-        A[n].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_0 + k0, MMQ_MMA_TILE_X_K_Q8_0);
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
+            const int k0 = k00 + k01;
+
+            A[n][k01/QI8_1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_1 + k0, MMQ_MMA_TILE_X_K_Q8_1);
+        }
 
 #pragma unroll
         for (int l = 0; l < mma_C::ne/2; ++l) {
             const int i = i0 + n*mma_A::I + mma_C::get_i(2*l);
 
-            dA[n][l] = x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + k0/QI8_0];
+#pragma unroll
+            for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
+                const int k0 = k00 + k01;
+
+                dmA[n][l][k01/QI8_1] = x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + k0/QI8_1];
+            }
         }
     }
 
 #pragma unroll
     for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-        mma_B B;
-        float dB[mma_C::ne/2];
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
+            const int k0 = k00 + k01;
+
+            mma_B   B;
+            half2 dsB[mma_C::ne/2];
 
-        B.load(y_qs + j0*MMQ_TILE_Y_K + k0, MMQ_TILE_Y_K);
+            B.load(y_qs + j0*MMQ_TILE_Y_K + k0 % WARP_SIZE, MMQ_TILE_Y_K);
 
 #pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int j = j0 + mma_C::get_j(l);
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
 
-            dB[l] = y_df[j*MMQ_TILE_Y_K + k0/QI8_1];
-        }
+                dsB[l] = y_dm[j*MMQ_TILE_Y_K + (k0/QI8_1) % (WARP_SIZE/QI8_1)];
+            }
 
 #pragma unroll
-        for (int n = 0; n < ntx; ++n) {
-            mma_C C;
-            C.mma_K8(A[n], B);
+            for (int n = 0; n < ntx; ++n) {
+                mma_C C;
+                C.mma_K8(A[n][k01/QI8_1], B);
 
 #pragma unroll
-            for (int l = 0; l < mma_C::ne; ++l) {
-                sum[(j0/mma_C::J + n)*mma_C::ne + l] += C.x[l]*dA[n][l/2]*dB[l%2];
+                for (int l = 0; l < mma_C::ne; ++l) {
+                const half2 dmA_dsB = dmA[n][l/2][k01/QI8_1]*dsB[l%2];
+                sum[(j0/mma_C::J + n)*mma_C::ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB);
+                }
             }
         }
     }
@@ -968,44 +991,37 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
 #ifdef INT8_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
-    half2 * x_dm = (half2 *) (x_qs + WARP_SIZE);
+    half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q2_K, mmq_y);
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + txs.qs);
 #endif // INT8_MMA_AVAILABLE
 
-    const int kbx  = threadIdx.x / QI2_K;
     const int kqsx = threadIdx.x % QI2_K;
 
 #pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + threadIdx.y;
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/QI2_K) {
+        int i = i0 + threadIdx.y*(WARP_SIZE/QI2_K) + threadIdx.x/QI2_K;
 
         if (need_check) {
             i = min(i, i_max);
         }
 
-        const block_q2_K * bxi = (const block_q2_K *) x + kbx0 + i*stride + kbx;
+        const block_q2_K * bxi = (const block_q2_K *) x + kbx0 + i*stride;
 
         const int x_ql_0 = get_int_b2(bxi->qs, kqsx);
 
 #pragma unroll
         for (int l = 0; l < QR2_K; ++l) {
-            const int k = kbx*QI2_K + (kqsx/8)*8 + l*2 + (kqsx % 8)/4;
+            const int k = (kqsx/8)*32 + l*8 + kqsx % 8;
 
-            int x_qs_k = ((x_ql_0 >> (2*l)) & 0x03030303) << (2*(kqsx % 4));
-            x_qs_k |= __shfl_xor_sync(0xFFFFFFFF, x_qs_k, 1, WARP_SIZE);
-            x_qs_k |= __shfl_xor_sync(0xFFFFFFFF, x_qs_k, 2, WARP_SIZE);
-
-            if (kqsx % QR2_K != 0) {
-                continue;
-            }
+            const int x_qs_k = (x_ql_0 >> (2*l)) & 0x03030303;
 
 #ifdef INT8_MMA_AVAILABLE
             x_qs[i*MMQ_MMA_TILE_X_K_Q2_K + k] = x_qs_k;
 #else
-            x_qs[i*(WARP_SIZE + 1)       + k] = x_qs_k;
+            x_qs[i*(2*WARP_SIZE + 1)     + k] = x_qs_k;
 #endif // INT8_MMA_AVAILABLE
         }
 
@@ -1018,44 +1034,68 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 #endif // FAST_FP16_AVAILABLE
 
 #ifdef INT8_MMA_AVAILABLE
-        x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + threadIdx.x] = x_dm_ik;
+        x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + kqsx] = x_dm_ik;
 #else
-        x_dm[i*(WARP_SIZE + 1)       + threadIdx.x] = x_dm_ik;
+        x_dm[i*(WARP_SIZE + 1)       + kqsx] = x_dm_ik;
 #endif // INT8_MMA_AVAILABLE
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q2_K_q8_1_dp4a(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q2_K, mmq_y);
     const int   * x_qs = (const int   *) x;
     const half2 * x_dm = (const half2 *) x_qs + txs.qs;
     const int   * y_qs = (const int   *) y + 4;
-    const float * y_df = (const float *) y;
+    const half2 * y_ds = (const half2 *) y;
 
+    float2 y_df[mmq_x/nwarps];
 #pragma unroll
     for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
         const int j = j0 + threadIdx.y;
 
+        y_df[j0/nwarps] = __half22float2(y_ds[j*MMQ_TILE_Y_K]);
+    }
+
 #pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR2_K*VDR_Q2_K_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
 
-            sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q2_K_q8_1_impl_mmq(
-                &x_qs[i*(WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + (QR2_K*k0) % WARP_SIZE],
-                &x_dm[i*(WARP_SIZE + 1) + k0], y_df[j*MMQ_TILE_Y_K + ((QR2_K*k0) % WARP_SIZE)/QI8_1]);
+                if (k01 < WARP_SIZE/2) {
+                    constexpr int ns = 2;
+                    sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q2_K_q8_1_impl_mmq<ns>(
+                        &x_qs[i*(2*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01],
+                        &x_dm[i*(WARP_SIZE + 1) + k0/4], k01 < WARP_SIZE/2 ? y_df[j0/nwarps].x : y_df[j0/nwarps].y,
+                        &y_ds[j*MMQ_TILE_Y_K + (1 + k01/QI8_1)]);
+                } else {
+                    constexpr int ns = 1;
+                    sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q2_K_q8_1_impl_mmq<ns>(
+                        &x_qs[i*(2*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01],
+                        &x_dm[i*(WARP_SIZE + 1) + k0/4], k01 < WARP_SIZE/2 ? y_df[j0/nwarps].x : y_df[j0/nwarps].y,
+                        &y_ds[j*MMQ_TILE_Y_K + (1 + k01/QI8_1)]);
+                }
+            }
         }
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 #ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K4 mma_A;
+    typedef mma_int_A_I16K8 mma_A_K8;
     typedef mma_int_B_J8K4  mma_B;
     typedef mma_int_C_I16J8 mma_C;
 
@@ -1066,74 +1106,107 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
     y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
 
     const int   * x_qs = (const int   *) x;
-    const half2 * x_dm = (const half2 *) x_qs + WARP_SIZE;
+    const half2 * x_dm = (const half2 *) x_qs + WARP_SIZE*2;
     const int   * y_qs = (const int   *) y + 4;
-    const float * y_df = (const float *) y;
+    const half2 * y_ds = (const half2 *) y;
 
     const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
 
-    mma_A   A[ntx][2];
-    float  dA[ntx][mma_C::ne/2][2];
-    float  mA[ntx][mma_C::ne/2][2];
+    mma_A   A[ntx][8];
+    float  dA[ntx][mma_C::ne/2][8];
+    float  mA[ntx][mma_C::ne/2][8];
 
 #pragma unroll
     for (int n = 0; n < ntx; ++n) {
 #pragma unroll
-        for (int l = 0; l < mma_A::ne; ++l) {
-            const int i = i0 + n*mma_A::I + mma_A::get_i(l);
-            const int shift = 2*mma_A::get_k(l);
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
+            const int k0 = k00 + k01;
 
-            A[n][0].x[l] = (x_qs[i*MMQ_MMA_TILE_X_K_Q2_K + k0 + 0] >> shift) & 0x03030303;
-            A[n][1].x[l] = (x_qs[i*MMQ_MMA_TILE_X_K_Q2_K + k0 + 1] >> shift) & 0x03030303;
+            ((mma_A_K8 *) A[n])[k01/QI8_1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q2_K + k0, MMQ_MMA_TILE_X_K_Q2_K);
         }
+    }
 
+#pragma unroll
+    for (int n = 0; n < ntx; ++n) {
 #pragma unroll
         for (int l = 0; l < mma_C::ne/2; ++l) {
             const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
 
 #pragma unroll
-            for (int kdm = 0; kdm < 2; ++kdm) {
-                const float2 dm = __half22float2(x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + k0 + kdm]);
+            for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1/2) {
+                const int k0 = k00 + k01;
 
-                dA[n][l][kdm] = dm.x;
-                mA[n][l][kdm] = dm.y;
+                const float2 dm = __half22float2(x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + k0/(QI8_1/2)]);
+
+                dA[n][l][k01/(QI8_1/2)] = dm.x;
+                mA[n][l][k01/(QI8_1/2)] = dm.y;
             }
         }
     }
 
 #pragma unroll
     for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-        mma_B B[2];
-        float dB[mma_C::ne/2];
-
-        B[0].load(y_qs + j0*MMQ_TILE_Y_K + (QR2_K*k0 + 0)        % WARP_SIZE, MMQ_TILE_Y_K);
-        B[1].load(y_qs + j0*MMQ_TILE_Y_K + (QR2_K*k0 + mma_B::K) % WARP_SIZE, MMQ_TILE_Y_K);
+        float2 dB[mma_C::ne/2];
 
 #pragma unroll
         for (int l = 0; l < mma_C::ne/2; ++l) {
             const int j = j0 + mma_C::get_j(l);
 
-            dB[l] = y_df[j*MMQ_TILE_Y_K + ((4*k0)/QI8_1) % (WARP_SIZE/QI8_1)];
+            dB[l] = __half22float2(y_ds[j*MMQ_TILE_Y_K]);
         }
 
-        mma_C Cm[2];
-        mma_A A1;
-        A1.x[0] = 0x01010101;
-        A1.x[1] = 0x01010101;
-        Cm[0].mma_K4(A1, B[0]);
-        Cm[1].mma_K4(A1, B[1]);
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
+            mma_B B[2];
+
+            B[0].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0),        MMQ_TILE_Y_K);
+            B[1].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
+
+            mma_C Cm[2];
+            if (k01 >= WARP_SIZE * 3/4) {
+                mma_A A1;
+                A1.x[0] = 0x01010101;
+                A1.x[1] = 0x01010101;
+                Cm[0].mma_K4(A1, B[0]);
+                Cm[1].mma_K4(A1, B[1]);
+            }
 
 #pragma unroll
-    for (int n = 0; n < ntx; ++n) {
-            mma_C Cd[2];
+            for (int n = 0; n < ntx; ++n) {
+                mma_C Cd[2];
 
-            Cd[0].mma_K4(A[n][0], B[0]);
-            Cd[1].mma_K4(A[n][1], B[1]);
+                Cd[0].mma_K4(A[n][k01/4 + 0], B[0]);
+                Cd[1].mma_K4(A[n][k01/4 + 1], B[1]);
 
 #pragma unroll
-            for (int l = 0; l < mma_C::ne; ++l) {
-                sum[(j0/mma_C::J + n)*mma_C::ne + l] += (
-                    Cd[0].x[l]*dA[n][l/2][0] + Cd[1].x[l]*dA[n][l/2][1] - Cm[0].x[l]*mA[n][l/2][0] - Cm[1].x[l]*mA[n][l/2][1])*dB[l%2];
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    float tmp = Cd[0].x[l]*dA[n][l/2][k01/4 + 0] + Cd[1].x[l]*dA[n][l/2][k01/4 + 1];
+                    if (k01 >= WARP_SIZE * 3/4) {
+                        tmp -= Cm[0].x[l]*mA[n][l/2][k01/4 + 0] + Cm[1].x[l]*mA[n][l/2][k01/4 + 1];
+                    }
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += tmp*(k01 < WARP_SIZE/2 ? dB[l%2].x : dB[l%2].y);
+                }
+            }
+        }
+
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE * 3/4; k01 += QI8_1) {
+            float2 sB[mma_C::ne/2];
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                sB[l] = __half22float2(y_ds[j*MMQ_TILE_Y_K + (1 + k01/QI8_1)]);
+            }
+
+#pragma unroll
+            for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] -= mA[n][l/2][k01/4 + 0]*sB[l%2].x;
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] -= mA[n][l/2][k01/4 + 1]*sB[l%2].y;
+                }
             }
         }
     }
@@ -1149,7 +1222,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 #ifdef INT8_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
-    int   * x_sc = (int   *) (x_df + WARP_SIZE/QI3_K);
+    int   * x_sc = (int   *) (x_df + 1);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q3_K, mmq_y);
     int   * x_qs = (int   *)  x_tile;
@@ -1157,75 +1230,66 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
     int   * x_sc = (int   *) (x_df + txs.dm);
 #endif // INT8_MMA_AVAILABLE
 
-    const int kbx  = threadIdx.x / QI3_K;
     const int kqsx = threadIdx.x % QI3_K;
 
 #pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
-        int i = i0 + threadIdx.y;
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/QI3_K) {
+        int i = i0 + threadIdx.y * (WARP_SIZE/QI3_K) + threadIdx.x / QI3_K;
 
         if (need_check) {
             i = min(i, i_max);
         }
 
-        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + kbx;
+        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride;
 
         const int x_ql_0 = get_int_b2(bxi->qs,    kqsx);
         const int x_qh_0 = get_int_b2(bxi->hmask, kqsx % (QI3_K/2)) >> (4 * (kqsx / (QI3_K/2)));
 
 #pragma unroll
         for (int l = 0; l < QR3_K; ++l) {
-            const int k = kbx*(QR3_K*QI3_K) + (kqsx/8)*32 + l*8 + kqsx % 8;
+            const int k = (kqsx/8)*32 + l*8 + kqsx % 8;
 
             const int x_ql_k =  (x_ql_0 >> (2*l))       & 0x03030303;
             const int x_qh_k = ((x_qh_0 >>    l)  << 2) & 0x04040404;
 
-            int x_qs_k = (x_ql_k | x_qh_k) << (4*(k%2));
-            x_qs_k |= __shfl_xor_sync(0xFFFFFFFF, x_qs_k, 1, WARP_SIZE);
-
-            if (kqsx % 2 != 0) {
-                continue;
-            }
+            const int x_qs_k = __vsubss4(x_ql_k | x_qh_k, 0x04040404);
 
 #ifdef INT8_MMA_AVAILABLE
-            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + k/2] = x_qs_k;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + k] = x_qs_k;
 #else
-            x_qs[i*(2*WARP_SIZE + 1)     + k/2] = x_qs_k;
+            x_qs[i*(2*WARP_SIZE + 1)     + k] = x_qs_k;
 #endif // INT8_MMA_AVAILABLE
         }
     }
 
-    const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
-    const int kbxd = threadIdx.x % blocks_per_tile_x_row;
-
 #pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
-        int i = (i0 + threadIdx.y * QI3_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y;
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*WARP_SIZE) {
+        int i = (i0 + threadIdx.y*WARP_SIZE + threadIdx.x) % mmq_y;
 
         if (need_check) {
             i = min(i, i_max);
         }
 
-        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + kbxd;
+        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride;
 
 #ifdef INT8_MMA_AVAILABLE
-        x_df[i*MMQ_MMA_TILE_X_K_Q3_K       + kbxd] = bxi->d;
+        x_df[i*MMQ_MMA_TILE_X_K_Q3_K] = bxi->d;
 #else
-        x_df[i*(WARP_SIZE/QI3_K) + i/QI3_K + kbxd] = bxi->d;
+        x_df[i]                       = bxi->d;
 #endif // INT8_MMA_AVAILABLE
     }
 
 #pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
-        int i = i0 + threadIdx.y * 4 + threadIdx.x / (WARP_SIZE/4);
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*8) {
+        int i = i0 + threadIdx.y*8 + threadIdx.x/(WARP_SIZE/8);
 
         if (need_check) {
             i = min(i, i_max);
         }
 
-        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/4)) / (QI3_K/4);
+        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride;
 
-        const int ksc = threadIdx.x % (QI3_K/4);
+        const int ksc = threadIdx.x % (WARP_SIZE/8);
 
         const int ksc_low = ksc % (QI3_K/8);
         const int shift_low = 4 * (ksc / (QI3_K/8));
@@ -1238,16 +1302,16 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
 
 #ifdef INT8_MMA_AVAILABLE
-        x_sc[i*MMQ_MMA_TILE_X_K_Q3_K + threadIdx.x % (WARP_SIZE/4)] = sc;
+        x_sc[i*MMQ_MMA_TILE_X_K_Q3_K + threadIdx.x % (WARP_SIZE/8)] = sc;
 #else
-        x_sc[i*(WARP_SIZE/4) + i/4   + threadIdx.x % (WARP_SIZE/4)] = sc;
+        x_sc[i*(WARP_SIZE/8) + i/8   + threadIdx.x % (WARP_SIZE/8)] = sc;
 #endif // INT8_MMA_AVAILABLE
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q3_K_q8_1_dp4a(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q3_K, mmq_y);
     const int   * x_qs = (const int   *) x;
@@ -1256,32 +1320,35 @@ static __device__ __forceinline__ void vec_dot_q3_K_q8_1_dp4a(
     const int   * y_qs = (const int   *) y + 4;
     const float * y_df = (const float *) y;
 
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR3_K*VDR_Q3_K_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
 
 #pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
 
-            const int kbx  = k0 / QI3_K;
-            const int ky  = (k0 % QI3_K) * QR3_K;
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
 
-            const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
+                const int8_t * scales = ((const int8_t *) (x_sc + i*(WARP_SIZE/8) + i/8)) + k0/4;
 
-            sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q3_K_q8_1_impl_mmq(
-                &x_qs[i*(2*WARP_SIZE + 1) + 2*k0], &y_qs[j*MMQ_TILE_Y_K + (k0*QR3_K) % WARP_SIZE], scales,
-                x_df[i*(WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[j*MMQ_TILE_Y_K + ((k0*QR3_K) % WARP_SIZE)/QI8_1]);
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q3_K_q8_1_impl_mmq(
+                    &x_qs[i*(2*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01], scales,
+                    x_df[i], y_df[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
         }
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q3_K_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 #ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K4 mma_A;
+    typedef mma_int_A_I16K8 mma_A_K8;
     typedef mma_int_B_J8K4  mma_B;
     typedef mma_int_C_I16J8 mma_C;
 
@@ -1293,73 +1360,74 @@ static __device__ __forceinline__ void vec_dot_q3_K_q8_1_mma(
 
     const int   * x_qs = (const int   *) x;
     const float * x_df = (const float *) x_qs + WARP_SIZE*2;
-    const int   * x_sc = (const int   *) x_df + WARP_SIZE/QI3_K;
+    const int   * x_sc = (const int   *) x_df + 1;
     const int   * y_qs = (const int   *) y + 4;
     const float * y_df = (const float *) y;
 
     const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
 
-    mma_A   A[ntx][2];
-    int   scA[ntx][mma_C::ne/2][2];
+    mma_A   A[ntx][8];
+    int   scA[ntx][mma_C::ne/2][8];
     float  dA[ntx][mma_C::ne/2];
 
 #pragma unroll
     for (int n = 0; n < ntx; ++n) {
 #pragma unroll
-        for (int l = 0; l < mma_A::ne; ++l) {
-            const int i = i0 + n*mma_A::I + mma_A::get_i(l);
-            const int k = QR3_K*k0 + mma_A::get_k(l);
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
+            const int k0 = k00 + k01;
 
-            A[n][0].x[l] = (x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + k/2 + 0]          >> (4*(k%2))) & 0x0F0F0F0F;
-            A[n][1].x[l] = (x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + k/2 + mma_A::K/2] >> (4*(k%2))) & 0x0F0F0F0F;
-            A[n][0].x[l] = __vsubss4(A[n][0].x[l], 0x04040404);
-            A[n][1].x[l] = __vsubss4(A[n][1].x[l], 0x04040404);
+            ((mma_A_K8 *) A[n])[k01/8].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q3_K + k0, MMQ_MMA_TILE_X_K_Q3_K);
         }
 
 #pragma unroll
         for (int l = 0; l < mma_C::ne/2; ++l) {
             const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
 
-            const int kbx  = k0 / QI3_K;
-            const int ky  = (k0 % QI3_K) * QR3_K;
-            const int8_t * sc = ((const int8_t *) (x_sc + i*MMQ_MMA_TILE_X_K_Q3_K + kbx*4)) + ky/4;
+#pragma unroll
+            for (int k01 = 0; k01 < WARP_SIZE; k01 += 16) {
+                const int k0 = k00 + k01;
 
-            scA[n][l][0] = sc[0];
-            scA[n][l][1] = sc[1];
-        }
+                const int sc_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q3_K + k0/16];
+                const int8_t * sc = (const int8_t *) &sc_packed;
 
 #pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+                for (int ksc = 0; ksc < sizeof(int); ++ksc) {
+                    scA[n][l][k01/4 + ksc] = sc[ksc];
+                }
+            }
 
-            dA[n][l] = x_df[i*MMQ_MMA_TILE_X_K_Q3_K + k0/QI3_K];
+            dA[n][l] = x_df[i*MMQ_MMA_TILE_X_K_Q3_K];
         }
     }
 
 #pragma unroll
     for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-        mma_B B[2];
-        float dB[mma_C::ne/2];
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR3_K*VDR_Q3_K_Q8_1_MMQ) {
+            mma_B B[2];
+            float dB[mma_C::ne/2];
 
-        B[0].load(y_qs + j0*MMQ_TILE_Y_K + (QR3_K*k0 + 0)        % WARP_SIZE, MMQ_TILE_Y_K);
-        B[1].load(y_qs + j0*MMQ_TILE_Y_K + (QR3_K*k0 + mma_B::K) % WARP_SIZE, MMQ_TILE_Y_K);
+            B[0].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0),        MMQ_TILE_Y_K);
+            B[1].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
 
 #pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int j = j0 + mma_C::get_j(l);
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
 
-            dB[l] = y_df[j*MMQ_TILE_Y_K + ((4*k0)/QI8_1) % (WARP_SIZE/QI8_1)];
-        }
+                dB[l] = y_df[j*MMQ_TILE_Y_K + k01/QI8_1];
+            }
 
 #pragma unroll
-        for (int n = 0; n < ntx; ++n) {
-            mma_C C[2];
-            C[0].mma_K4(A[n][0], B[0]);
-            C[1].mma_K4(A[n][1], B[1]);
+            for (int n = 0; n < ntx; ++n) {
+                mma_C C[2];
+                C[0].mma_K4(A[n][k01/4 + 0], B[0]);
+                C[1].mma_K4(A[n][k01/4 + 1], B[1]);
 
 #pragma unroll
-            for (int l = 0; l < mma_C::ne; ++l) {
-                sum[(j0/mma_C::J + n)*mma_C::ne + l] += (C[0].x[l]*scA[n][l/2][0] + C[1].x[l]*scA[n][l/2][1])*dA[n][l/2]*dB[l%2];
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += dA[n][l/2]*dB[l%2]*
+                        (C[0].x[l]*scA[n][l/2][k01/4 + 0] + C[1].x[l]*scA[n][l/2][k01/4 + 1]);
+                }
             }
         }
     }
@@ -1451,7 +1519,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_K, mmq_y);
     const int   * x_qs = (const int   *) x;
@@ -1460,26 +1528,31 @@ static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a(
     const int   * y_qs = (const int   *) y + 4;
     const half2 * y_ds = (const half2 *) y;
 
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_K*VDR_Q4_K_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
 #pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
 
 #pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
 
-            const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2*((k0 % 16) / 8);
+                const uint8_t * sc = (const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/32] + 2*(k01/16);
 
-            sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_K_q8_1_impl_mmq(
-                &x_qs[i*(WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + (QR4_K*k0) % WARP_SIZE], sc, sc+8,
-                x_dm[i*(WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[j*MMQ_TILE_Y_K + ((QR4_K*k0) % WARP_SIZE)/QI8_1]);
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_K_q8_1_impl_mmq(
+                    &x_qs[i*(WARP_SIZE + 1) + k0/2], &y_qs[j*MMQ_TILE_Y_K + k01], sc, sc+8,
+                    x_dm[i*(WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
         }
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 #ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K8 mma_A;
@@ -1500,35 +1573,40 @@ static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mma(
 
     const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
 
-    mma_A   A[ntx][2];
-    int   scA[ntx][mma_C::ne/2][2];
-    int    mA[ntx][mma_C::ne/2][2];
+    mma_A   A[ntx][4];
+    int   scA[ntx][mma_C::ne/2][4];
+    int    mA[ntx][mma_C::ne/2][4];
     half2 dmA[ntx][mma_C::ne/2];
 
 #pragma unroll
     for (int n = 0; n < ntx; ++n) {
 #pragma unroll
-        for (int kvdr = 0; kvdr < VDR_Q4_K_Q8_1_MMQ; kvdr += 8) {
-            A[n][kvdr/4 + 0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q4_K + k0, MMQ_MMA_TILE_X_K_Q4_K);
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 16) {
+            const int k0 = k00 + k01;
+
+            A[n][k01/8 + 0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q4_K + k0/QR4_K, MMQ_MMA_TILE_X_K_Q4_K);
 
 #pragma unroll
             for (int l = 0; l < mma_A::ne; ++l) {
-                A[n][kvdr/4 + 1].x[l]  = (A[n][kvdr/4 + 0].x[l] >> 4) & 0x0F0F0F0F;
-                A[n][kvdr/4 + 0].x[l] &= 0x0F0F0F0F;
+                A[n][k01/8 + 1].x[l]  = (A[n][k01/8 + 0].x[l] >> 4) & 0x0F0F0F0F;
+                A[n][k01/8 + 0].x[l] &= 0x0F0F0F0F;
             }
         }
 
 #pragma unroll
-        for (int kvdr = 0; kvdr < VDR_Q4_K_Q8_1_MMQ; kvdr += 4) {
-#pragma unroll
-            for (int l = 0; l < mma_C::ne/2; ++l) {
-                const int i = i0 + n*mma_A::I + mma_C::get_i(2*l);
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+
+            const int sc_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q4_K + (k00/32 + 0)];
+            const int  m_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q4_K + (k00/32 + 2)];
 
-                const uint8_t * sc = ((const uint8_t *) &x_sc[i*MMQ_MMA_TILE_X_K_Q4_K + k0/16]) + 2 * ((k0 % 16) / 8);
-                const uint8_t *  m = sc + 8;
+            const uint8_t * sc = (const uint8_t *) &sc_packed;
+            const uint8_t *  m = (const uint8_t *)  &m_packed;
 
-                scA[n][l][kvdr/4] = sc[kvdr/4];
-                mA[n][l][kvdr/4]  =  m[kvdr/4];
+#pragma unroll
+            for (int ksc = 0; ksc < sizeof(int); ++ksc) {
+                scA[n][l][ksc] = sc[ksc];
+                mA[n][l][ksc]  =  m[ksc];
             }
         }
 
@@ -1536,7 +1614,7 @@ static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mma(
         for (int l = 0; l < mma_C::ne/2; ++l) {
             const int i = i0 + n*mma_A::I + mma_C::get_i(2*l);
 
-            dmA[n][l] = x_dm[i*MMQ_MMA_TILE_X_K_Q4_K + k0/QI4_K];
+            dmA[n][l] = x_dm[i*MMQ_MMA_TILE_X_K_Q4_K];
         }
     }
 
@@ -1546,28 +1624,28 @@ static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mma(
         float tmpm[ntx][mma_C::ne] = {{0.0f}};
 
 #pragma unroll
-        for (int kvdr = 0; kvdr < VDR_Q4_K_Q8_1_MMQ; kvdr += 4) {
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
             mma_B   B;
             half2 dsB[mma_C::ne/2];
 
-            B.load(y_qs + j0*MMQ_TILE_Y_K + (2*k0 + 2*kvdr) % WARP_SIZE, MMQ_TILE_Y_K);
+            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
 
 #pragma unroll
             for (int l = 0; l < mma_C::ne/2; ++l) {
                 const int j = j0 + mma_C::get_j(l);
 
-                dsB[l] = y_ds[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
+                dsB[l] = y_ds[j*MMQ_TILE_Y_K + k01/QI8_1];
             }
 
 #pragma unroll
             for (int n = 0; n < ntx; ++n) {
                 mma_C C;
-                C.mma_K8(A[n][kvdr/4], B);
+                C.mma_K8(A[n][k01/8], B);
 
 #pragma unroll
                 for (int l = 0; l < mma_C::ne; ++l) {
-                    tmpd[n][l] += (C.x[l]*scA[n][l/2][kvdr/4]) *  __low2float(dsB[l%2]);
-                    tmpm[n][l] += mA[n][l/2][kvdr/4]           * __high2float(dsB[l%2]);
+                    tmpd[n][l] += (C.x[l]*scA[n][l/2][k01/8]) *  __low2float(dsB[l%2]);
+                    tmpm[n][l] += mA[n][l/2][k01/8]           * __high2float(dsB[l%2]);
                 }
             }
         }
@@ -1682,7 +1760,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_K, mmq_y);
     const int   * x_qs = (const int   *) x;
@@ -1691,26 +1769,31 @@ static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a(
     const int   * y_qs = (const int   *) y + 4;
     const half2 * y_ds = (const half2 *) y;
 
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR5_K*VDR_Q5_K_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
 #pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
 
 #pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
 
-            const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8);
+                const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k00/32]) + 2*(k01/16);
 
-            sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q5_K_q8_1_impl_mmq(
-                &x_qs[i*(QR5_K*WARP_SIZE + 1) + QR5_K*k0], &y_qs[j*MMQ_TILE_Y_K + (QR5_K*k0) % WARP_SIZE], sc, sc+8,
-                x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[j*MMQ_TILE_Y_K + ((QR5_K*k0) % WARP_SIZE)/QI8_1]);
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q5_K_q8_1_impl_mmq(
+                    &x_qs[i*(QR5_K*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01], sc, sc+8,
+                    x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
         }
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 #ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K8 mma_A;
@@ -1731,26 +1814,34 @@ static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mma(
 
     const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
 
-    mma_A   A[ntx][2];
-    int   scA[ntx][mma_C::ne/2][2];
-    int    mA[ntx][mma_C::ne/2][2];
+    mma_A   A[ntx][4];
+    int   scA[ntx][mma_C::ne/2][4];
+    int    mA[ntx][mma_C::ne/2][4];
     half2 dmA[ntx][mma_C::ne/2];
 
 #pragma unroll
     for (int n = 0; n < ntx; ++n) {
 #pragma unroll
-        for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
-            A[n][kvdr/4].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q5_K + (QR5_K*k0 + QR5_K*kvdr), MMQ_MMA_TILE_X_K_Q5_K);
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
+            const int k0 = k00 + k01;
+
+            A[n][k01/8].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q5_K + k0, MMQ_MMA_TILE_X_K_Q5_K);
+        }
 
 #pragma unroll
-            for (int l = 0; l < mma_C::ne/2; ++l) {
-                const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+
+            const int sc_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q5_K + (k00/32 + 0)];
+            const int  m_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q5_K + (k00/32 + 2)];
 
-                const uint8_t * sc = ((const uint8_t *) &x_sc[i*MMQ_MMA_TILE_X_K_Q5_K + k0/16]) + 2 * ((k0 % 16) / 8);
-                const uint8_t *  m = sc + 8;
+            const uint8_t * sc = (const uint8_t *) &sc_packed;
+            const uint8_t *  m = (const uint8_t *)  &m_packed;
 
-                scA[n][l][kvdr/4] = sc[kvdr/4];
-                mA[n][l][kvdr/4]  =  m[kvdr/4];
+#pragma unroll
+            for (int ksc = 0; ksc < sizeof(int); ++ksc) {
+                scA[n][l][ksc] = sc[ksc];
+                mA[n][l][ksc]  =  m[ksc];
             }
         }
 
@@ -1758,7 +1849,7 @@ static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mma(
         for (int l = 0; l < mma_C::ne/2; ++l) {
             const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
 
-            dmA[n][l] = x_dm[i*MMQ_MMA_TILE_X_K_Q5_K + k0/QI5_K];
+            dmA[n][l] = x_dm[i*MMQ_MMA_TILE_X_K_Q5_K];
         }
     }
 
@@ -1768,28 +1859,30 @@ static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mma(
         float tmpm[ntx][mma_C::ne] = {{0.0f}};
 
 #pragma unroll
-        for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) {
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
+            const int k0 = k00 + k01;
+
             mma_B   B;
             half2 dsB[mma_C::ne/2];
 
-            B.load(y_qs + j0*MMQ_TILE_Y_K + (2*k0 + 2*kvdr) % WARP_SIZE, MMQ_TILE_Y_K);
+            B.load(y_qs + j0*MMQ_TILE_Y_K + k0 % WARP_SIZE, MMQ_TILE_Y_K);
 
 #pragma unroll
             for (int l = 0; l < mma_C::ne/2; ++l) {
                 const int j = j0 + mma_C::get_j(l);
 
-                dsB[l] = y_ds[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
+                dsB[l] = y_ds[j*MMQ_TILE_Y_K + (k0/QI8_1) % (WARP_SIZE/QI8_1)];
             }
 
 #pragma unroll
         for (int n = 0; n < ntx; ++n) {
                 mma_C C;
-                C.mma_K8(A[n][kvdr/4], B);
+                C.mma_K8(A[n][k01/8], B);
 
 #pragma unroll
                 for (int l = 0; l < mma_C::ne; ++l) {
-                    tmpd[n][l] += (C.x[l]*scA[n][l/2][kvdr/4]) *  __low2float(dsB[l%2]);
-                    tmpm[n][l] += mA[n][l/2][kvdr/4]           * __high2float(dsB[l%2]);
+                    tmpd[n][l] += (C.x[l]*scA[n][l/2][k01/8]) *  __low2float(dsB[l%2]);
+                    tmpm[n][l] += mA[n][l/2][k01/8]           * __high2float(dsB[l%2]);
                 }
             }
         }
@@ -1896,7 +1989,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q6_K_q8_1_dp4a(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q6_K, mmq_y);
     const int   * x_qs = (const int   *) x;
@@ -1905,26 +1998,31 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_dp4a(
     const int   * y_qs = (const int   *) y + 4;
     const float * y_df = (const float *) y;
 
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QR6_K*VDR_Q6_K_Q8_1_MMQ) {
+        const int k0 = k00 + k01;
+
 #pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
 
 #pragma unroll
-        for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-            const int i = i0 + threadIdx.x;
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
 
-            const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/8]);
+                const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]);
 
-            sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q6_K_q8_1_impl_mmq(
-                &x_qs[i*(QR6_K*WARP_SIZE + 1) + QR6_K*k0], &y_qs[j*MMQ_TILE_Y_K + (QR6_K*k0) % WARP_SIZE], sc,
-                x_df[i*(WARP_SIZE/QI6_K) + i/QI6_K], &y_df[j*MMQ_TILE_Y_K + ((QR6_K*k0) % WARP_SIZE)/QI8_1]);
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q6_K_q8_1_impl_mmq(
+                    &x_qs[i*(QR6_K*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01], sc,
+                    x_df[i*(WARP_SIZE/QI6_K) + i/QI6_K], &y_df[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
         }
     }
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k0) {
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
 #ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K4 mma_A;
@@ -1945,25 +2043,35 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
 
     const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
 
-    mma_A   A[ntx][4];
-    int   scA[ntx][mma_C::ne/2][4];
+    mma_A   A[ntx][8];
+    int   scA[ntx][mma_C::ne/2][8];
     float  dA[ntx][mma_C::ne/2];
 
 #pragma unroll
     for (int n = 0; n < ntx; ++n) {
 #pragma unroll
-        for (int kvdr = 0; kvdr < VDR_Q6_K_Q8_1_MMQ; kvdr += 4) {
-            A[n][kvdr/2 + 0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (QR6_K*k0 + QR6_K*kvdr + 0),        MMQ_MMA_TILE_X_K_Q6_K);
-            A[n][kvdr/2 + 1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (QR6_K*k0 + QR6_K*kvdr + mma_A::K), MMQ_MMA_TILE_X_K_Q6_K);
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
+            const int k0 = k00 + k01;
+
+            A[n][k01/4 + 0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + 0),        MMQ_MMA_TILE_X_K_Q6_K);
+            A[n][k01/4 + 1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + mma_A::K), MMQ_MMA_TILE_X_K_Q6_K);
+        }
+
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 16) {
+            const int k0 = k00 + k01;
 
 #pragma unroll
             for (int l = 0; l < mma_C::ne/2; ++l) {
                 const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
 
-                const int8_t * sc = ((const int8_t *) &x_sc[i*MMQ_MMA_TILE_X_K_Q6_K + k0/8]);
+                const int      sc_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q6_K + k0/16];
+                const int8_t * sc        = (const int8_t *) &sc_packed;
 
-                scA[n][l][kvdr/2 + 0] = sc[kvdr/2 + 0];
-                scA[n][l][kvdr/2 + 1] = sc[kvdr/2 + 1];
+#pragma unroll
+                for (int ksc = 0; ksc < sizeof(int); ++ksc) {
+                    scA[n][l][k01/4 + ksc] = sc[ksc];
+                }
             }
         }
 
@@ -1971,7 +2079,7 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
         for (int l = 0; l < mma_C::ne/2; ++l) {
             const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
 
-            dA[n][l] = x_df[i*MMQ_MMA_TILE_X_K_Q6_K + k0/QI6_K];
+            dA[n][l] = x_df[i*MMQ_MMA_TILE_X_K_Q6_K];
         }
     }
 
@@ -1980,30 +2088,29 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
         float tmp[ntx][mma_C::ne] = {{0.0f}};
 
 #pragma unroll
-        for (int kvdr = 0; kvdr < VDR_Q6_K_Q8_1_MMQ; kvdr += 4) {
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
             mma_B B[2];
             float dB[mma_C::ne/2];
 
-            const int k0B = (2*k0 + 2*kvdr) % WARP_SIZE;
-            B[0].load(y_qs + j0*MMQ_TILE_Y_K + 0        + k0B, MMQ_TILE_Y_K);
-            B[1].load(y_qs + j0*MMQ_TILE_Y_K + mma_B::K + k0B, MMQ_TILE_Y_K);
+            B[0].load(y_qs + j0*MMQ_TILE_Y_K + 0        + k01, MMQ_TILE_Y_K);
+            B[1].load(y_qs + j0*MMQ_TILE_Y_K + mma_B::K + k01, MMQ_TILE_Y_K);
 
 #pragma unroll
             for (int l = 0; l < mma_C::ne/2; ++l) {
                 const int j = j0 + mma_C::get_j(l);
 
-                dB[l] = y_df[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)];
+                dB[l] = y_df[j*MMQ_TILE_Y_K + k01/QI8_1];
             }
 
 #pragma unroll
             for (int n = 0; n < ntx; ++n) {
                 mma_C C[2];
-                C[0].mma_K4(A[n][kvdr/2 + 0], B[0]);
-                C[1].mma_K4(A[n][kvdr/2 + 1], B[1]);
+                C[0].mma_K4(A[n][k01/4 + 0], B[0]);
+                C[1].mma_K4(A[n][k01/4 + 1], B[1]);
 
 #pragma unroll
                 for (int l = 0; l < mma_C::ne; ++l) {
-                    tmp[n][l] += (C[0].x[l]*scA[n][l/2][kvdr/2 + 0] + C[1].x[l]*scA[n][l/2][kvdr/2 + 1])*dB[l%2];
+                    tmp[n][l] += (C[0].x[l]*scA[n][l/2][k01/4 + 0] + C[1].x[l]*scA[n][l/2][k01/4 + 1])*dB[l%2];
                 }
             }
         }
@@ -2051,8 +2158,8 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const int2 v = get_int_from_table_16(aux_q4);
         const int k0 = 8 * (threadIdx.x / 4) + threadIdx.x % 4;
 #ifdef INT8_MMA_AVAILABLE
-        x_qs[i*MMQ_MMA_TILE_X_K_Q5_0 + k0 + 0] = v.x;
-        x_qs[i*MMQ_MMA_TILE_X_K_Q5_0 + k0 + 4] = v.y;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 4] = v.y;
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + k0 + 0] = v.x;
         x_qs[i*(2*WARP_SIZE + 1)     + k0 + 4] = v.y;
@@ -2073,7 +2180,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const block_iq4_nl * bxi = (const block_iq4_nl *) x + kbx0 + i*stride + kbxd;
 
 #ifdef INT8_MMA_AVAILABLE
-        x_df[i*MMQ_MMA_TILE_X_K_Q5_0 + kbxd] = __half2float(bxi->d);
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = __half2float(bxi->d);
 #else
         x_df[i*(WARP_SIZE/4) + i/4   + kbxd] = __half2float(bxi->d);
 #endif // INT8_MMA_AVAILABLE
@@ -2109,8 +2216,8 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const int2 v = get_int_from_table_16(aux_q4);
         const int k0 = 8 * (threadIdx.x / 4) + threadIdx.x % 4;
 #ifdef INT8_MMA_AVAILABLE
-        x_qs[i*MMQ_MMA_TILE_X_K_Q5_0 + k0 + 0] = v.x;
-        x_qs[i*MMQ_MMA_TILE_X_K_Q5_0 + k0 + 4] = v.y;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 4] = v.y;
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + k0 + 0] = v.x;
         x_qs[i*(2*WARP_SIZE + 1)     + k0 + 4] = v.y;
@@ -2133,7 +2240,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
             | (((bxi->scales_h >> (2*(threadIdx.x % 8))) & 0x03) << 4);
 
 #ifdef INT8_MMA_AVAILABLE
-        x_df[i*MMQ_MMA_TILE_X_K_Q5_0 + threadIdx.x % 8] = d * (ls - 32);
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + threadIdx.x % 8] = d * (ls - 32);
 #else
         x_df[i*(WARP_SIZE/4) + i/4   + threadIdx.x % 8] = d * (ls - 32);
 #endif // INT8_MMA_AVAILABLE
@@ -2229,16 +2336,16 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_0> {
     static constexpr int              vdr          = VDR_Q5_0_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q5_0<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q5_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
-    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q5_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_1> {
     static constexpr int              vdr          = VDR_Q5_1_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q5_1<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q5_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
-    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q5_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
@@ -2293,45 +2400,18 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ4_NL> {
     static constexpr int              vdr          = VDR_IQ4_NL_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq4_nl<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q5_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
-    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q5_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ4_XS> {
     static constexpr int              vdr          = VDR_IQ4_XS_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq4_xs<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q5_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
-    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q5_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
-static bool mmq_need_sum(const ggml_type type_x) {
-    switch (type_x) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-            return true;
-        case GGML_TYPE_Q5_0:
-            return false;
-        case GGML_TYPE_Q5_1:
-            return true;
-        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-            return false;
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-            return true;
-        case GGML_TYPE_Q6_K:
-        case GGML_TYPE_IQ4_XS:
-        case GGML_TYPE_IQ4_NL:
-            return false;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
-    return false;
-}
-
 template <ggml_type type, int mmq_x, int nwarps, bool need_check, bool fixup>
 static __device__ void mul_mat_q_process_tile(
     const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, float * __restrict__ tmp_fixup,
@@ -2339,10 +2419,7 @@ static __device__ void mul_mat_q_process_tile(
     const int & it, const int & jt, const int & kb0_start, const int & kb0_stop) {
 
     constexpr int              qk         = ggml_cuda_type_traits<type>::qk;
-    constexpr int              qr         = ggml_cuda_type_traits<type>::qr;
-    constexpr int              qi         = ggml_cuda_type_traits<type>::qi;
     constexpr int              mmq_y      = get_mmq_y_device();
-    constexpr int              vdr        = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vdr;
     constexpr load_tiles_mmq_t load_tiles = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::load_tiles;
 
     extern __shared__ char data_mul_mat_q[];
@@ -2357,7 +2434,7 @@ static __device__ void mul_mat_q_process_tile(
     constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
 #endif // INT8_MMA_AVAILABLE
 
-    constexpr int blocks_per_warp = WARP_SIZE / qi;
+    constexpr int blocks_per_iter = MMQ_ITER_K / qk;
 
     float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f};
 
@@ -2366,29 +2443,40 @@ static __device__ void mul_mat_q_process_tile(
 
     const int * y = (const int *) yc + jt*(mmq_x*sizeof(block_q8_1_mmq)/sizeof(int));
 
-    for (int kb0 = kb0_start; kb0 < kb0_stop; kb0 += blocks_per_warp) {
-
+    for (int kb0 = kb0_start; kb0 < kb0_stop; kb0 += blocks_per_iter) {
         load_tiles(x, tile_x, stride01*it*mmq_y + kb0, tile_x_max_i, stride01);
 
-#pragma unroll
-        for (int kr = 0; kr < qr; ++kr) {
-            const int * by0 = y + stride11*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + kr*sizeof(block_q8_1_mmq)/sizeof(int));
+        {
+            const int * by0 = y + stride11*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + 0*sizeof(block_q8_1_mmq)/sizeof(int));
 #pragma unroll
             for (int l0 = 0; l0 < mmq_x*MMQ_TILE_Y_K; l0 += nwarps*WARP_SIZE) {
                 int l = l0 + threadIdx.y*WARP_SIZE + threadIdx.x;
 
                 tile_y[l] = by0[l];
             }
+        }
 
-            __syncthreads();
+        __syncthreads();
 
-// #pragma unroll // unrolling this loop causes too much register pressure
-            for (int k0 = kr*WARP_SIZE/qr; k0 < (kr+1)*WARP_SIZE/qr; k0 += vdr) {
-                vec_dot(tile_x, tile_y, sum, k0);
-            }
+        vec_dot(tile_x, tile_y, sum, 0);
+
+        __syncthreads();
+
+        {
+            const int * by0 = y + stride11*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + 1*sizeof(block_q8_1_mmq)/sizeof(int));
+#pragma unroll
+            for (int l0 = 0; l0 < mmq_x*MMQ_TILE_Y_K; l0 += nwarps*WARP_SIZE) {
+                int l = l0 + threadIdx.y*WARP_SIZE + threadIdx.x;
 
-            __syncthreads();
+                tile_y[l] = by0[l];
+            }
         }
+
+        __syncthreads();
+
+        vec_dot(tile_x, tile_y, sum, WARP_SIZE);
+
+        __syncthreads();
     }
 
     if (fixup) {
@@ -2424,7 +2512,6 @@ static __global__ void mul_mat_q(
     }
 
     constexpr int qk    = ggml_cuda_type_traits<type>::qk;
-    constexpr int qi    = ggml_cuda_type_traits<type>::qi;
     constexpr int mmq_y = get_mmq_y_device();
 
     // On AMD or old CUDA the performance with stream-k was worse, use conventional tiling instead:
@@ -2439,7 +2526,7 @@ static __global__ void mul_mat_q(
 #endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ < CC_VOLTA
 
     const     int64_t blocks_per_ne00 = ne00 / qk;
-    constexpr int     blocks_per_warp = WARP_SIZE / qi;
+    constexpr int     blocks_per_iter = MMQ_ITER_K / qk;
 
     const int ntx = (ne11 + mmq_x - 1) / mmq_x; // Number of tiles x
     const int nty = (ne01 + mmq_y - 1) / mmq_y; // Number of tiles y
@@ -2448,8 +2535,8 @@ static __global__ void mul_mat_q(
     int64_t kbc      = (int64_t) blockIdx.x     *blocks_per_ne00*ntx*nty / gridDim.x;
     int64_t kbc_stop = (int64_t)(blockIdx.x + 1)*blocks_per_ne00*ntx*nty / gridDim.x;
 
-    kbc      -= (kbc      % blocks_per_ne00) % blocks_per_warp;
-    kbc_stop -= (kbc_stop % blocks_per_ne00) % blocks_per_warp;
+    kbc      -= (kbc      % blocks_per_ne00) % blocks_per_iter;
+    kbc_stop -= (kbc_stop % blocks_per_ne00) % blocks_per_iter;
 
     // kb0 == k index when doing the matrix multiplication for an output tile.
     int kb0_start = kbc % blocks_per_ne00;
@@ -2490,8 +2577,7 @@ static __global__ void mul_mat_q_stream_k_fixup(
 
     constexpr int     mmq_y           = get_mmq_y_device();
     constexpr int     qk              = ggml_cuda_type_traits<type>::qk;
-    constexpr int     qi              = ggml_cuda_type_traits<type>::qi;
-    constexpr int     blocks_per_warp = WARP_SIZE / qi;
+    constexpr int     blocks_per_iter = MMQ_ITER_K / qk;
     const     int64_t blocks_per_ne00 = ne00 / qk;
 
     float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f};
@@ -2501,15 +2587,18 @@ static __global__ void mul_mat_q_stream_k_fixup(
 
     bool any_fixup = false;
 
-    const int bidx_start = (blockIdx.y*nty + blockIdx.x)     * block_num_mmq / (gridDim.y*gridDim.x);
-    const int bidx_stop  = (blockIdx.y*nty + blockIdx.x + 1) * block_num_mmq / (gridDim.y*gridDim.x) + 1;
+    const int bidx_start = ((blockIdx.y*nty + blockIdx.x)     * block_num_mmq)                           / (gridDim.y*gridDim.x);
+    const int bidx_stop  = ((blockIdx.y*nty + blockIdx.x + 1) * block_num_mmq + gridDim.y*gridDim.x - 1) / (gridDim.y*gridDim.x);
+
+    int64_t kbc_0;
+    int64_t kbc_stop_0 = (int64_t) bidx_start*blocks_per_ne00*ntx*nty / block_num_mmq;
 
     for (int bidx = bidx_start; bidx < bidx_stop; ++bidx) {
-        int64_t kbc      = (int64_t) bidx     *blocks_per_ne00*ntx*nty / block_num_mmq;
-        int64_t kbc_stop = (int64_t)(bidx + 1)*blocks_per_ne00*ntx*nty / block_num_mmq;
+        kbc_0 = kbc_stop_0;
+        kbc_stop_0 = (int64_t) (bidx + 1)*blocks_per_ne00*ntx*nty / block_num_mmq;
 
-        kbc      -= (kbc      % blocks_per_ne00) % blocks_per_warp;
-        kbc_stop -= (kbc_stop % blocks_per_ne00) % blocks_per_warp;
+        const int64_t kbc      = kbc_0      - (kbc_0      % blocks_per_ne00) % blocks_per_iter;
+        const int64_t kbc_stop = kbc_stop_0 - (kbc_stop_0 % blocks_per_ne00) % blocks_per_iter;
 
         // Skip fixup tile if the MMQ CUDA block never wrote anything to it:
         if (kbc == kbc_stop || kbc_stop % blocks_per_ne00 == 0) {
diff --git a/ggml/src/ggml-cuda/quantize.cu b/ggml/src/ggml-cuda/quantize.cu
index b4678682238d31..aa7f1eff0e6a25 100644
--- a/ggml/src/ggml-cuda/quantize.cu
+++ b/ggml/src/ggml-cuda/quantize.cu
@@ -37,47 +37,92 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
     reinterpret_cast<half&>(y[ib].ds.y) = sum;
 }
 
-template <bool need_sum>
+template <mmq_q8_1_ds_layout ds_layout>
 static __global__ void quantize_mmq_q8_1(
     const float * __restrict__ x, void * __restrict__ vy, const int64_t kx0, const int64_t kx1, const int64_t kx0_padded) {
 
-    const int64_t ix0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
+    constexpr int vals_per_scale = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 64 : 32;
+    constexpr int vals_per_sum   = ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6 ? 16 : 32;
+
+    const int64_t ix0 = ((int64_t)blockDim.x*blockIdx.x + threadIdx.x)*4;
 
     if (ix0 >= kx0_padded) {
         return;
     }
 
+    const float4 * x4 = (const float4 *) x;
+
     const int64_t ix1 = kx1*blockIdx.z + blockIdx.y;
 
     block_q8_1_mmq * y = (block_q8_1_mmq *) vy;
 
-    const int64_t ib0 = blockIdx.z*(gridDim.y*gridDim.x*blockDim.x/(4*QK8_1)); // first block of channel
-    const int64_t ib  = ib0 + (ix0 / (4*QK8_1))*kx1 + blockIdx.y;              // block index in channel
-    const int64_t iqs = ix0 % (4*QK8_1);                                       // quant index in block
-
-    const float xi = ix0 < kx0 ? x[ix1*kx0 + ix0] : 0.0f;
-    float amax = fabsf(xi);
-
-    amax = warp_reduce_max(amax);
+    const int64_t ib0 = blockIdx.z*((int64_t)gridDim.y*gridDim.x*blockDim.x/QK8_1); // first block of channel
+    const int64_t ib  = ib0 + (ix0 / (4*QK8_1))*kx1 + blockIdx.y;                   // block index in channel
+    const int64_t iqs = ix0 % (4*QK8_1);                                            // quant index in block
+
+    // Load 4 floats per thread and calculate max. abs. value between them:
+    const float4 xi = ix0 < kx0 ? x4[(ix1*kx0 + ix0)/4] : make_float4(0.0f, 0.0f, 0.0f, 0.0f);
+    float amax = fabsf(xi.x);
+    amax = fmaxf(amax, fabsf(xi.y));
+    amax = fmaxf(amax, fabsf(xi.z));
+    amax = fmaxf(amax, fabsf(xi.w));
+
+    // Exchange max. abs. value between vals_per_scale/4 threads.
+#pragma unroll
+    for (int mask = vals_per_scale/8; mask > 0; mask >>= 1) {
+        amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, WARP_SIZE));
+    }
 
     float sum;
-    if (need_sum) {
-        sum = warp_reduce_sum(xi);
+    if (ds_layout != MMQ_Q8_1_DS_LAYOUT_D4) {
+        sum = xi.x + xi.y + xi.z + xi.w;
+
+        // Exchange calculate sum across vals_per_sum/4 threads.
+#pragma unroll
+        for (int mask = vals_per_sum/8; mask > 0; mask >>= 1) {
+            sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, WARP_SIZE);
+        }
     }
 
-    const float d = amax / 127;
-    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
+    const float d_inv = 127.0f / amax;
+    char4 q;
+    q.x = roundf(xi.x*d_inv);
+    q.y = roundf(xi.y*d_inv);
+    q.z = roundf(xi.z*d_inv);
+    q.w = roundf(xi.w*d_inv);
 
-    y[ib].qs[iqs] = q;
+    // Write back 4 int8 values as a single 32 bit value for better memroy bandwidth:
+    char4 * yqs4 = (char4 *) y[ib].qs;
+    yqs4[iqs/4] = q;
+
+    if (ds_layout == MMQ_Q8_1_DS_LAYOUT_D2S6) {
+        if (iqs % 16 != 0 || iqs >= 96) {
+            return;
+        }
+
+        y[ib].d2s6[2 + iqs/16] = sum;
+
+        if (iqs % 64 != 0) {
+            return;
+        }
+
+        const float d = 1.0f / d_inv;
+
+        y[ib].d2s6[iqs/64] = d;
 
-    if (iqs % QK8_1 != 0) {
         return;
     }
 
-    if (need_sum) {
-        y[ib].ds[iqs/QK8_1] = make_half2(d, sum);
+    if (iqs % 32 != 0) {
+        return;
+    }
+
+    const float d = 1.0f / d_inv;
+
+    if (ds_layout == MMQ_Q8_1_DS_LAYOUT_DS4) {
+        y[ib].ds4[iqs/32] = make_half2(d, sum);
     } else {
-        ((float *) y[ib].ds)[iqs/QK8_1] = d;
+        y[ib].d4[iqs/32]  = d;
     }
 }
 
@@ -101,12 +146,24 @@ void quantize_mmq_q8_1_cuda(
 
     GGML_ASSERT(kx0_padded % (4*QK8_1) == 0);
 
-    const int64_t block_num_x = (kx0_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    const int64_t block_num_x = (kx0_padded + 4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ - 1) / (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ);
     const dim3 num_blocks(block_num_x, kx1, channels);
-    const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
-    if (mmq_need_sum(type_x)) {
-        quantize_mmq_q8_1<true><<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
-    } else {
-        quantize_mmq_q8_1<false><<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
+    const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1);
+    switch (mmq_get_q8_1_ds_layout(type_x)) {
+        case MMQ_Q8_1_DS_LAYOUT_D4:
+            quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_D4>
+                <<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
+            break;
+        case MMQ_Q8_1_DS_LAYOUT_DS4:
+            quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_DS4>
+                <<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
+            break;
+        case MMQ_Q8_1_DS_LAYOUT_D2S6:
+            quantize_mmq_q8_1<MMQ_Q8_1_DS_LAYOUT_D2S6>
+                <<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
     }
 }
diff --git a/ggml/src/ggml-cuda/quantize.cuh b/ggml/src/ggml-cuda/quantize.cuh
index 486c9360a46fdc..03bf322b958736 100644
--- a/ggml/src/ggml-cuda/quantize.cuh
+++ b/ggml/src/ggml-cuda/quantize.cuh
@@ -5,7 +5,11 @@
 
 #include <cstdint>
 
-#define CUDA_QUANTIZE_BLOCK_SIZE 256
+#define CUDA_QUANTIZE_BLOCK_SIZE     256
+#define CUDA_QUANTIZE_BLOCK_SIZE_MMQ 128
+
+static_assert(MATRIX_ROW_PADDING %    CUDA_QUANTIZE_BLOCK_SIZE      == 0, "Risk of out-of-bounds access.");
+static_assert(MATRIX_ROW_PADDING % (4*CUDA_QUANTIZE_BLOCK_SIZE_MMQ) == 0, "Risk of out-of-bounds access.");
 
 typedef void (*quantize_cuda_t)(
     const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded,
diff --git a/ggml/src/ggml-cuda/vecdotq.cuh b/ggml/src/ggml-cuda/vecdotq.cuh
index 1d510484ac4742..6a17d0f3eb0d29 100644
--- a/ggml/src/ggml-cuda/vecdotq.cuh
+++ b/ggml/src/ggml-cuda/vecdotq.cuh
@@ -189,7 +189,7 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_imp
 }
 
 #define VDR_Q2_K_Q8_1_MMVQ 1
-#define VDR_Q2_K_Q8_1_MMQ  2
+#define VDR_Q2_K_Q8_1_MMQ  4
 
 // contiguous v/x values
 static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
@@ -219,32 +219,56 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
     return dm2f.x*sumf_d - dm2f.y*sumf_m;
 }
 
-// contiguous u/y values
+// contiguous v/x + u/y values
+template <int ns8>
 static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
-    const int * __restrict__ v, const int * __restrict__ u, const half2 * dm2, const float & d8) {
+    const int * __restrict__ v, const int * __restrict__ u, const half2 * dm2, const float & d8, const half2 * s8) {
 
-    float sumf_d = 0.0f;
-    float sumf_m = 0.0f;
+    float sumf    = 0.0f;
+    float sumf_d8 = 0.0f;
 
 #pragma unroll
-    for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
-        const float2 dm2f = __half22float2(dm2[i0/(QI8_1/2)]);
-        int sumi_d = 0;
-        int sumi_m = 0;
+    for (int i0 = 0; i0 < QR2_K*VDR_Q2_K_Q8_1_MMQ; i0 += QI8_1) {
+        const float2 dm2f0 = __half22float2(dm2[i0/(QI8_1/2) + 0]);
+        int sumi_d0 = 0;
+
+        const float2 dm2f1 = __half22float2(dm2[i0/(QI8_1/2) + 1]);
+        int sumi_d1 = 0;
 
-        const int vi0 = v[i0/(QI8_1/2)];
 #pragma unroll
         for (int i = i0; i < i0 + QI8_1/2; ++i) {
-            const int vi = (vi0 >> (2*(i % (QI8_1/2)))) & 0x03030303;
-            sumi_d = ggml_cuda_dp4a(vi,         u[i], sumi_d); // SIMD dot product
-            sumi_m = ggml_cuda_dp4a(0x01010101, u[i], sumi_m);
+            sumi_d0 = ggml_cuda_dp4a(v[i], u[i], sumi_d0);
+        }
+        sumf_d8 += dm2f0.x * sumi_d0;
+
+#pragma unroll
+        for (int i = i0 + QI8_1/2; i < i0 + QI8_1; ++i) {
+            sumi_d1 = ggml_cuda_dp4a(v[i], u[i], sumi_d1);
         }
+        sumf_d8 += dm2f1.x * sumi_d1;
 
-        sumf_d += dm2f.x * sumi_d;
-        sumf_m += dm2f.y * sumi_m;
+        if (i0/QI8_1 < ns8) {
+            const float2 s8f = __half22float2(s8[i0/QI8_1]);
+            sumf -= dm2f0.y*s8f.x;
+            sumf -= dm2f1.y*s8f.y;
+        } else {
+            int sumi_m0 = 0;
+#pragma unroll
+            for (int i = i0; i < i0 + QI8_1/2; ++i) {
+                sumi_m0 = ggml_cuda_dp4a(0x01010101, u[i], sumi_m0);
+            }
+            sumf_d8 -= dm2f0.y * sumi_m0;
+
+            int sumi_m1 = 0;
+#pragma unroll
+            for (int i = i0 + QI8_1/2; i < i0 + QI8_1; ++i) {
+                sumi_m1 = ggml_cuda_dp4a(0x01010101, u[i], sumi_m1);
+            }
+            sumf_d8 -= dm2f1.y * sumi_m1;
+        }
     }
 
-    return d8*(sumf_d - sumf_m);
+    return sumf + d8*sumf_d8;
 }
 
 #define VDR_Q3_K_Q8_1_MMVQ 1
@@ -283,7 +307,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
     return d3 * sumf;
 }
 
-// contiguous u/y values
+// contiguous v/x + u/y values
 static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
     const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
     const float & d3, const float & d8) {
@@ -296,8 +320,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
 
 #pragma unroll
         for (int i = i0; i < i0 + QI8_1/2; ++i) {
-            const int vi = __vsubss4((v[i/2] >> (4*(i%2))) & 0x0F0F0F0F, 0x04040404);
-            sumi_sc = ggml_cuda_dp4a(vi, u[i], sumi_sc); // SIMD dot product
+            sumi_sc = ggml_cuda_dp4a(v[i], u[i], sumi_sc); // SIMD dot product
         }
 
         sumi += sumi_sc * scales[i0 / (QI8_1/2)];
@@ -334,7 +357,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
     return dm4f.x*sumf_d - dm4f.y*sumf_m;
 }
 
-// contiguous u/y values
+// contiguous v/x + u/y values
 static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
     const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
     const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
@@ -397,7 +420,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
     return dm5f.x*sumf_d - dm5f.y*sumf_m;
 }
 
-// contiguous u/y values
+// contiguous v/x + u/y values
 static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
     const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
     const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
@@ -451,13 +474,16 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
     return d*sumf;
 }
 
-// contiguous u/y values
+// contiguous v/x + u/y values
 static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
     const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
     const float & d6, const float * __restrict__ d8) {
 
     float sumf_d = 0.0f;
 
+    const int      sc_packed = get_int_b4(sc, 0);
+    const int8_t * sc_reg    = (const int8_t *) &sc_packed;
+
 #pragma unroll
     for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
         int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
@@ -471,7 +497,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
             sumi_d.y = ggml_cuda_dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
         }
 
-        sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
+        sumf_d += d8[i0/4] * (sc_reg[i0/2+0]*sumi_d.x + sc_reg[i0/2+1]*sumi_d.y);
     }
 
     return d6 * sumf_d;

From b078c619aa4e97fe726d61b0b5499a2e19a418a4 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Thu, 11 Jul 2024 17:53:42 +0200
Subject: [PATCH 033/125] cuda : suppress 'noreturn' warn in no_device_code
 (#8414)

* cuda : suppress 'noreturn' warn in no_device_code

This commit adds a while(true) loop to the no_device_code function in
common.cuh. This is done to suppress the warning:

```console
/ggml/src/ggml-cuda/template-instances/../common.cuh:346:1: warning:
function declared 'noreturn' should not return [-Winvalid-noreturn]
  346 | }
      | ^
```

The motivation for this is to reduce the number of warnings when
compilng with GGML_HIPBLAS=ON.

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>

* squash! cuda : suppress 'noreturn' warn in no_device_code

Update __trap macro instead of using a while loop to suppress the
warning.

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>

---------

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
---
 ggml/src/ggml-cuda/common.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 4ff06b8719d378..26d9412a23eb6f 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -104,7 +104,7 @@
 #define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
 #define cudaStream_t hipStream_t
 #define cudaSuccess hipSuccess
-#define __trap abort
+#define __trap() do { abort(); __builtin_unreachable(); } while(0)
 #define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
 #define CUBLAS_STATUS_NOT_INITIALIZED HIPBLAS_STATUS_NOT_INITIALIZED
 #define CUBLAS_STATUS_ALLOC_FAILED HIPBLAS_STATUS_ALLOC_FAILED

From 368645698ab648e390dcd7c00a2bf60efa654f57 Mon Sep 17 00:00:00 2001
From: Nicholai Tukanov <nicholaitukanov@gmail.com>
Date: Thu, 11 Jul 2024 11:49:15 -0500
Subject: [PATCH 034/125] ggml : add NVPL BLAS support (#8329) (#8425)

* ggml : add NVPL BLAS support

* ggml : replace `<BLASLIB>_ENABLE_CBLAS` with `GGML_BLAS_USE_<BLASLIB>`

---------

Co-authored-by: ntukanov <ntukanov@nvidia.com>
---
 Makefile               |  8 +++++++-
 ggml/src/ggml-blas.cpp | 13 +++++++++----
 2 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/Makefile b/Makefile
index 668b38b99c3129..4869d2ecf3be28 100644
--- a/Makefile
+++ b/Makefile
@@ -547,11 +547,17 @@ ifdef GGML_OPENBLAS64
 endif # GGML_OPENBLAS64
 
 ifdef GGML_BLIS
-	MK_CPPFLAGS += -DGGML_USE_BLAS -I/usr/local/include/blis -I/usr/include/blis
+	MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_BLIS -I/usr/local/include/blis -I/usr/include/blis
 	MK_LDFLAGS  += -lblis -L/usr/local/lib
 	OBJ_GGML    += ggml/src/ggml-blas.o
 endif # GGML_BLIS
 
+ifdef GGML_NVPL
+	MK_CPPFLAGS += -DGGML_USE_BLAS -DGGML_BLAS_USE_NVPL -DNVPL_ILP64 -I/usr/local/include/nvpl_blas -I/usr/include/nvpl_blas
+	MK_LDFLAGS  += -L/usr/local/lib -lnvpl_blas_core -lnvpl_blas_ilp64_gomp
+	OBJ_GGML    += ggml/src/ggml-blas.o
+endif # GGML_NVPL
+
 ifndef GGML_NO_LLAMAFILE
 	MK_CPPFLAGS += -DGGML_USE_LLAMAFILE
 	OBJ_GGML    += ggml/src/llamafile/sgemm.o
diff --git a/ggml/src/ggml-blas.cpp b/ggml/src/ggml-blas.cpp
index d709a357bbf290..a37aa407282b94 100644
--- a/ggml/src/ggml-blas.cpp
+++ b/ggml/src/ggml-blas.cpp
@@ -8,11 +8,12 @@
 #   include <Accelerate/Accelerate.h>
 #elif defined(GGML_BLAS_USE_MKL)
 #   include <mkl.h>
+#elif defined(GGML_BLAS_USE_BLIS)
+#   include <blis.h>
+#elif defined(GGML_BLAS_USE_NVPL)
+#   include <nvpl_blas.h>
 #else
 #   include <cblas.h>
-#   ifdef BLIS_ENABLE_CBLAS
-#       include <blis.h>
-#   endif
 #endif
 
 struct ggml_backend_blas_context {
@@ -140,10 +141,14 @@ static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct gg
     openblas_set_num_threads(ctx->n_threads);
 #endif
 
-#if defined(BLIS_ENABLE_CBLAS)
+#if defined(GGML_BLAS_USE_BLIS)
     bli_thread_set_num_threads(ctx->n_threads);
 #endif
 
+#if defined(GGML_BLAS_USE_NVPL)
+    nvpl_blas_set_num_threads(ctx->n_threads);
+#endif
+
     for (int64_t i13 = 0; i13 < ne13; i13++) {
         for (int64_t i12 = 0; i12 < ne12; i12++) {
             const int64_t i03 = i13/r3;

From b549a1bbefb2f1fbb8b558bac1f2ae7967e60964 Mon Sep 17 00:00:00 2001
From: Chen Xi <xixichen08@foxmail.com>
Date: Fri, 12 Jul 2024 00:52:04 +0000
Subject: [PATCH 035/125] [SYCL] fix the mul_mat_id ut issues (#8427)

* fix part of mul_mat_id

* skip the bfloat 16 sycl ut

Signed-off-by: Chen Xi <xi2chen@intel.com>

---------

Signed-off-by: Chen Xi <xi2chen@intel.com>
Co-authored-by: Meng, Hengyu <hengyu.meng@intel.com>
Co-authored-by: Chen Xi <xi2chen@intel.com>
---
 ggml/src/ggml-backend.c |  2 +-
 ggml/src/ggml-sycl.cpp  | 49 +++++++++++------------------------------
 src/llama.cpp           |  7 ------
 3 files changed, 14 insertions(+), 44 deletions(-)

diff --git a/ggml/src/ggml-backend.c b/ggml/src/ggml-backend.c
index 13c71c310c446d..dbbaa3941febea 100644
--- a/ggml/src/ggml-backend.c
+++ b/ggml/src/ggml-backend.c
@@ -394,7 +394,7 @@ void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event)
 
 // backend registry
 
-#define GGML_REG_MAX_BACKENDS 16
+#define GGML_REG_MAX_BACKENDS 64
 
 struct ggml_backend_reg {
     char name[128];
diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl.cpp
index 9c419ba8967064..5a890237f24535 100644
--- a/ggml/src/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl.cpp
@@ -3768,37 +3768,13 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_ten
         stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
     SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
 
-    const ggml_tensor_extra_gpu *src0_extra =
-        (const ggml_tensor_extra_gpu *)src0->extra;
-    const ggml_tensor_extra_gpu *src1_extra =
-        (const ggml_tensor_extra_gpu *)src1->extra;
-    const ggml_tensor_extra_gpu *dst_extra =
-        (const ggml_tensor_extra_gpu *)dst->extra;
-
-    ggml_tensor_extra_gpu src0_row_extra;
-    ggml_tensor_extra_gpu src1_row_extra;
-    ggml_tensor_extra_gpu dst_row_extra;
-
     ggml_tensor src0_row = *src0;
     ggml_tensor src1_row = *src1;
     ggml_tensor dst_row = *dst;
 
-    src1_row.backend = GGML_BACKEND_TYPE_GPU;
-    dst_row.backend  = GGML_BACKEND_TYPE_GPU;
-
-    src0_row.extra = &src0_row_extra;
-    src1_row.extra = &src1_row_extra;
-    dst_row.extra = &dst_row_extra;
-
-    char *src0_original = src1->backend == GGML_BACKEND_TYPE_CPU
-                              ? (char *)src0->data
-                              : (char *)src0_extra->data_device[ctx.device];
-    char *src1_original = src1->backend == GGML_BACKEND_TYPE_CPU
-                              ? (char *)src1->data
-                              : (char *)src1_extra->data_device[ctx.device];
-    char *dst_original = dst->backend == GGML_BACKEND_TYPE_CPU
-                             ? (char *)dst->data
-                             : (char *)dst_extra->data_device[ctx.device];
+    char *src0_original = (char *)src0->data;
+    char *src1_original = (char *)src1->data;
+    char *dst_original = (char *)dst->data;
 
     src0_row.ne[2] = 1;
     src0_row.ne[3] = 1;
@@ -3827,12 +3803,9 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_ten
                 const int64_t i1 = id;
                 const int64_t i2 = i12;
 
-            src0_row_extra.data_device[ctx.device] =
-                src0_original + i02*nb02;
-            src1_row_extra.data_device[ctx.device] =
-                src1_original + + i11*nb11 + i12*nb12;
-            dst_row_extra.data_device[ctx.device] =
-                dst_original + i1*nb1   + i2*nb2;
+            src0_row.data = src0_original + i02*nb02;
+            src1_row.data = src1_original + + i11*nb11 + i12*nb12;
+            dst_row.data = dst_original + i1*nb1   + i2*nb2;
 
             ggml_sycl_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
             }
@@ -3841,8 +3814,8 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_ten
         ggml_sycl_pool_alloc<char> src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1));
         ggml_sycl_pool_alloc<char>  dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst));
 
-        src1_row_extra.data_device[ctx.device] = src1_contiguous.get();
-        dst_row_extra.data_device[ctx.device]  =  dst_contiguous.get();
+        src1_row.data = src1_contiguous.get();
+        dst_row.data  =  dst_contiguous.get();
 
         for (int64_t i02 = 0; i02 < n_as; i02++) {
             int64_t num_src1_rows = 0;
@@ -3898,7 +3871,7 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_ten
                 });
             }
 
-            src0_row_extra.data_device[ctx.device] = src0_original + i02*nb02;
+            src0_row.data = src0_original + i02*nb02;
 
             GGML_ASSERT(nb11 == sizeof(float)*ne10);
             GGML_ASSERT(nb1 == sizeof(float)*ne0);
@@ -5221,6 +5194,10 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
                         return false;
                     }
                 }
+                ggml_type src0_type = op->src[0]->type;
+                if (src0_type == GGML_TYPE_BF16) {
+                    return false;
+                }
                 return true;
             } break;
         case GGML_OP_GET_ROWS:
diff --git a/src/llama.cpp b/src/llama.cpp
index ed77ed918e554e..f91ac777969d98 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -5883,13 +5883,6 @@ static bool llm_load_tensors(
 
     auto & hparams = model.hparams;
 
-#ifdef GGML_USE_SYCL
-    // disable MoE with SYCL until mul_mat_id is updated
-    if (hparams.n_expert > 0) {
-        n_gpu_layers = 0;
-    }
-#endif
-
     model.split_mode   = split_mode;
     model.main_gpu     = main_gpu;
     model.n_gpu_layers = n_gpu_layers;

From 370b1f7e7a7514d9a63daf15f7b0f00319b7f908 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 12 Jul 2024 10:46:02 +0300
Subject: [PATCH 036/125] ggml : minor naming changes (#8433)

* ggml : minor naming changes

ggml-ci

* ggml : use PRId64 [no ci]

* ggml : revert FA K/Q names
---
 examples/quantize-stats/quantize-stats.cpp |   2 +-
 ggml/include/ggml.h                        |  50 +++++-----
 ggml/src/ggml-aarch64.c                    |  68 +++++++------
 ggml/src/ggml-aarch64.h                    |  14 +--
 ggml/src/ggml-quants.c                     |  94 ++++++++---------
 ggml/src/ggml-quants.h                     |  38 +++----
 ggml/src/ggml.c                            | 111 ++++++++++-----------
 tests/test-double-float.cpp                |   4 +-
 tests/test-quantize-fns.cpp                |   2 +-
 tests/test-quantize-perf.cpp               |   2 +-
 10 files changed, 194 insertions(+), 191 deletions(-)

diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp
index 746df8446b85e8..68cf8d3595e878 100644
--- a/examples/quantize-stats/quantize-stats.cpp
+++ b/examples/quantize-stats/quantize-stats.cpp
@@ -154,7 +154,7 @@ static void test_roundtrip_on_chunk(
     }
 
     if (use_reference) {
-        qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size);
+        qfns.from_float_ref(input_scratch, quantized_scratch, chunk_size);
     } else {
         qfns.from_float(input_scratch, quantized_scratch, chunk_size);
     }
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 1e367753738d9f..f2145ff356cbd1 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -714,9 +714,9 @@ extern "C" {
     GGML_API GGML_CALL size_t  ggml_nbytes      (const struct ggml_tensor * tensor);
     GGML_API           size_t  ggml_nbytes_pad  (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
 
-    GGML_API GGML_CALL int    ggml_blck_size(enum ggml_type type);
-    GGML_API GGML_CALL size_t ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
-    GGML_API GGML_CALL size_t ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
+    GGML_API GGML_CALL int64_t ggml_blck_size(enum ggml_type type);
+    GGML_API GGML_CALL size_t  ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
+    GGML_API GGML_CALL size_t  ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
 
     GGML_DEPRECATED(
     GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
@@ -2410,31 +2410,31 @@ extern "C" {
 #endif
     typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
     typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int64_t k);
-    typedef void (*ggml_vec_dot_t)   (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
-                                      const void * GGML_RESTRICT y, size_t by, int nrc);
-    typedef void (*ggml_from_float_to_mat_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int64_t nr,
-                                             int64_t k, int64_t bx);
-    typedef void (*ggml_gemv_t)      (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
-                                      const void * GGML_RESTRICT y, int nr, int nc);
-    typedef void (*ggml_gemm_t)      (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
-                                      const void * GGML_RESTRICT y, int nr, int nc);
+    typedef void (*ggml_from_float_to_mat_t)
+                                     (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs);
+    typedef void (*ggml_vec_dot_t)  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
+                                       const void * GGML_RESTRICT y, size_t by, int nrc);
+    typedef void (*ggml_gemv_t)     (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
+                                       const void * GGML_RESTRICT y, int nr, int nc);
+    typedef void (*ggml_gemm_t)     (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
+                                       const void * GGML_RESTRICT y, int nr, int nc);
 
     typedef struct {
-        const char      * type_name;
-        int               blck_size;
-        size_t            type_size;
-        bool              is_quantized;
-        ggml_to_float_t   to_float;
-        ggml_from_float_t from_float;
-        ggml_from_float_t from_float_reference;
-        ggml_vec_dot_t    vec_dot;
-        enum ggml_type    vec_dot_type;
-        int64_t           nrows; // number of rows to process simultaneously;
-        int64_t           ncols; // number of columns to process simultaneously;
-        int64_t           interleave_blcksize; // interleave elements in blocks of interleave_blcksize;
+        const char             * type_name;
+        int64_t                  blck_size;
+        int64_t                  blck_size_interleave; // interleave elements in blocks
+        size_t                   type_size;
+        bool                     is_quantized;
+        ggml_to_float_t          to_float;
+        ggml_from_float_t        from_float;
+        ggml_from_float_t        from_float_ref;
         ggml_from_float_to_mat_t from_float_to_mat;
-        ggml_gemv_t       gemv;
-        ggml_gemm_t       gemm;
+        ggml_vec_dot_t           vec_dot;
+        enum ggml_type           vec_dot_type;
+        int64_t                  nrows; // number of rows to process simultaneously
+        int64_t                  ncols; // number of columns to process simultaneously
+        ggml_gemv_t              gemv;
+        ggml_gemm_t              gemm;
     } ggml_type_traits_t;
 
     GGML_API ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
diff --git a/ggml/src/ggml-aarch64.c b/ggml/src/ggml-aarch64.c
index 008718634fe0d2..40838cf4ffdcf6 100644
--- a/ggml/src/ggml-aarch64.c
+++ b/ggml/src/ggml-aarch64.c
@@ -20,19 +20,19 @@
 
 // Functions to create the interleaved data layout formats
 
-// interleave 4 block_q4_0s in blocks of interleave_blcksize
+// interleave 4 block_q4_0s in blocks of blck_size_interleave
 // returns an interleaved block_q4_0x4
 // in the interleaved block_q4_0x4, place deltas for 4 block_q4_0 blocks
-// first, then interleave quants from 4 block_q4_0s in blocks of interleave_blcksize
+// first, then interleave quants from 4 block_q4_0s in blocks of blck_size_interleave
 //
 // - in                  : an array of block_q4_0 pointers
-// - interleave_blcksize : the block_q4_0 quants bytes are interleaved in blocks of
-//                         interleave_blcksize bytes
+// - blck_size_interleave : the block_q4_0 quants bytes are interleaved in blocks of
+//                         blck_size_interleave bytes
 // - xor_mask            : the mask to convert the nibbles in block_q4_0 quants bytes
 //                         from bias offset form to pure sign form (this saves subtract
 //                         operations durin unpacking)
 //
-static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int interleave_blcksize, unsigned int xor_mask) {
+static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) {
     block_q4_0x4 out;
 
     for (int i = 0; i < 4; i++) {
@@ -40,9 +40,9 @@ static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int interleave_b
     }
 
     for (int i = 0; i < QK4_0 * 2; i++) {
-        int src_offset = (i / (4 * interleave_blcksize)) * interleave_blcksize;
-        int src_id = (i % (4 * interleave_blcksize)) / interleave_blcksize;
-        src_offset += (i % interleave_blcksize);
+        int src_offset = (i / (4 * blck_size_interleave)) * blck_size_interleave;
+        int src_id = (i % (4 * blck_size_interleave)) / blck_size_interleave;
+        src_offset += (i % blck_size_interleave);
 
         out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask;
     }
@@ -50,11 +50,11 @@ static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int interleave_b
     return out;
 }
 
-// interleave 8 block_q4_0s in blocks of interleave_blcksize
+// interleave 8 block_q4_0s in blocks of blck_size_interleave
 // returns an interleaved block_q4_0x8
 // in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks
-// first, then interleave quants from 8 block_q4_0s in blocks of interleave_blcksize
-static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int interleave_blcksize, unsigned int xor_mask) {
+// first, then interleave quants from 8 block_q4_0s in blocks of blck_size_interleave
+static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave, unsigned int xor_mask) {
     block_q4_0x8 out;
 
     for (int i = 0; i < 8; i++) {
@@ -62,9 +62,9 @@ static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int interleave_b
     }
 
     for (int i = 0; i < QK4_0 * 4; i++) {
-        int src_offset = (i / (8 * interleave_blcksize)) * interleave_blcksize;
-        int src_id = (i % (8 * interleave_blcksize)) / interleave_blcksize;
-        src_offset += (i % interleave_blcksize);
+        int src_offset = (i / (8 * blck_size_interleave)) * blck_size_interleave;
+        int src_id = (i % (8 * blck_size_interleave)) / blck_size_interleave;
+        src_offset += (i % blck_size_interleave);
 
         out.qs[i] = in[src_id].qs[src_offset] ^ xor_mask;
     }
@@ -135,7 +135,7 @@ void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k)
     }
 #else
     // scalar
-    const int interleave_blcksize = 4;
+    const int blck_size_interleave = 4;
     float srcv[4][QK8_0];
     float id[4];
 
@@ -155,12 +155,12 @@ void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k)
         }
 
         for (int j = 0; j < QK8_0 * 4; j++) {
-            int src_offset = (j / (4 * interleave_blcksize)) * interleave_blcksize;
-            int src_id = (j % (4 * interleave_blcksize)) / interleave_blcksize;
-            src_offset += (j % interleave_blcksize);
+            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
+            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
+            src_offset += (j % blck_size_interleave);
 
             float x0 = srcv[src_id][src_offset] * id[src_id];
-            y[i].qs[j] = roundf(x0);;
+            y[i].qs[j] = roundf(x0);
         }
     }
 #endif
@@ -253,7 +253,7 @@ void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k)
     }
 #else
     // scalar
-    const int interleave_blcksize = 8;
+    const int blck_size_interleave = 8;
     float srcv[4][QK8_0];
     float id[4];
 
@@ -273,26 +273,30 @@ void quantize_q8_0_4x8(const float * restrict x, void * restrict vy, int64_t k)
         }
 
         for (int j = 0; j < QK8_0 * 4; j++) {
-            int src_offset = (j / (4 * interleave_blcksize)) * interleave_blcksize;
-            int src_id = (j % (4 * interleave_blcksize)) / interleave_blcksize;
-            src_offset += (j % interleave_blcksize);
+            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
+            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
+            src_offset += (j % blck_size_interleave);
 
             float x0 = srcv[src_id][src_offset] * id[src_id];
-            y[i].qs[j] = roundf(x0);;
+            y[i].qs[j] = roundf(x0);
         }
     }
 #endif
 }
 
-void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t interleave_blcksize) {
+void quantize_mat_q8_0(const float * restrict x, void * restrict vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) {
     assert(nrow == 4);
     UNUSED(nrow);
-    if (interleave_blcksize == 4) quantize_q8_0_4x4(x, vy, n_per_row);
-    else if (interleave_blcksize == 8) quantize_q8_0_4x8(x, vy, n_per_row);
-    else assert(false);
+    if (blck_size_interleave == 4) {
+        quantize_q8_0_4x4(x, vy, n_per_row);
+    } else if (blck_size_interleave == 8) {
+        quantize_q8_0_4x8(x, vy, n_per_row);
+    } else {
+        assert(false);
+    }
 }
 
-static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, int nrows_interleaved, int interleave_blcksize) {
+static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, int nrows_interleaved, int blck_size_interleave) {
     assert(n_per_row % QK4_0 == 0);
     const int nb = n_per_row / QK4_0;
 
@@ -311,15 +315,15 @@ static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict ds
         for (int64_t x = 0; x < nb; x++) {
 
             for (int i  = 0; i < nrows_interleaved; i++ ) {
-                quantize_row_q4_0_reference(src + b + i * n_per_row + x * QK4_0, (block_q4_0 *) dst_tmp + i, QK4_0);
+                quantize_row_q4_0_ref(src + b + i * n_per_row + x * QK4_0, (block_q4_0 *) dst_tmp + i, QK4_0);
             }
 
             if (nrows_interleaved == 8) {
-                *(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, interleave_blcksize, 0x88);
+                *(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, blck_size_interleave, 0x88);
                 out_ptr = (block_q4_0x8 *) out_ptr + 1;
             }
             else if (nrows_interleaved == 4) {
-                *(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, interleave_blcksize, 0x88);
+                *(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, blck_size_interleave, 0x88);
                 out_ptr = (block_q4_0x4 *) out_ptr + 1;
             }
         }
diff --git a/ggml/src/ggml-aarch64.h b/ggml/src/ggml-aarch64.h
index 65ead1efed5728..517babaf1691bb 100644
--- a/ggml/src/ggml-aarch64.h
+++ b/ggml/src/ggml-aarch64.h
@@ -16,7 +16,7 @@ extern "C" {
 void quantize_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 
-void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t interleave_blcksize);
+void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nrows, int64_t n_per_row, int64_t blck_size_interleave);
 
 // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
 size_t quantize_q4_0_4x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
@@ -24,14 +24,14 @@ size_t quantize_q4_0_4x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT d
 size_t quantize_q4_0_8x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 
 // GEMV
-void ggml_gemv_q4_0_4x4_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q4_0_4x8_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q4_0_8x8_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 
 // GEMM
-void ggml_gemm_q4_0_4x4_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q4_0_4x8_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q4_0_8x8_q8_0  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 
 #ifdef __cplusplus
 }
diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index cbe377cf5caee6..1839a722ed00cb 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -658,7 +658,7 @@ static inline __m128i packNibbles( __m256i bytes ) {
 #endif  //__loongarch_asx
 
 // reference implementation for deterministic creation of model files
-void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int64_t k) {
+void quantize_row_q4_0_ref(const float * restrict x, block_q4_0 * restrict y, int64_t k) {
     static const int qk = QK4_0;
 
     assert(k % qk == 0);
@@ -696,11 +696,11 @@ void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict
 }
 
 void quantize_row_q4_0(const float * restrict x, void * restrict y, int64_t k) {
-    quantize_row_q4_0_reference(x, y, k);
+    quantize_row_q4_0_ref(x, y, k);
 }
 
 
-void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int64_t k) {
+void quantize_row_q4_1_ref(const float * restrict x, block_q4_1 * restrict y, int64_t k) {
     const int qk = QK4_1;
 
     assert(k % qk == 0);
@@ -738,10 +738,10 @@ void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict
 }
 
 void quantize_row_q4_1(const float * restrict x, void * restrict y, int64_t k) {
-    quantize_row_q4_1_reference(x, y, k);
+    quantize_row_q4_1_ref(x, y, k);
 }
 
-void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int64_t k) {
+void quantize_row_q5_0_ref(const float * restrict x, block_q5_0 * restrict y, int64_t k) {
     static const int qk = QK5_0;
 
     assert(k % qk == 0);
@@ -786,10 +786,10 @@ void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict
 }
 
 void quantize_row_q5_0(const float * restrict x, void * restrict y, int64_t k) {
-    quantize_row_q5_0_reference(x, y, k);
+    quantize_row_q5_0_ref(x, y, k);
 }
 
-void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int64_t k) {
+void quantize_row_q5_1_ref(const float * restrict x, block_q5_1 * restrict y, int64_t k) {
     const int qk = QK5_1;
 
     assert(k % qk == 0);
@@ -834,11 +834,11 @@ void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict
 }
 
 void quantize_row_q5_1(const float * restrict x, void * restrict y, int64_t k) {
-    quantize_row_q5_1_reference(x, y, k);
+    quantize_row_q5_1_ref(x, y, k);
 }
 
 // reference implementation for deterministic creation of model files
-void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int64_t k) {
+void quantize_row_q8_0_ref(const float * restrict x, block_q8_0 * restrict y, int64_t k) {
     assert(k % QK8_0 == 0);
     const int nb = k / QK8_0;
 
@@ -1144,12 +1144,12 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k)
 #else
     GGML_UNUSED(nb);
     // scalar
-    quantize_row_q8_0_reference(x, y, k);
+    quantize_row_q8_0_ref(x, y, k);
 #endif
 }
 
 // reference implementation for deterministic creation of model files
-void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int64_t k) {
+void quantize_row_q8_1_ref(const float * restrict x, block_q8_1 * restrict y, int64_t k) {
     assert(QK8_1 == 32);
     assert(k % QK8_1 == 0);
     const int nb = k / QK8_1;
@@ -1508,7 +1508,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k)
 #else
     GGML_UNUSED(nb);
     // scalar
-    quantize_row_q8_1_reference(x, y, k);
+    quantize_row_q8_1_ref(x, y, k);
 #endif
 }
 
@@ -1899,7 +1899,7 @@ static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t *
 
 //========================- 2-bit (de)-quantization
 
-void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int64_t k) {
+void quantize_row_q2_K_ref(const float * restrict x, block_q2_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -2002,7 +2002,7 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6
 }
 
 void quantize_row_q2_K(const float * restrict x, void * restrict vy, int64_t k) {
-    quantize_row_q2_K_reference(x, vy, k);
+    quantize_row_q2_K_ref(x, vy, k);
 }
 
 static float make_qkx3_quants(int n, int nmax, const float * restrict x, const float * restrict weights,
@@ -2226,7 +2226,7 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri
 size_t quantize_q2_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q2_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q2_K_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q2_K_ref(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
@@ -2241,7 +2241,7 @@ size_t quantize_q2_K(const float * restrict src, void * restrict dst, int64_t nr
 
 //========================= 3-bit (de)-quantization
 
-void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int64_t k) {
+void quantize_row_q3_K_ref(const float * restrict x, block_q3_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -2368,7 +2368,7 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int6
 }
 
 void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) {
-    quantize_row_q3_K_reference(x, vy, k);
+    quantize_row_q3_K_ref(x, vy, k);
 }
 
 static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int64_t n_per_row, const float * restrict quant_weights) {
@@ -2458,7 +2458,7 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri
 size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q3_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q3_K_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q3_K_ref(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
@@ -2473,7 +2473,7 @@ size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nr
 
 // ====================== 4-bit (de)-quantization
 
-void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int64_t k) {
+void quantize_row_q4_K_ref(const float * restrict x, block_q4_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
 
@@ -2572,7 +2572,7 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6
 void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_q4_K * restrict y = vy;
-    quantize_row_q4_K_reference(x, y, k);
+    quantize_row_q4_K_ref(x, y, k);
 }
 
 static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int64_t n_per_row, const float * quant_weights) {
@@ -2651,7 +2651,7 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri
 size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q4_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q4_K_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q4_K_ref(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
@@ -2666,7 +2666,7 @@ size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nr
 
 // ====================== 5-bit (de)-quantization
 
-void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int64_t k) {
+void quantize_row_q5_K_ref(const float * restrict x, block_q5_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int64_t nb = k / QK_K;
 
@@ -2783,7 +2783,7 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6
 void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_q5_K * restrict y = vy;
-    quantize_row_q5_K_reference(x, y, k);
+    quantize_row_q5_K_ref(x, y, k);
 }
 
 static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int64_t n_per_row, const float * quant_weights) {
@@ -2882,7 +2882,7 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri
 size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q5_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q5_K_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q5_K_ref(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
@@ -2897,7 +2897,7 @@ size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nr
 
 // ====================== 6-bit (de)-quantization
 
-void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int64_t k) {
+void quantize_row_q6_K_ref(const float * restrict x, block_q6_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int64_t nb = k / QK_K;
 
@@ -3001,7 +3001,7 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6
 void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_q6_K * restrict y = vy;
-    quantize_row_q6_K_reference(x, y, k);
+    quantize_row_q6_K_ref(x, y, k);
 }
 
 static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int64_t n_per_row, const float * quant_weights) {
@@ -3091,7 +3091,7 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri
 size_t quantize_q6_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     size_t row_size = ggml_row_size(GGML_TYPE_Q6_K, n_per_row);
     if (!quant_weights) {
-        quantize_row_q6_K_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q6_K_ref(src, dst, (int64_t)nrow*n_per_row);
     }
     else {
         char * qrow = (char *)dst;
@@ -3108,7 +3108,7 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri
     static_assert(QK4_0 == 32, "QK4_0 must be 32");
 
     if (!quant_weights) {
-        quantize_row_q4_0_reference(x, y, n_per_row);
+        quantize_row_q4_0_ref(x, y, n_per_row);
         return;
     }
 
@@ -3134,7 +3134,7 @@ static void quantize_row_q4_0_impl(const float * restrict x, block_q4_0 * restri
 
 size_t quantize_q4_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        quantize_row_q4_0_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q4_0_ref(src, dst, (int64_t)nrow*n_per_row);
         return nrow * ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q4_0, n_per_row);
@@ -3151,7 +3151,7 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri
     static_assert(QK4_1 == 32, "QK4_1 must be 32");
 
     if (!quant_weights) {
-        quantize_row_q4_1_reference(x, y, n_per_row);
+        quantize_row_q4_1_ref(x, y, n_per_row);
         return;
     }
 
@@ -3179,7 +3179,7 @@ static void quantize_row_q4_1_impl(const float * restrict x, block_q4_1 * restri
 
 size_t quantize_q4_1(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        quantize_row_q4_1_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q4_1_ref(src, dst, (int64_t)nrow*n_per_row);
         return nrow * ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q4_1, n_per_row);
@@ -3196,7 +3196,7 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri
     static_assert(QK5_0 == 32, "QK5_0 must be 32");
 
     if (!quant_weights) {
-        quantize_row_q5_0_reference(x, y, n_per_row);
+        quantize_row_q5_0_ref(x, y, n_per_row);
         return;
     }
 
@@ -3233,7 +3233,7 @@ static void quantize_row_q5_0_impl(const float * restrict x, block_q5_0 * restri
 
 size_t quantize_q5_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        quantize_row_q5_0_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q5_0_ref(src, dst, (int64_t)nrow*n_per_row);
         return nrow * ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q5_0, n_per_row);
@@ -3250,7 +3250,7 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri
     static_assert(QK5_1 == 32, "QK5_1 must be 32");
 
     if (!quant_weights) {
-        quantize_row_q5_1_reference(x, y, n_per_row);
+        quantize_row_q5_1_ref(x, y, n_per_row);
         return;
     }
 
@@ -3286,7 +3286,7 @@ static void quantize_row_q5_1_impl(const float * restrict x, block_q5_1 * restri
 
 size_t quantize_q5_1(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     if (!quant_weights) {
-        quantize_row_q5_1_reference(src, dst, (int64_t)nrow*n_per_row);
+        quantize_row_q5_1_ref(src, dst, (int64_t)nrow*n_per_row);
         return nrow * ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
     }
     size_t row_size = ggml_row_size(GGML_TYPE_Q5_1, n_per_row);
@@ -3302,7 +3302,7 @@ size_t quantize_q5_1(const float * restrict src, void * restrict dst, int64_t nr
 size_t quantize_q8_0(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
     (void)quant_weights; // not used
     const size_t row_size = ggml_row_size(GGML_TYPE_Q8_0, n_per_row);
-    quantize_row_q8_0_reference(src, dst, (int64_t)nrow*n_per_row);
+    quantize_row_q8_0_ref(src, dst, (int64_t)nrow*n_per_row);
     return nrow * row_size;
 }
 
@@ -3590,7 +3590,7 @@ void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y,
 
 //===================================== Q8_K ==============================================
 
-void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int64_t k) {
+void quantize_row_q8_K_ref(const float * restrict x, block_q8_K * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     const int64_t nb = k / QK_K;
 
@@ -3641,7 +3641,7 @@ void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int6
 }
 
 void quantize_row_q8_K(const float * restrict x, void * restrict y, int64_t k) {
-    quantize_row_q8_K_reference(x, y, k);
+    quantize_row_q8_K_ref(x, y, k);
 }
 
 //===================================== Dot ptoducts =================================
@@ -13530,10 +13530,10 @@ size_t quantize_iq3_xxs(const float * restrict src, void * restrict dst, int64_t
 void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq3_xxs * restrict y = vy;
-    quantize_row_iq3_xxs_reference(x, y, k);
+    quantize_row_iq3_xxs_ref(x, y, k);
 }
 
-void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int64_t k) {
+void quantize_row_iq3_xxs_ref(const float * restrict x, block_iq3_xxs * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     quantize_row_iq3_xxs_impl(256, x, y, k, NULL);
 }
@@ -13746,10 +13746,10 @@ size_t quantize_iq3_s(const float * restrict src, void * restrict dst, int64_t n
 void quantize_row_iq3_s(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq3_s * restrict y = vy;
-    quantize_row_iq3_s_reference(x, y, k);
+    quantize_row_iq3_s_ref(x, y, k);
 }
 
-void quantize_row_iq3_s_reference(const float * restrict x, block_iq3_s * restrict y, int64_t k) {
+void quantize_row_iq3_s_ref(const float * restrict x, block_iq3_s * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     quantize_iq3_s(x, y, 1, k, NULL);
 }
@@ -14487,7 +14487,7 @@ void quantize_row_iq4_nl(const float * restrict x, void * restrict vy, int64_t k
     }
 }
 
-void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * restrict y, int64_t k) {
+void quantize_row_iq4_nl_ref(const float * restrict x, block_iq4_nl * restrict y, int64_t k) {
     assert(k % QK4_NL == 0);
     quantize_row_iq4_nl(x, y, k);
 }
@@ -14515,10 +14515,10 @@ size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t
 void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq4_xs * restrict y = vy;
-    quantize_row_iq4_xs_reference(x, y, k);
+    quantize_row_iq4_xs_ref(x, y, k);
 }
 
-void quantize_row_iq4_xs_reference(const float * restrict x, block_iq4_xs * restrict y, int64_t k) {
+void quantize_row_iq4_xs_ref(const float * restrict x, block_iq4_xs * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     quantize_iq4_xs(x, y, 1, k, NULL);
 }
@@ -14705,7 +14705,7 @@ size_t quantize_iq2_s(const float * restrict src, void * restrict dst, int64_t n
     return nrow * nblock * sizeof(block_iq2_s);
 }
 
-void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restrict y, int64_t k) {
+void quantize_row_iq2_s_ref(const float * restrict x, block_iq2_s * restrict y, int64_t k) {
     assert(k % QK_K == 0);
     quantize_iq2_s(x, y, 1, k, NULL);
 }
@@ -14713,7 +14713,7 @@ void quantize_row_iq2_s_reference(const float * restrict x, block_iq2_s * restri
 void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k) {
     assert(k % QK_K == 0);
     block_iq2_s * restrict y = vy;
-    quantize_row_iq2_s_reference(x, y, k);
+    quantize_row_iq2_s_ref(x, y, k);
 }
 
 static bool validate_float(float f, size_t i) {
diff --git a/ggml/src/ggml-quants.h b/ggml/src/ggml-quants.h
index 30983b8728fa24..88b1f3269646d8 100644
--- a/ggml/src/ggml-quants.h
+++ b/ggml/src/ggml-quants.h
@@ -12,25 +12,25 @@ extern "C" {
 #endif
 
 // Quantization
-void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k);
-void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k);
-void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k);
-void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k);
-void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
-void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);
-
-void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
-void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
-void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
-void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k);
-void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k);
-void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k);
-
-void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k);
-void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl  * GGML_RESTRICT y, int64_t k);
-void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs  * GGML_RESTRICT y, int64_t k);
-void quantize_row_iq3_s_reference  (const float * GGML_RESTRICT x, block_iq3_s   * GGML_RESTRICT y, int64_t k);
-void quantize_row_iq2_s_reference  (const float * GGML_RESTRICT x, block_iq2_s   * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_1_ref(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_0_ref(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_1_ref(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_0_ref(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);
+
+void quantize_row_q2_K_ref(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q3_K_ref(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_K_ref(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_K_ref(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q6_K_ref(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_K_ref(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k);
+
+void quantize_row_iq3_xxs_ref(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_nl_ref (const float * GGML_RESTRICT x, block_iq4_nl  * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_xs_ref (const float * GGML_RESTRICT x, block_iq4_xs  * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq3_s_ref  (const float * GGML_RESTRICT x, block_iq3_s   * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq2_s_ref  (const float * GGML_RESTRICT x, block_iq2_s   * GGML_RESTRICT y, int64_t k);
 
 void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 1bb731e16d51dd..9a541478718474 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -592,7 +592,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = false,
         .to_float                 = (ggml_to_float_t) ggml_fp16_to_fp32_row,
         .from_float               = (ggml_from_float_t) ggml_fp32_to_fp16_row,
-        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .from_float_ref           = (ggml_from_float_t) ggml_fp32_to_fp16_row,
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f16,
         .vec_dot_type             = GGML_TYPE_F16,
         .nrows                    = 1,
@@ -604,7 +604,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_0,
         .from_float               = quantize_row_q4_0,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_0_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q4_0_ref,
         .vec_dot                  = ggml_vec_dot_q4_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
 #if defined (__ARM_FEATURE_MATMUL_INT8)
@@ -620,7 +620,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_1,
         .from_float               = quantize_row_q4_1,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_1_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q4_1_ref,
         .vec_dot                  = ggml_vec_dot_q4_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
 #if defined (__ARM_FEATURE_MATMUL_INT8)
@@ -636,7 +636,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = false,
         .to_float                 = NULL,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = NULL,
         .vec_dot_type             = GGML_TYPE_COUNT,
         .nrows                    = 1,
@@ -648,7 +648,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = false,
         .to_float                 = NULL,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = NULL,
         .vec_dot_type             = GGML_TYPE_COUNT,
         .nrows                    = 1,
@@ -660,7 +660,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_0,
         .from_float               = quantize_row_q5_0,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_0_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q5_0_ref,
         .vec_dot                  = ggml_vec_dot_q5_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
         .nrows                    = 1,
@@ -672,7 +672,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_1,
         .from_float               = quantize_row_q5_1,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_1_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q5_1_ref,
         .vec_dot                  = ggml_vec_dot_q5_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
         .nrows                    = 1,
@@ -684,7 +684,8 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q8_0,
         .from_float               = quantize_row_q8_0,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_0_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q8_0_ref,
+        .from_float_to_mat        = quantize_mat_q8_0,
         .vec_dot                  = ggml_vec_dot_q8_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
 #if defined (__ARM_FEATURE_MATMUL_INT8)
@@ -692,7 +693,6 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
 #else
         .nrows                    = 1,
 #endif
-        .from_float_to_mat        = quantize_mat_q8_0,
     },
     [GGML_TYPE_Q8_1] = {
         .type_name                = "q8_1",
@@ -700,7 +700,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .type_size                = sizeof(block_q8_1),
         .is_quantized             = true,
         .from_float               = quantize_row_q8_1,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_1_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q8_1_ref,
         .vec_dot_type             = GGML_TYPE_Q8_1,
         .nrows                    = 1,
     },
@@ -711,7 +711,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q2_K,
         .from_float               = quantize_row_q2_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q2_K_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q2_K_ref,
         .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -723,7 +723,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q3_K,
         .from_float               = quantize_row_q3_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q3_K_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q3_K_ref,
         .vec_dot                  = ggml_vec_dot_q3_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -735,7 +735,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q4_K,
         .from_float               = quantize_row_q4_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_K_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q4_K_ref,
         .vec_dot                  = ggml_vec_dot_q4_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -747,7 +747,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q5_K,
         .from_float               = quantize_row_q5_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_K_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q5_K_ref,
         .vec_dot                  = ggml_vec_dot_q5_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -759,7 +759,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_q6_K,
         .from_float               = quantize_row_q6_K,
-        .from_float_reference     = (ggml_from_float_t) quantize_row_q6_K_reference,
+        .from_float_ref           = (ggml_from_float_t) quantize_row_q6_K_ref,
         .vec_dot                  = ggml_vec_dot_q6_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -771,7 +771,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq2_xxs,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = ggml_vec_dot_iq2_xxs_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -783,7 +783,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq2_xs,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = ggml_vec_dot_iq2_xs_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -795,7 +795,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq3_xxs,
         .from_float               = quantize_row_iq3_xxs,
-        .from_float_reference     = (ggml_from_float_t)quantize_row_iq3_xxs_reference,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq3_xxs_ref,
         .vec_dot                  = ggml_vec_dot_iq3_xxs_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -807,7 +807,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq3_s,
         .from_float               = quantize_row_iq3_s,
-        .from_float_reference     = (ggml_from_float_t)quantize_row_iq3_s_reference,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq3_s_ref,
         .vec_dot                  = ggml_vec_dot_iq3_s_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -819,7 +819,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq2_s,
         .from_float               = quantize_row_iq2_s,
-        .from_float_reference     = (ggml_from_float_t)quantize_row_iq2_s_reference,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq2_s_ref,
         .vec_dot                  = ggml_vec_dot_iq2_s_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -831,7 +831,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq1_s,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = ggml_vec_dot_iq1_s_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -843,7 +843,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq1_m,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = ggml_vec_dot_iq1_m_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -855,7 +855,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq4_nl,
         .from_float               = quantize_row_iq4_nl,
-        .from_float_reference     = (ggml_from_float_t)quantize_row_iq4_nl_reference,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq4_nl_ref,
         .vec_dot                  = ggml_vec_dot_iq4_nl_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
         .nrows                    = 1,
@@ -867,7 +867,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = true,
         .to_float                 = (ggml_to_float_t) dequantize_row_iq4_xs,
         .from_float               = quantize_row_iq4_xs,
-        .from_float_reference     = (ggml_from_float_t)quantize_row_iq4_xs_reference,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_iq4_xs_ref,
         .vec_dot                  = ggml_vec_dot_iq4_xs_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
@@ -886,7 +886,7 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
         .is_quantized             = false,
         .to_float                 = (ggml_to_float_t) ggml_bf16_to_fp32_row,
         .from_float               = (ggml_from_float_t) ggml_fp32_to_bf16_row,
-        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_bf16_row,
+        .from_float_ref           = (ggml_from_float_t) ggml_fp32_to_bf16_row,
         .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_bf16,
         .vec_dot_type             = GGML_TYPE_BF16,
         .nrows                    = 1,
@@ -894,48 +894,48 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
     [GGML_TYPE_Q4_0_4_4] = {
         .type_name                = "q4_0_4x4",
         .blck_size                = QK4_0,
+        .blck_size_interleave     = 4,
         .type_size                = sizeof(block_q4_0),
         .is_quantized             = true,
         .to_float                 = NULL,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = NULL,
         .vec_dot_type             = GGML_TYPE_Q8_0,
         .nrows                    = 1,
         .ncols                    = 4,
-        .interleave_blcksize      = 4,
         .gemv                     = ggml_gemv_q4_0_4x4_q8_0,
         .gemm                     = ggml_gemm_q4_0_4x4_q8_0,
     },
     [GGML_TYPE_Q4_0_4_8] = {
         .type_name                = "q4_0_4x8",
         .blck_size                = QK4_0,
+        .blck_size_interleave     = 8,
         .type_size                = sizeof(block_q4_0),
         .is_quantized             = true,
         .to_float                 = NULL,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = NULL,
         .vec_dot_type             = GGML_TYPE_Q8_0,
         .nrows                    = 1,
         .ncols                    = 4,
-        .interleave_blcksize      = 8,
         .gemv                     = ggml_gemv_q4_0_4x8_q8_0,
         .gemm                     = ggml_gemm_q4_0_4x8_q8_0,
     },
     [GGML_TYPE_Q4_0_8_8] = {
         .type_name                = "q4_0_8x8",
         .blck_size                = QK4_0,
+        .blck_size_interleave     = 8,
         .type_size                = sizeof(block_q4_0),
         .is_quantized             = true,
         .to_float                 = NULL,
         .from_float               = NULL,
-        .from_float_reference     = NULL,
+        .from_float_ref           = NULL,
         .vec_dot                  = NULL,
         .vec_dot_type             = GGML_TYPE_Q8_0,
         .nrows                    = 1,
         .ncols                    = 8,
-        .interleave_blcksize      = 8,
         .gemv                     = ggml_gemv_q4_0_8x8_q8_0,
         .gemm                     = ggml_gemm_q4_0_8x8_q8_0,
     }
@@ -3115,7 +3115,7 @@ size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) {
     return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN);
 }
 
-GGML_CALL int ggml_blck_size(enum ggml_type type) {
+GGML_CALL int64_t ggml_blck_size(enum ggml_type type) {
     return type_traits[type].blck_size;
 }
 
@@ -12192,15 +12192,14 @@ static void ggml_compute_forward_mul_mat(
 
     const enum ggml_type type = src0->type;
 
-    enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
-    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
-    int64_t           const vec_dot_num_rows      = type_traits[type].nrows;
-    int64_t           const matmul_num_cols       = type_traits[type].ncols;
-    int64_t           const interleave_blcksize   = type_traits[type].interleave_blcksize;
-    ggml_from_float_to_mat_t const from_float_to_mat
-                                                  = type_traits[vec_dot_type].from_float_to_mat;
-    ggml_gemv_t       const gemv                  = type_traits[type].gemv;
-    ggml_gemm_t       const gemm                  = type_traits[type].gemm;
+    enum ggml_type           const vec_dot_type         = type_traits[type].vec_dot_type;
+    ggml_from_float_t        const from_float           = type_traits[vec_dot_type].from_float;
+    ggml_from_float_to_mat_t const from_float_to_mat    = type_traits[vec_dot_type].from_float_to_mat;
+    int64_t                  const vec_dot_num_rows     = type_traits[type].nrows;
+    int64_t                  const matmul_num_cols      = type_traits[type].ncols;
+    int64_t                  const blck_size_interleave = type_traits[type].blck_size_interleave;
+    ggml_gemv_t              const gemv                 = type_traits[type].gemv;
+    ggml_gemm_t              const gemm                 = type_traits[type].gemm;
 
     GGML_ASSERT(ne0 == ne01);
     GGML_ASSERT(ne1 == ne11);
@@ -12264,14 +12263,14 @@ UseGgmlGemm1:;
                     for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
                         from_float_to_mat((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
                                           (void *)               (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
-                                          4, ne10, interleave_blcksize);
+                                          4, ne10, blck_size_interleave);
                     }
                     i11_processed = ne11 - ne11 % 4;
                 }
                 for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) {
-                    from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
-                                          (void *)               (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
-                                           ne10);
+                    from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
+                           (void *)               (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
+                           ne10);
                 }
             }
         }
@@ -12355,7 +12354,7 @@ UseGgmlGemm2:;
         int64_t src0_start = (ith * ne01) / nth;
         int64_t src0_end   = ((ith + 1) * ne01) / nth;
         src0_start = (src0_start % matmul_num_cols) ? src0_start + matmul_num_cols - (src0_start % matmul_num_cols): src0_start;
-        src0_end   = (src0_end % matmul_num_cols) ? src0_end + matmul_num_cols - (src0_end % matmul_num_cols): src0_end;
+        src0_end   = (src0_end   % matmul_num_cols) ? src0_end   + matmul_num_cols - (src0_end   % matmul_num_cols): src0_end;
         if (src0_start >= src0_end) return;
 
         // If there are more than three rows in src1, use gemm; otherwise, use gemv.
@@ -12413,11 +12412,11 @@ static void ggml_compute_forward_mul_mat_id(
 
     const bool src1_cont = ggml_is_contiguous(src1);
 
-    ggml_vec_dot_t    const vec_dot               = type_traits[type].vec_dot;
-    enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
-    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
-    int64_t           const matmul_num_cols       = type_traits[type].ncols;
-    ggml_gemv_t       const gemv                  = type_traits[type].gemv;
+    ggml_vec_dot_t    const vec_dot         = type_traits[type].vec_dot;
+    enum ggml_type    const vec_dot_type    = type_traits[type].vec_dot_type;
+    ggml_from_float_t const from_float      = type_traits[vec_dot_type].from_float;
+    int64_t           const matmul_num_cols = type_traits[type].ncols;
+    ggml_gemv_t       const gemv            = type_traits[type].gemv;
 
     // we don't support permuted src0 or src1
     GGML_ASSERT(nb00 == ggml_type_size(type));
@@ -12458,9 +12457,9 @@ static void ggml_compute_forward_mul_mat_id(
         for (int64_t i13 = 0; i13 < ne13; ++i13) {
             for (int64_t i12 = 0; i12 < ne12; ++i12) {
                 for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
-                    from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
-                                          (void *)               (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
-                                           ne10);
+                    from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
+                               (void *)               (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
+                               ne10);
                 }
             }
         }
@@ -21063,8 +21062,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                 (int64_t) info->ne[3];
 
             if (ne % ggml_blck_size(info->type) != 0) {
-                fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%d)\n",
-                        __func__, info->name.data, (int)info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
+                fprintf(stderr, "%s: tensor '%s' of type %d (%s) number of elements (%" PRId64 ") is not a multiple of block size (%" PRId64 ")\n",
+                        __func__, info->name.data, (int) info->type, ggml_type_name(info->type), ne, ggml_blck_size(info->type));
                 fclose(file);
                 gguf_free(ctx);
                 return NULL;
diff --git a/tests/test-double-float.cpp b/tests/test-double-float.cpp
index 753dae911b0cb3..6aac4737a7df0f 100644
--- a/tests/test-double-float.cpp
+++ b/tests/test-double-float.cpp
@@ -14,7 +14,7 @@
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wdouble-promotion"
 
-// ggml.c::quantize_row_q4_0_reference
+// ggml.c::quantize_row_q4_0_ref
 inline static uint8_t round_orig(float v0) { return ((int8_t) (round(v0))) + 8; }
 
 // ggml.c::ggml_silu_f32
@@ -24,7 +24,7 @@ inline static float silu_orig(float x) {
 
 #pragma GCC diagnostic pop
 
-// ggml.c::quantize_row_q4_0_reference
+// ggml.c::quantize_row_q4_0_ref
 inline static uint8_t round_float(float v0) { return (int8_t)roundf(v0) + 8; }
 
 // ggml.c::ggml_silu_f32
diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp
index e690ac6c85a71d..c97458d1df2b8c 100644
--- a/tests/test-quantize-fns.cpp
+++ b/tests/test-quantize-fns.cpp
@@ -60,7 +60,7 @@ static float reference_quantization_error(ggml_type_traits_t & qfns, size_t test
     qfns.from_float(test_data, tmp_q.data(), test_size);
     qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
 
-    qfns.from_float_reference(test_data, tmp_q.data(), test_size);
+    qfns.from_float_ref(test_data, tmp_q.data(), test_size);
     qfns.to_float(tmp_q.data(), tmp_out_ref.data(), test_size);
 
     return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size);
diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp
index 48d9fae3dc06e2..24e066053219a1 100644
--- a/tests/test-quantize-perf.cpp
+++ b/tests/test-quantize-perf.cpp
@@ -285,7 +285,7 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void) -> float {
-                        qfns.from_float_reference(test_data1, test_q1, size);
+                        qfns.from_float_ref(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = ggml_row_size(type, size);

From 71c1121d11f1437be9421fd0cbaa011b9ef49098 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 12 Jul 2024 10:46:14 +0300
Subject: [PATCH 037/125] examples : sprintf -> snprintf (#8434)

* examples : sprintf -> snprintf

ggml-ci

* examples : use sizeof() instead of hardcoded constants
---
 examples/eval-callback/eval-callback.cpp |  2 +-
 examples/gguf-hash/gguf-hash.cpp         | 14 +++++++-------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp
index 64cd338c263513..c8a3016a4d80ac 100644
--- a/examples/eval-callback/eval-callback.cpp
+++ b/examples/eval-callback/eval-callback.cpp
@@ -99,7 +99,7 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
 
     char src1_str[128] = {0};
     if (src1) {
-        sprintf(src1_str, "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
+        snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
     }
 
     printf("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__,
diff --git a/examples/gguf-hash/gguf-hash.cpp b/examples/gguf-hash/gguf-hash.cpp
index c34728c3df4bea..e96c75117f5335 100644
--- a/examples/gguf-hash/gguf-hash.cpp
+++ b/examples/gguf-hash/gguf-hash.cpp
@@ -347,7 +347,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
                 char hex_result[17];
                 for (int  offset = 0; offset < 8; offset++) {
                     unsigned int shift_bits_by = (8 * (8 - offset - 1));
-                    sprintf( ( hex_result + (2*offset)), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff);
+                    snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff);
                 }
 
                 if (hash_params.manifest_is_usable) {
@@ -384,7 +384,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
 
                 char hex_result[41] = {0};
                 for (int  offset = 0; offset < 20; offset++) {
-                    sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff);
+                    snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
                 }
 
                 if (hash_params.manifest_is_usable) {
@@ -421,7 +421,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
 
                 char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0};
                 for (int  offset = 0; offset < SHA256_DIGEST_SIZE; offset++) {
-                    sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff);
+                    snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
                 }
 
                 if (hash_params.manifest_is_usable) {
@@ -460,7 +460,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
         char hex_result[17];
         for (int  offset = 0; offset < 8; offset++) {
             unsigned int shift_bits_by = (8 * (8 - offset - 1));
-            sprintf( ( hex_result + (2*offset)), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff);
+            snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", (unsigned char) (hash >> shift_bits_by)&0xff);
         }
 
         if (hash_params.manifest_is_usable) {
@@ -490,7 +490,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
 
         char hex_result[41];
         for (int  offset = 0; offset < 20; offset++) {
-            sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff);
+            snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
         }
 
         if (hash_params.manifest_is_usable) {
@@ -520,7 +520,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
 
         char hex_result[SHA256_DIGEST_SIZE * 2 + 1] = {0};
         for (int  offset = 0; offset < SHA256_DIGEST_SIZE; offset++) {
-            sprintf( ( hex_result + (2*offset)), "%02x", result[offset]&0xff);
+            snprintf( ( hex_result + (2*offset)), sizeof(hex_result) - (2*offset), "%02x", result[offset]&0xff);
         }
 
         if (hash_params.manifest_is_usable) {
@@ -552,7 +552,7 @@ static hash_exit_code_t gguf_hash(const hash_params & hash_params) {
         generate_uuidv5(result, uuid);
 
         char string_buffer[37] = {0};
-        sprintf(string_buffer, "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x",
+        snprintf(string_buffer, sizeof(string_buffer), "%02x%02x%02x%02x-%02x%02x-%02x%02x-%02x%02x-%02x%02x%02x%02x%02x%02x",
             uuid[0], uuid[1], uuid[2], uuid[3],
             uuid[4], uuid[5], uuid[6], uuid[7],
             uuid[8], uuid[9], uuid[10], uuid[11],

From 5aefbce27a66473d4b1263ba3f7bdd3d14245975 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ji=C5=99=C3=AD=20Podiv=C3=ADn?=
 <66251151+jpodivin@users.noreply.github.com>
Date: Fri, 12 Jul 2024 10:06:33 +0200
Subject: [PATCH 038/125] convert : remove fsep token from GPTRefactForCausalLM
 (#8237)

The <filename> token used by Refact doesn't serve
the same purpose as the <file_separator> from CodeGemma.

Signed-off-by: Jiri Podivin <jpodivin@redhat.com>
---
 convert_hf_to_gguf.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index ebb5ca376133b9..cf930be17a6e07 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -1203,11 +1203,10 @@ def set_vocab(self):
 
         # TODO: how to determine special FIM tokens automatically?
         special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
-                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
+                                          special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
         special_vocab._set_special_token("prefix", 1)
         special_vocab._set_special_token("suffix", 3)
         special_vocab._set_special_token("middle", 2)
-        special_vocab._set_special_token("fsep",   4) # is this correct?
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):

From 8a4441ea1a2564578134404f31158c318e9c0bf3 Mon Sep 17 00:00:00 2001
From: Armen Kaleshian <kriation@users.noreply.github.com>
Date: Fri, 12 Jul 2024 04:08:19 -0400
Subject: [PATCH 039/125] docker : fix filename for convert-hf-to-gguf.py in
 tools.sh (#8441)

Commit b0a4699 changed the name of this script from convert-hf-to-gguf.py to
convert_hf_to_gguf.py breaking how convert is called from within a Docker
container.
---
 .devops/tools.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.devops/tools.sh b/.devops/tools.sh
index 335382f69ee1f8..cf0e8f32d738c0 100755
--- a/.devops/tools.sh
+++ b/.devops/tools.sh
@@ -8,7 +8,7 @@ arg1="$1"
 shift
 
 if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
-    python3 ./convert-hf-to-gguf.py "$@"
+    python3 ./convert_hf_to_gguf.py "$@"
 elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
     ./llama-quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then

From c3ebcfa148e867a68e78fd5c4f0c23e8f84c788b Mon Sep 17 00:00:00 2001
From: Douglas Hanley <thesecretaryofwar@gmail.com>
Date: Fri, 12 Jul 2024 03:14:12 -0500
Subject: [PATCH 040/125] server : ensure batches are either all embed or all
 completion (#8420)

* make sure batches are all embed or all non-embed

* non-embedding batch for sampled tokens; fix unused params warning
---
 examples/server/server.cpp | 38 ++++++++++++++++++++++++++++++++------
 1 file changed, 32 insertions(+), 6 deletions(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index efd426289a54cb..badeb9121324fa 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -2005,6 +2005,11 @@ struct server_context {
         int32_t n_batch  = llama_n_batch(ctx);
         int32_t n_ubatch = llama_n_ubatch(ctx);
 
+        // track if this is an embedding or non-embedding batch
+        // if we've added sampled tokens above, we are in non-embedding mode
+        // -1: none, 0: non-embedding, 1: embedding
+        int32_t batch_type = batch.n_tokens > 0 ? 0 : -1;
+
         // next, batch any pending prompts without exceeding n_batch
         if (params.cont_batching || batch.n_tokens == 0) {
             for (auto & slot : slots) {
@@ -2175,6 +2180,14 @@ struct server_context {
                         }
                     }
 
+                    // check that we are in the right batch_type, if not defer the slot
+                    bool slot_type = slot.embedding ? 1 : 0;
+                    if (batch_type == -1) {
+                        batch_type = slot_type;
+                    } else if (batch_type != slot_type) {
+                        continue;
+                    }
+
                     // keep only the common part
                     int p0 = (int) system_tokens.size() + slot.n_past;
                     if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) {
@@ -2276,6 +2289,9 @@ struct server_context {
             {"n_tokens", batch.n_tokens},
         });
 
+        // make sure we're in the right embedding mode
+        llama_set_embeddings(ctx, batch_type == 1);
+
         // process the created batch of tokens
         for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
             const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
@@ -2990,6 +3006,11 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_completions = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+        if (ctx_server.params.embedding) {
+            res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
 
         json data = json::parse(req.body);
@@ -3085,6 +3106,11 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_chat_completions = [&ctx_server, &params, &res_error](const httplib::Request & req, httplib::Response & res) {
+        if (ctx_server.params.embedding) {
+            res_error(res, format_error_response("This server does not support chat completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
         json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template);
 
@@ -3157,6 +3183,11 @@ int main(int argc, char ** argv) {
     };
 
     const auto handle_infill = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+        if (ctx_server.params.embedding) {
+            res_error(res, format_error_response("This server does not support infill. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
 
         json data = json::parse(req.body);
@@ -3243,13 +3274,8 @@ int main(int argc, char ** argv) {
         return res.set_content(data.dump(), "application/json; charset=utf-8");
     };
 
-    const auto handle_embeddings = [&params, &ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_embeddings = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
-        if (!params.embedding) {
-            res.status = 501;
-            res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
-            return;
-        }
 
         const json body = json::parse(req.body);
         bool is_openai = false;

From f53226245f421bd01b47cce43a47e791de82c636 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 12 Jul 2024 11:05:21 +0200
Subject: [PATCH 041/125] llama : suppress unary minus operator warning (#8448)

This commit updates the _try_copy lambda and moves the unary minus
operator to after the cast to int32_t.

The motivation for this that currently the following warning is
generated on windows:

```console
llama.cpp\src\llama.cpp(21147,30): warning C4146: unary minus operator
applied to unsigned type, result still unsigned
```
---
 src/llama.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index f91ac777969d98..59b76a6d80cdf8 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -21144,7 +21144,7 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
             size--;
         }
         if (length < (int32_t)size) {
-            return (int32_t) -size;
+            return -(int32_t) size;
         }
         memcpy(buf, token, size);
         return (int32_t) size;

From 6af51c0d96e6268769fc05c98d5b1a5e832c0017 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 12 Jul 2024 14:48:04 +0300
Subject: [PATCH 042/125] main : print error on empty input (#8456)

---
 examples/main/main.cpp | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 4ef55c1e6b3cf8..a0d817b1a89d14 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -289,8 +289,13 @@ int main(int argc, char ** argv) {
 
     // Should not run without any tokens
     if (embd_inp.empty()) {
-        embd_inp.push_back(llama_token_bos(model));
-        LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+        if (add_bos) {
+            embd_inp.push_back(llama_token_bos(model));
+            LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+        } else {
+            LOG_TEE("error: input is empty\n");
+            return -1;
+        }
     }
 
     // Tokenize negative prompt

From 4e24cffd8cccd653634e24ee461c252bd77b1426 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 12 Jul 2024 14:48:15 +0300
Subject: [PATCH 043/125] server : handle content array in chat API (#8449)

* server : handle content array in chat API

* Update examples/server/utils.hpp

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>

---------

Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
---
 examples/server/utils.hpp | 22 ++++++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index 7ef2a519a10c76..db6b3b74d1dd2f 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -122,8 +122,26 @@ inline std::string format_chat(const struct llama_model * model, const std::stri
 
     for (size_t i = 0; i < messages.size(); ++i) {
         const auto & curr_msg = messages[i];
-        std::string role    = json_value(curr_msg, "role",    std::string(""));
-        std::string content = json_value(curr_msg, "content", std::string(""));
+
+        std::string role = json_value(curr_msg, "role", std::string(""));
+
+        std::string content;
+        if (curr_msg.contains("content")) {
+            if (curr_msg["content"].is_string()) {
+                content = curr_msg["content"].get<std::string>();
+            } else if (curr_msg["content"].is_array()) {
+                for (const auto & part : curr_msg["content"]) {
+                    if (part.contains("text")) {
+                        content += "\n" + part["text"].get<std::string>();
+                    }
+                }
+            } else {
+                throw std::runtime_error("Invalid 'content' type (ref: https://github.com/ggerganov/llama.cpp/issues/8367)");
+            }
+        } else {
+            throw std::runtime_error("Missing 'content' (ref: https://github.com/ggerganov/llama.cpp/issues/8367)");
+        }
+
         chat.push_back({role, content});
     }
 

From c917b67f06c42d8ca8391b9bc73f5fe62c83bf70 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 13 Jul 2024 18:32:33 +0300
Subject: [PATCH 044/125] metal : template-ify some of the kernels (#8447)

ggml-ci
---
 ggml/src/ggml-metal.m     |  28 +-
 ggml/src/ggml-metal.metal | 729 ++++++++++----------------------------
 2 files changed, 193 insertions(+), 564 deletions(-)

diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m
index 79902c9a80616a..b5939efa6c279a 100644
--- a/ggml/src/ggml-metal.m
+++ b/ggml/src/ggml-metal.m
@@ -193,16 +193,16 @@
   //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,     // https://github.com/ggerganov/llama.cpp/issues/7261
     GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
   //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261
-    GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
     GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
+    GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
+    GGML_METAL_KERNEL_TYPE_CPY_F16_F16,
+    GGML_METAL_KERNEL_TYPE_CPY_F16_F32,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,
     GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,
     GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,
-    GGML_METAL_KERNEL_TYPE_CPY_F16_F16,
-    GGML_METAL_KERNEL_TYPE_CPY_F16_F32,
     GGML_METAL_KERNEL_TYPE_CONCAT,
     GGML_METAL_KERNEL_TYPE_SQR,
     GGML_METAL_KERNEL_TYPE_SUM_ROWS,
@@ -651,14 +651,14 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
       //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,   flash_attn_ext_vec_f16_h256,    ctx->support_simdgroup_reduction);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16,                   cpy_f32_f16,                    true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32,                   cpy_f32_f32,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16,                   cpy_f16_f16,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32,                   cpy_f16_f32,                    true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,                  cpy_f32_q8_0,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,                  cpy_f32_q4_0,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,                  cpy_f32_q4_1,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,                  cpy_f32_q5_0,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,                  cpy_f32_q5_1,                   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,                cpy_f32_iq4_nl,                 true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16,                   cpy_f16_f16,                    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32,                   cpy_f16_f32,                    true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT,                        concat,                         true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR,                           sqr,                            true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                      sum_rows,                       true);
@@ -810,8 +810,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
                 switch (op->src[0]->type) {
                     case GGML_TYPE_F32:
                         switch (op->type) {
-                           case GGML_TYPE_F16:
                            case GGML_TYPE_F32:
+                           case GGML_TYPE_F16:
                            case GGML_TYPE_Q8_0:
                            case GGML_TYPE_Q4_0:
                            case GGML_TYPE_Q4_1:
@@ -824,8 +824,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
                         }
                     case GGML_TYPE_F16:
                         switch (op->type) {
-                           case GGML_TYPE_F16:
                            case GGML_TYPE_F32:
+                           case GGML_TYPE_F16:
                                 return true;
                            default:
                                 return false;
@@ -837,7 +837,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_GET_ROWS:
             {
-                return op->src[0]->type != GGML_TYPE_BF16 && op->ne[3] == 1;
+                return op->ne[3] == 1;
             }
         default:
             return false;
@@ -1580,8 +1580,8 @@ static enum ggml_status ggml_metal_graph_compute(
                             // some Metal matrix data types require aligned pointers
                             // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
                             switch (src0->type) {
-                                case GGML_TYPE_F32: GGML_ASSERT(nb01 % 16 == 0); break;
-                                case GGML_TYPE_F16: GGML_ASSERT(nb01 % 8  == 0); break;
+                                case GGML_TYPE_F32:  GGML_ASSERT(nb01 % 16 == 0); break;
+                                case GGML_TYPE_F16:  GGML_ASSERT(nb01 % 8  == 0); break;
                                 default: break;
                             }
 
@@ -2775,8 +2775,8 @@ static enum ggml_status ggml_metal_graph_compute(
                                     GGML_ASSERT(ne0 % ggml_blck_size(dst->type) == 0);
 
                                     switch (dstt) {
-                                        case GGML_TYPE_F16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline;  break;
-                                        case GGML_TYPE_F32:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline;  break;
+                                        case GGML_TYPE_F32:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F32].pipeline; break;
+                                        case GGML_TYPE_F16:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_F16].pipeline; break;
                                         case GGML_TYPE_Q8_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0].pipeline; break;
                                         case GGML_TYPE_Q4_0:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0].pipeline; break;
                                         case GGML_TYPE_Q4_1:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1].pipeline; break;
@@ -2789,8 +2789,8 @@ static enum ggml_status ggml_metal_graph_compute(
                             case GGML_TYPE_F16:
                                 {
                                     switch (dstt) {
-                                        case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break;
-                                        case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break;
+                                        case GGML_TYPE_F32:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F32].pipeline; break;
+                                        case GGML_TYPE_F16:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CPY_F16_F16].pipeline; break;
                                         default: GGML_ASSERT(false && "not implemented");
                                     };
                                 } break;
diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal.metal
index c3503479b35bac..2a3b0c0a69a746 100644
--- a/ggml/src/ggml-metal.metal
+++ b/ggml/src/ggml-metal.metal
@@ -1219,9 +1219,10 @@ kernel void kernel_mul_mv_q8_0_f32(
     kernel_mul_mv_q8_0_f32_impl(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,nullptr,tgpig,tiisg,sgitg);
 }
 
-#define N_F32_F32 4
+#define N_MV_T_T 4
 
-void kernel_mul_mv_f32_f32_impl(
+template<typename T0, typename T04, typename T1, typename T14>
+void kernel_mul_mv_impl(
         device const  char * src0,
         device const  char * src1,
         device       float * dst,
@@ -1239,13 +1240,12 @@ void kernel_mul_mv_f32_f32_impl(
                   uint64_t   nb12,
                    int64_t   ne0,
                    int64_t   ne1,
-                     uint    r2,
-                     uint    r3,
-                     uint3   tgpig,
-                     uint    tiisg) {
-
+                   uint      r2,
+                   uint      r3,
+                   uint3     tgpig,
+                   uint      tiisg) {
     const int64_t r0 = tgpig.x;
-    const int64_t rb = tgpig.y*N_F32_F32;
+    const int64_t rb = tgpig.y*N_MV_T_T;
     const int64_t im = tgpig.z;
 
     const uint i12 = im%ne12;
@@ -1253,20 +1253,20 @@ void kernel_mul_mv_f32_f32_impl(
 
     const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
 
-    device const float * x = (device const float *) (src0 + offset0);
+    device const T0 * x = (device const T0 *) (src0 + offset0);
 
     if (ne00 < 128) {
-        for (int row = 0; row < N_F32_F32; ++row) {
+        for (int row = 0; row < N_MV_T_T; ++row) {
             int r1 = rb + row;
             if (r1 >= ne11) {
                 break;
             }
 
-            device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
+            device const T1 * y = (device const T1 *) (src1 + r1*nb11 + im*nb12);
 
             float sumf = 0;
             for (int i = tiisg; i < ne00; i += 32) {
-                sumf += (float) x[i] * (float) y[i];
+                sumf += (T0) x[i] * (T1) y[i];
             }
 
             float all_sum = simd_sum(sumf);
@@ -1275,32 +1275,32 @@ void kernel_mul_mv_f32_f32_impl(
             }
         }
     } else {
-        device const float4 * x4 = (device const float4 *)x;
-        for (int row = 0; row < N_F32_F32; ++row) {
+        device const T04 * x4 = (device const T04 *) x;
+        for (int row = 0; row < N_MV_T_T; ++row) {
             int r1 = rb + row;
             if (r1 >= ne11) {
                 break;
             }
 
-            device const float  * y  = (device const float  *) (src1 + r1*nb11 + im*nb12);
-            device const float4 * y4 = (device const float4 *) y;
+            device const T1  * y  = (device const T1  *) (src1 + r1*nb11 + im*nb12);
+            device const T14 * y4 = (device const T14 *) y;
 
             float sumf = 0;
             for (int i = tiisg; i < ne00/4; i += 32) {
-                for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
+                for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]);
             }
 
             float all_sum = simd_sum(sumf);
             if (tiisg == 0) {
-                for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
+                for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]);
                 dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
             }
         }
     }
 }
 
-[[host_name("kernel_mul_mv_f32_f32")]]
-kernel void kernel_mul_mv_f32_f32(
+template<typename T0, typename T04, typename T1, typename T14>
+kernel void kernel_mul_mv(
         device const  char * src0,
         device const  char * src1,
         device       float * dst,
@@ -1322,90 +1322,38 @@ kernel void kernel_mul_mv_f32_f32(
         constant   uint    & r3,
         uint3 tgpig[[threadgroup_position_in_grid]],
         uint  tiisg[[thread_index_in_simdgroup]]) {
-    kernel_mul_mv_f32_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
+    kernel_mul_mv_impl<T0, T04, T1, T14>(
+        src0,
+        src1,
+        dst,
+        ne00,
+        ne01,
+        ne02,
+        nb00,
+        nb01,
+        nb02,
+        ne10,
+        ne11,
+        ne12,
+        nb10,
+        nb11,
+        nb12,
+        ne0,
+        ne1,
+        r2,
+        r3,
+        tgpig,
+        tiisg);
 }
 
-#define N_F16_F16 4
+typedef decltype(kernel_mul_mv<half, half4, half, half4>) mul_mv_t;
 
-kernel void kernel_mul_mv_f16_f16(
-        device const  char * src0,
-        device const  char * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne11,
-        constant   int64_t & ne12,
-        constant  uint64_t & nb10,
-        constant  uint64_t & nb11,
-        constant  uint64_t & nb12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]]) {
+template [[host_name("kernel_mul_mv_f32_f32")]]   kernel mul_mv_t kernel_mul_mv<float,  float4,  float,  float4>;
+template [[host_name("kernel_mul_mv_f16_f32")]]   kernel mul_mv_t kernel_mul_mv<half,   half4,   float,  float4>;
+template [[host_name("kernel_mul_mv_f16_f16")]]   kernel mul_mv_t kernel_mul_mv<half,   half4,   half,   half4>;
 
-    const int64_t r0 = tgpig.x;
-    const int64_t rb = tgpig.y*N_F16_F16;
-    const int64_t im = tgpig.z;
-
-    const uint i12 = im%ne12;
-    const uint i13 = im/ne12;
-
-    const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
-
-    device const half * x = (device const half *) (src0 + offset0);
-
-    if (ne00 < 128) {
-        for (int row = 0; row < N_F16_F16; ++row) {
-            int r1 = rb + row;
-            if (r1 >= ne11) {
-                break;
-            }
-
-            device const half * y = (device const half *) (src1 + r1*nb11 + im*nb12);
-
-            float sumf = 0;
-            for (int i = tiisg; i < ne00; i += 32) {
-                sumf += (half) x[i] * (half) y[i];
-            }
-
-            float all_sum = simd_sum(sumf);
-            if (tiisg == 0) {
-                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
-            }
-        }
-    } else {
-        device const half4 * x4 = (device const half4 *)x;
-        for (int row = 0; row < N_F16_F16; ++row) {
-            int r1 = rb + row;
-            if (r1 >= ne11) {
-                break;
-            }
-
-            device const half  * y  = (device const half  *) (src1 + r1*nb11 + im*nb12);
-            device const half4 * y4 = (device const half4 *) y;
-
-            float sumf = 0;
-            for (int i = tiisg; i < ne00/4; i += 32) {
-                for (int k = 0; k < 4; ++k) sumf += (half) x4[i][k] * y4[i][k];
-            }
-
-            float all_sum = simd_sum(sumf);
-            if (tiisg == 0) {
-                for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (half) x[i] * y[i];
-                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
-            }
-        }
-    }
-}
-
-void kernel_mul_mv_f16_f32_1row_impl(
+template<typename T, typename T4>
+kernel void kernel_mul_mv_1row(
         device const  char * src0,
         device const  char * src1,
         device       float * dst,
@@ -1437,7 +1385,7 @@ void kernel_mul_mv_f16_f32_1row_impl(
 
     const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
 
-    device const half  * x = (device const half  *) (src0 + offset0);
+    device const T     * x = (device const T     *) (src0 + offset0);
     device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
 
     float sumf = 0;
@@ -1450,153 +1398,29 @@ void kernel_mul_mv_f16_f32_1row_impl(
             dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
         }
     } else {
-        device const half4  * x4 = (device const half4  *) x;
+        device const T4     * x4 = (device const T4     *) x;
         device const float4 * y4 = (device const float4 *) y;
+
         for (int i = tiisg; i < ne00/4; i += 32) {
-            for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k];
+            for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]);
         }
+
         float all_sum = simd_sum(sumf);
+
         if (tiisg == 0) {
-            for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
+            for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) (x[i] * y[i]);
             dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
         }
     }
 }
 
-[[host_name("kernel_mul_mv_f16_f32_1row")]]
-kernel void kernel_mul_mv_f16_f32_1row(
-        device const  char * src0,
-        device const  char * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne11,
-        constant   int64_t & ne12,
-        constant  uint64_t & nb10,
-        constant  uint64_t & nb11,
-        constant  uint64_t & nb12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint  tiisg[[thread_index_in_simdgroup]]) {
-    kernel_mul_mv_f16_f32_1row_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
-}
-
-#define N_F16_F32 4
-
-void kernel_mul_mv_f16_f32_impl(
-        device const  char * src0,
-        device const  char * src1,
-        device       float * dst,
-                   int64_t   ne00,
-                   int64_t   ne01,
-                   int64_t   ne02,
-                  uint64_t   nb00,
-                  uint64_t   nb01,
-                  uint64_t   nb02,
-                   int64_t   ne10,
-                   int64_t   ne11,
-                   int64_t   ne12,
-                  uint64_t   nb10,
-                  uint64_t   nb11,
-                  uint64_t   nb12,
-                   int64_t   ne0,
-                   int64_t   ne1,
-                   uint      r2,
-                   uint      r3,
-                   uint3     tgpig,
-                   uint      tiisg) {
-
-    const int64_t r0 = tgpig.x;
-    const int64_t rb = tgpig.y*N_F16_F32;
-    const int64_t im = tgpig.z;
-
-    const uint i12 = im%ne12;
-    const uint i13 = im/ne12;
-
-    const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
-
-    device const half * x = (device const half *) (src0 + offset0);
-
-    if (ne00 < 128) {
-        for (int row = 0; row < N_F16_F32; ++row) {
-            int r1 = rb + row;
-            if (r1 >= ne11) {
-                break;
-            }
-
-            device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12);
-
-            float sumf = 0;
-            for (int i = tiisg; i < ne00; i += 32) {
-                sumf += (float) x[i] * (float) y[i];
-            }
-
-            float all_sum = simd_sum(sumf);
-            if (tiisg == 0) {
-                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
-            }
-        }
-    } else {
-        device const half4 * x4 = (device const half4 *)x;
-        for (int row = 0; row < N_F16_F32; ++row) {
-            int r1 = rb + row;
-            if (r1 >= ne11) {
-                break;
-            }
-
-            device const float  * y  = (device const float  *) (src1 + r1*nb11 + im*nb12);
-            device const float4 * y4 = (device const float4 *) y;
-
-            float sumf = 0;
-            for (int i = tiisg; i < ne00/4; i += 32) {
-                for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
-            }
+typedef decltype(kernel_mul_mv_1row<half, half4>) mul_mv_1row_t;
 
-            float all_sum = simd_sum(sumf);
-            if (tiisg == 0) {
-                for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i];
-                dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
-            }
-        }
-    }
-}
-
-[[host_name("kernel_mul_mv_f16_f32")]]
-kernel void kernel_mul_mv_f16_f32(
-        device const  char * src0,
-        device const  char * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant   int64_t & ne10,
-        constant   int64_t & ne11,
-        constant   int64_t & ne12,
-        constant  uint64_t & nb10,
-        constant  uint64_t & nb11,
-        constant  uint64_t & nb12,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   uint    & r2,
-        constant   uint    & r3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint tiisg[[thread_index_in_simdgroup]]) {
-    kernel_mul_mv_f16_f32_impl(src0, src1, dst, ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, nb10, nb11, nb12, ne0, ne1, r2, r3, tgpig, tiisg);
-}
+template [[host_name("kernel_mul_mv_f16_f32_1row")]]  kernel mul_mv_1row_t kernel_mul_mv_1row<half,   half4>;
 
 // Assumes row size (ne00) is a multiple of 4
-kernel void kernel_mul_mv_f16_f32_l4(
+template<typename T, typename T4>
+kernel void kernel_mul_mv_l4(
         device const  char * src0,
         device const  char * src1,
         device       float * dst,
@@ -1628,14 +1452,14 @@ kernel void kernel_mul_mv_f16_f32_l4(
 
     const uint offset0 = r0*nb01 + (i12/r2)*nb02 + (i13/r3)*nb02*ne02;
 
-    device const half4 * x4 = (device const half4 *) (src0 + offset0);
+    device const T4 * x4 = (device const T4 *) (src0 + offset0);
 
     for (int r1 = 0; r1 < nrows; ++r1) {
         device const float4 * y4 = (device const float4 *) (src1 + r1*nb11 + im*nb12);
 
         float sumf = 0;
         for (int i = tiisg; i < ne00/4; i += 32) {
-            for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k];
+            for (int k = 0; k < 4; ++k) sumf += (float) (x4[i][k] * y4[i][k]);
         }
 
         float all_sum = simd_sum(sumf);
@@ -1645,6 +1469,10 @@ kernel void kernel_mul_mv_f16_f32_l4(
     }
 }
 
+typedef decltype(kernel_mul_mv_l4<half, half4>) mul_mv_l4_t;
+
+template [[host_name("kernel_mul_mv_f16_f32_l4")]]  kernel mul_mv_l4_t kernel_mul_mv_l4<half, half4>;
+
 static float rope_yarn_ramp(const float low, const float high, const int i0) {
     const float y = (i0 / 2 - low) / max(0.001f, high - low);
     return 1.0f - min(1.0f, max(0.0f, y));
@@ -2765,91 +2593,10 @@ kernel void kernel_flash_attn_ext_vec_f16(
 template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>;
 //template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>;
 
-kernel void kernel_cpy_f16_f16(
-        device  const half * src0,
-        device        half * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne03,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant  uint64_t & nb03,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   int64_t & ne2,
-        constant   int64_t & ne3,
-        constant  uint64_t & nb0,
-        constant  uint64_t & nb1,
-        constant  uint64_t & nb2,
-        constant  uint64_t & nb3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]],
-        uint3   ntg[[threads_per_threadgroup]]) {
-    const int64_t i03 = tgpig[2];
-    const int64_t i02 = tgpig[1];
-    const int64_t i01 = tgpig[0];
-
-    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
-
-    const int64_t i3 = n / (ne2*ne1*ne0);
-    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
-    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
-    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
-
-    device half * dst_data = (device half *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
-
-    for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
-        device const half * src = (device half *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
-        dst_data[i00] = src[0];
-    }
-}
-
-kernel void kernel_cpy_f16_f32(
-        device  const half * src0,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne03,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant  uint64_t & nb03,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   int64_t & ne2,
-        constant   int64_t & ne3,
-        constant  uint64_t & nb0,
-        constant  uint64_t & nb1,
-        constant  uint64_t & nb2,
-        constant  uint64_t & nb3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]],
-        uint3   ntg[[threads_per_threadgroup]]) {
-    const int64_t i03 = tgpig[2];
-    const int64_t i02 = tgpig[1];
-    const int64_t i01 = tgpig[0];
-
-    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
-
-    const int64_t i3 = n / (ne2*ne1*ne0);
-    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
-    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
-    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
-
-    device float * dst_data = (device float *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
-
-    for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
-        device const half * src = (device half *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
-        dst_data[i00] = src[0];
-    }
-}
-
-kernel void kernel_cpy_f32_f16(
-        device const float * src0,
-        device        half * dst,
+template<typename T0, typename T1>
+kernel void kernel_cpy(
+        device  const void * src0,
+        device        void * dst,
         constant   int64_t & ne00,
         constant   int64_t & ne01,
         constant   int64_t & ne02,
@@ -2880,56 +2627,20 @@ kernel void kernel_cpy_f32_f16(
     const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
     const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
 
-    device half * dst_data = (device half *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+    device T1 * dst_data = (device T1 *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
 
     for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
-        device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
-
-        dst_data[i00] = src[0];
+        device const T0 * src = (device T0 *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
+        dst_data[i00] = (T1) src[0];
     }
 }
 
-kernel void kernel_cpy_f32_f32(
-        device const float * src0,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant   int64_t & ne01,
-        constant   int64_t & ne02,
-        constant   int64_t & ne03,
-        constant  uint64_t & nb00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant  uint64_t & nb03,
-        constant   int64_t & ne0,
-        constant   int64_t & ne1,
-        constant   int64_t & ne2,
-        constant   int64_t & ne3,
-        constant  uint64_t & nb0,
-        constant  uint64_t & nb1,
-        constant  uint64_t & nb2,
-        constant  uint64_t & nb3,
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]],
-        uint3   ntg[[threads_per_threadgroup]]) {
-    const int64_t i03 = tgpig[2];
-    const int64_t i02 = tgpig[1];
-    const int64_t i01 = tgpig[0];
-
-    const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
+typedef decltype(kernel_cpy<float, float>) kernel_cpy_t;
 
-    const int64_t i3 = n / (ne2*ne1*ne0);
-    const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0);
-    const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0;
-    const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0);
-
-    device float * dst_data = (device float *) ((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
-
-    for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) {
-        device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00);
-
-        dst_data[i00] = src[0];
-    }
-}
+template [[host_name("kernel_cpy_f32_f32")]]  kernel kernel_cpy_t kernel_cpy<float,  float>;
+template [[host_name("kernel_cpy_f32_f16")]]  kernel kernel_cpy_t kernel_cpy<float,  half>;
+template [[host_name("kernel_cpy_f16_f16")]]  kernel kernel_cpy_t kernel_cpy<half,   half>;
+template [[host_name("kernel_cpy_f16_f32")]]  kernel kernel_cpy_t kernel_cpy<half,   float>;
 
 kernel void kernel_cpy_f32_q8_0(
         device const float * src0,
@@ -5730,9 +5441,9 @@ void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4
 }
 
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
-kernel void kernel_get_rows(
+kernel void kernel_get_rows_q(
         device const  void * src0,
-        device const  char * src1,
+        device const  void * src1,
         device       float * dst,
         constant   int64_t & ne00,
         constant  uint64_t & nb01,
@@ -5745,55 +5456,24 @@ kernel void kernel_get_rows(
         uint3                tgpig[[threadgroup_position_in_grid]],
         uint                 tiitg[[thread_index_in_threadgroup]],
         uint3                tptg [[threads_per_threadgroup]]) {
-    //const int64_t i = tgpig;
-    //const int64_t r = ((device int32_t *) src1)[i];
-
     const int64_t i10 = tgpig.x;
     const int64_t i11 = tgpig.y;
 
-    const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
+    const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0];
 
     const int64_t i02 = i11;
 
     for (int64_t ind = tiitg; ind < ne00/16; ind += tptg.x) {
         float4x4 temp;
-        dequantize_func(
-            ((device const block_q *) ((device char *) src0 + r*nb01 + i02*nb02)) + ind/nl, ind%nl, temp);
+        dequantize_func(((device const block_q *) ((const device char *) src0 + r*nb01 + i02*nb02)) + ind/nl, ind%nl, temp);
         *(((device float4x4 *) ((device char *) dst + i11*nb2 + i10*nb1)) + ind) = temp;
     }
 }
 
-kernel void kernel_get_rows_f32(
-        device const  void * src0,
-        device const  char * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
-        constant  uint64_t & nb01,
-        constant  uint64_t & nb02,
-        constant   int64_t & ne10,
-        constant  uint64_t & nb10,
-        constant  uint64_t & nb11,
-        constant  uint64_t & nb1,
-        constant  uint64_t & nb2,
-        uint3                tgpig[[threadgroup_position_in_grid]],
-        uint                 tiitg[[thread_index_in_threadgroup]],
-        uint3                tptg [[threads_per_threadgroup]]) {
-    const int64_t i10 = tgpig.x;
-    const int64_t i11 = tgpig.y;
-
-    const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
-
-    const int64_t i02 = i11;
-
-    for (int ind = tiitg; ind < ne00; ind += tptg.x) {
-        ((device float *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
-            ((device float *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
-    }
-}
-
-kernel void kernel_get_rows_f16(
+template<typename T>
+kernel void kernel_get_rows_f(
         device const  void * src0,
-        device const  char * src1,
+        device const  void * src1,
         device       float * dst,
         constant   int64_t & ne00,
         constant  uint64_t & nb01,
@@ -5809,19 +5489,19 @@ kernel void kernel_get_rows_f16(
     const int64_t i10 = tgpig.x;
     const int64_t i11 = tgpig.y;
 
-    const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
+    const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0];
 
     const int64_t i02 = i11;
 
     for (int ind = tiitg; ind < ne00; ind += tptg.x) {
-        ((device float *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
-            ((device half *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
+        ((      device float *) ((      device char *)  dst + i11*nb2  + i10*nb1))[ind] =
+        ((const device T     *) ((const device char *) src0 + i02*nb02 +  r*nb01))[ind];
     }
 }
 
 kernel void kernel_get_rows_i32(
         device const  void * src0,
-        device const  char * src1,
+        device const  void * src1,
         device     int32_t * dst,
         constant   int64_t & ne00,
         constant  uint64_t & nb01,
@@ -5837,13 +5517,13 @@ kernel void kernel_get_rows_i32(
     const int64_t i10 = tgpig.x;
     const int64_t i11 = tgpig.y;
 
-    const int64_t r = ((device int32_t *) ((device char *) src1 + i11*nb11 + i10*nb10))[0];
+    const int64_t r = ((const device int32_t *) ((const device char *) src1 + i11*nb11 + i10*nb10))[0];
 
     const int64_t i02 = i11;
 
     for (int ind = tiitg; ind < ne00; ind += tptg.x) {
-        ((device int32_t *) ((device char *) dst + i11*nb2 + i10*nb1))[ind] =
-            ((device int32_t *) ((device char *) src0 + r*nb01 + i02*nb02))[ind];
+        ((      device int32_t *) ((      device char *) dst  + i11*nb2 + i10*nb1))[ind] =
+        ((const device int32_t *) ((const device char *) src0 + i02*nb02 + r*nb01))[ind];
     }
 }
 
@@ -5860,28 +5540,28 @@ kernel void kernel_get_rows_i32(
 #define SG_MAT_ROW 8
 
 // each block_q contains 16*nl weights
-template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
-void kernel_mul_mm_impl(device const  uchar * src0,
-                        device const  uchar * src1,
-                        device        float * dst,
-                        constant    int64_t & ne00,
-                        constant    int64_t & ne02,
-                        constant   uint64_t & nb01,
-                        constant   uint64_t & nb02,
-                        constant    int64_t & ne12,
-                        constant   uint64_t & nb10,
-                        constant   uint64_t & nb11,
-                        constant   uint64_t & nb12,
-                        constant    int64_t & ne0,
-                        constant    int64_t & ne1,
-                        constant       uint & r2,
-                        constant       uint & r3,
-                        threadgroup   uchar * shared_memory [[threadgroup(0)]],
-                        uint3                 tgpig[[threadgroup_position_in_grid]],
-                        uint                  tiitg[[thread_index_in_threadgroup]],
-                        uint                  sgitg[[simdgroup_index_in_threadgroup]]) {
+template<typename T, typename T4x4, typename simdgroup_T8x8, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread T4x4 &)>
+kernel void kernel_mul_mm(device const  uchar * src0,
+                          device const  uchar * src1,
+                          device        float * dst,
+                          constant    int64_t & ne00,
+                          constant    int64_t & ne02,
+                          constant   uint64_t & nb01,
+                          constant   uint64_t & nb02,
+                          constant    int64_t & ne12,
+                          constant   uint64_t & nb10,
+                          constant   uint64_t & nb11,
+                          constant   uint64_t & nb12,
+                          constant    int64_t & ne0,
+                          constant    int64_t & ne1,
+                          constant       uint & r2,
+                          constant       uint & r3,
+                          threadgroup   uchar * shared_memory [[threadgroup(0)]],
+                          uint3                 tgpig[[threadgroup_position_in_grid]],
+                          uint                  tiitg[[thread_index_in_threadgroup]],
+                          uint                  sgitg[[simdgroup_index_in_threadgroup]]) {
 
-    threadgroup half  * sa = (threadgroup half  *)(shared_memory);
+    threadgroup T     * sa = (threadgroup T     *)(shared_memory);
     threadgroup float * sb = (threadgroup float *)(shared_memory + 4096);
 
     const uint r0 = tgpig.y;
@@ -5896,7 +5576,7 @@ void kernel_mul_mm_impl(device const  uchar * src0,
     short thread_row = ((short)tiitg/THREAD_PER_ROW) < n_rows ? ((short)tiitg/THREAD_PER_ROW) : n_rows - 1;
     short thread_col = ((short)tiitg/THREAD_PER_COL) < n_cols ? ((short)tiitg/THREAD_PER_COL) : n_cols - 1;
 
-    simdgroup_half8x8  ma[4];
+    simdgroup_T8x8     ma[4];
     simdgroup_float8x8 mb[2];
     simdgroup_float8x8 c_res[8];
     for (int i = 0; i < 8; i++){
@@ -5919,7 +5599,7 @@ void kernel_mul_mm_impl(device const  uchar * src0,
 
     for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) {
         // load data and store to threadgroup memory
-        half4x4 temp_a;
+        T4x4 temp_a;
         dequantize_func(x, il, temp_a);
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
@@ -5939,7 +5619,7 @@ void kernel_mul_mm_impl(device const  uchar * src0,
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
         // load matrices from threadgroup memory and conduct outer products
-        threadgroup half  * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
+        threadgroup T     * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
         threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2));
 
         #pragma unroll(4)
@@ -6115,48 +5795,6 @@ void kernel_mul_mm_id_impl(
     }
 }
 
-template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
-kernel void kernel_mul_mm(device const  uchar * src0,
-                          device const  uchar * src1,
-                          device        float * dst,
-                          constant    int64_t & ne00,
-                          constant    int64_t & ne02,
-                          constant   uint64_t & nb01,
-                          constant   uint64_t & nb02,
-                          constant    int64_t & ne12,
-                          constant   uint64_t & nb10,
-                          constant   uint64_t & nb11,
-                          constant   uint64_t & nb12,
-                          constant    int64_t & ne0,
-                          constant    int64_t & ne1,
-                          constant       uint & r2,
-                          constant       uint & r3,
-                          threadgroup   uchar * shared_memory [[threadgroup(0)]],
-                          uint3                 tgpig[[threadgroup_position_in_grid]],
-                          uint                  tiitg[[thread_index_in_threadgroup]],
-                          uint                  sgitg[[simdgroup_index_in_threadgroup]]) {
-    kernel_mul_mm_impl<block_q, nl, dequantize_func>(
-        src0,
-        src1,
-        dst,
-        ne00,
-        ne02,
-        nb01,
-        nb02,
-        ne12,
-        nb10,
-        nb11,
-        nb12,
-        ne0,
-        ne1,
-        r2,
-        r3,
-        shared_memory,
-        tgpig,
-        tiitg,
-        sgitg);
-}
-
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread half4x4 &)>
 kernel void kernel_mul_mm_id(
         device const   uchar * src0s,
@@ -6237,69 +5875,60 @@ kernel void kernel_mul_mm_id(
 // get rows
 //
 
-typedef void (get_rows_t)(
-        device const void * src0,
-        device const char * src1,
-        device      float * dst,
-        constant  int64_t & ne00,
-        constant uint64_t & nb01,
-        constant uint64_t & nb02,
-        constant  int64_t & ne10,
-        constant uint64_t & nb10,
-        constant uint64_t & nb11,
-        constant uint64_t & nb1,
-        constant uint64_t & nb2,
-        uint3, uint, uint3);
-
-//template [[host_name("kernel_get_rows_f32")]]  kernel get_rows_t kernel_get_rows<float4x4,   1, dequantize_f32>;
-//template [[host_name("kernel_get_rows_f16")]]  kernel get_rows_t kernel_get_rows<half4x4,    1, dequantize_f16>;
-template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_t kernel_get_rows<block_q4_0, 2, dequantize_q4_0>;
-template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_t kernel_get_rows<block_q4_1, 2, dequantize_q4_1>;
-template [[host_name("kernel_get_rows_q5_0")]] kernel get_rows_t kernel_get_rows<block_q5_0, 2, dequantize_q5_0>;
-template [[host_name("kernel_get_rows_q5_1")]] kernel get_rows_t kernel_get_rows<block_q5_1, 2, dequantize_q5_1>;
-template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_t kernel_get_rows<block_q8_0, 2, dequantize_q8_0>;
-template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_t kernel_get_rows<block_q2_K, QK_NL, dequantize_q2_K>;
-template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_t kernel_get_rows<block_q3_K, QK_NL, dequantize_q3_K>;
-template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_t kernel_get_rows<block_q4_K, QK_NL, dequantize_q4_K>;
-template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_t kernel_get_rows<block_q5_K, QK_NL, dequantize_q5_K>;
-template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows<block_q6_K, QK_NL, dequantize_q6_K>;
-template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
-template [[host_name("kernel_get_rows_iq2_xs")]]  kernel get_rows_t kernel_get_rows<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
-template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
-template [[host_name("kernel_get_rows_iq3_s")]]   kernel get_rows_t kernel_get_rows<block_iq3_s,   QK_NL, dequantize_iq3_s>;
-template [[host_name("kernel_get_rows_iq2_s")]]   kernel get_rows_t kernel_get_rows<block_iq2_s,   QK_NL, dequantize_iq2_s>;
-template [[host_name("kernel_get_rows_iq1_s")]]   kernel get_rows_t kernel_get_rows<block_iq1_s,   QK_NL, dequantize_iq1_s>;
-template [[host_name("kernel_get_rows_iq1_m")]]   kernel get_rows_t kernel_get_rows<block_iq1_m,   QK_NL, dequantize_iq1_m>;
-template [[host_name("kernel_get_rows_iq4_nl")]]  kernel get_rows_t kernel_get_rows<block_iq4_nl,  2,     dequantize_iq4_nl>;
-template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_t kernel_get_rows<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+typedef decltype(kernel_get_rows_f<float>) get_rows_f_t;
+
+template [[host_name("kernel_get_rows_f32")]]  kernel get_rows_f_t kernel_get_rows_f<float>;
+template [[host_name("kernel_get_rows_f16")]]  kernel get_rows_f_t kernel_get_rows_f<half>;
+
+typedef decltype(kernel_get_rows_q<block_q4_0, 2, dequantize_q4_0>) get_rows_q_t;
+
+template [[host_name("kernel_get_rows_q4_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_0,    2, dequantize_q4_0>;
+template [[host_name("kernel_get_rows_q4_1")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_1,    2, dequantize_q4_1>;
+template [[host_name("kernel_get_rows_q5_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_0,    2, dequantize_q5_0>;
+template [[host_name("kernel_get_rows_q5_1")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_1,    2, dequantize_q5_1>;
+template [[host_name("kernel_get_rows_q8_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q8_0,    2, dequantize_q8_0>;
+template [[host_name("kernel_get_rows_q2_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q2_K,    QK_NL, dequantize_q2_K>;
+template [[host_name("kernel_get_rows_q3_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q3_K,    QK_NL, dequantize_q3_K>;
+template [[host_name("kernel_get_rows_q4_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_K,    QK_NL, dequantize_q4_K>;
+template [[host_name("kernel_get_rows_q5_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_K,    QK_NL, dequantize_q5_K>;
+template [[host_name("kernel_get_rows_q6_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q6_K,    QK_NL, dequantize_q6_K>;
+template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
+template [[host_name("kernel_get_rows_iq2_xs")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
+template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_q_t kernel_get_rows_q<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_get_rows_iq3_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_get_rows_iq2_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq2_s,   QK_NL, dequantize_iq2_s>;
+template [[host_name("kernel_get_rows_iq1_s")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_get_rows_iq1_m")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_get_rows_iq4_nl")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_nl,  2,     dequantize_iq4_nl>;
+template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
 
 //
 // matrix-matrix multiplication
 //
 
-typedef decltype(kernel_mul_mm<float4x4, 1, dequantize_f32>) mat_mm_t;
-
-template [[host_name("kernel_mul_mm_f32_f32")]]     kernel mat_mm_t kernel_mul_mm<float4x4,      1,     dequantize_f32>;
-template [[host_name("kernel_mul_mm_f16_f32")]]     kernel mat_mm_t kernel_mul_mm<half4x4,       1,     dequantize_f16>;
-template [[host_name("kernel_mul_mm_q4_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_0,    2,     dequantize_q4_0>;
-template [[host_name("kernel_mul_mm_q4_1_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_1,    2,     dequantize_q4_1>;
-template [[host_name("kernel_mul_mm_q5_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_0,    2,     dequantize_q5_0>;
-template [[host_name("kernel_mul_mm_q5_1_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_1,    2,     dequantize_q5_1>;
-template [[host_name("kernel_mul_mm_q8_0_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q8_0,    2,     dequantize_q8_0>;
-template [[host_name("kernel_mul_mm_q2_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q2_K,    QK_NL, dequantize_q2_K>;
-template [[host_name("kernel_mul_mm_q3_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q3_K,    QK_NL, dequantize_q3_K>;
-template [[host_name("kernel_mul_mm_q4_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q4_K,    QK_NL, dequantize_q4_K>;
-template [[host_name("kernel_mul_mm_q5_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q5_K,    QK_NL, dequantize_q5_K>;
-template [[host_name("kernel_mul_mm_q6_K_f32")]]    kernel mat_mm_t kernel_mul_mm<block_q6_K,    QK_NL, dequantize_q6_K>;
-template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
-template [[host_name("kernel_mul_mm_iq2_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
-template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
-template [[host_name("kernel_mul_mm_iq3_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq3_s,   QK_NL, dequantize_iq3_s>;
-template [[host_name("kernel_mul_mm_iq2_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq2_s,   QK_NL, dequantize_iq2_s>;
-template [[host_name("kernel_mul_mm_iq1_s_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq1_s,   QK_NL, dequantize_iq1_s>;
-template [[host_name("kernel_mul_mm_iq1_m_f32")]]   kernel mat_mm_t kernel_mul_mm<block_iq1_m,   QK_NL, dequantize_iq1_m>;
-template [[host_name("kernel_mul_mm_iq4_nl_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_nl,  2,     dequantize_iq4_nl>;
-template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+typedef decltype(kernel_mul_mm<half, half4x4, simdgroup_half8x8, float4x4, 1, dequantize_f32>) mat_mm_t;
+
+template [[host_name("kernel_mul_mm_f32_f32")]]     kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   float4x4,      1,     dequantize_f32>;
+template [[host_name("kernel_mul_mm_f16_f32")]]     kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half4x4,       1,     dequantize_f16>;
+template [[host_name("kernel_mul_mm_q4_0_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q4_0,    2,     dequantize_q4_0>;
+template [[host_name("kernel_mul_mm_q4_1_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q4_1,    2,     dequantize_q4_1>;
+template [[host_name("kernel_mul_mm_q5_0_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q5_0,    2,     dequantize_q5_0>;
+template [[host_name("kernel_mul_mm_q5_1_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q5_1,    2,     dequantize_q5_1>;
+template [[host_name("kernel_mul_mm_q8_0_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q8_0,    2,     dequantize_q8_0>;
+template [[host_name("kernel_mul_mm_q2_K_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q2_K,    QK_NL, dequantize_q2_K>;
+template [[host_name("kernel_mul_mm_q3_K_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q3_K,    QK_NL, dequantize_q3_K>;
+template [[host_name("kernel_mul_mm_q4_K_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q4_K,    QK_NL, dequantize_q4_K>;
+template [[host_name("kernel_mul_mm_q5_K_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q5_K,    QK_NL, dequantize_q5_K>;
+template [[host_name("kernel_mul_mm_q6_K_f32")]]    kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q6_K,    QK_NL, dequantize_q6_K>;
+template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq2_xxs, QK_NL, dequantize_iq2_xxs>;
+template [[host_name("kernel_mul_mm_iq2_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq2_xs,  QK_NL, dequantize_iq2_xs>;
+template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq3_xxs, QK_NL, dequantize_iq3_xxs>;
+template [[host_name("kernel_mul_mm_iq3_s_f32")]]   kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq3_s,   QK_NL, dequantize_iq3_s>;
+template [[host_name("kernel_mul_mm_iq2_s_f32")]]   kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq2_s,   QK_NL, dequantize_iq2_s>;
+template [[host_name("kernel_mul_mm_iq1_s_f32")]]   kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq1_s,   QK_NL, dequantize_iq1_s>;
+template [[host_name("kernel_mul_mm_iq1_m_f32")]]   kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq1_m,   QK_NL, dequantize_iq1_m>;
+template [[host_name("kernel_mul_mm_iq4_nl_f32")]]  kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq4_nl,  2,     dequantize_iq4_nl>;
+template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mat_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
 
 //
 // indirect matrix-matrix multiplication
@@ -6436,7 +6065,7 @@ void mmv_fn(
     impl_fn(src0,(const device float *)src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,r2,r3,shared_values,tgpig,tiisg,sgitg);
 }
 
-typedef decltype(mmv_fn<kernel_mul_mv_f32_f32_impl>) mul_mv_impl_fn_t;
+typedef decltype(mmv_fn<kernel_mul_mv_impl<half, half4, half, half4>>) mul_mv_impl_fn_t;
 
 template<mul_mv_impl_fn_t impl_fn>
 kernel void kernel_mul_mv_id(
@@ -6514,20 +6143,20 @@ kernel void kernel_mul_mv_id(
         sgitg);
 }
 
-typedef decltype(kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f32_f32_impl>>) kernel_mul_mv_id_t;
-
-template [[host_name("kernel_mul_mv_id_f32_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f32_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_f16_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_f16_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q8_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q8_0_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q4_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
-template [[host_name("kernel_mul_mv_id_q4_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
-template [[host_name("kernel_mul_mv_id_q5_0_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
-template [[host_name("kernel_mul_mv_id_q5_1_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
-template [[host_name("kernel_mul_mv_id_q2_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q2_K_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q3_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q3_K_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q4_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q4_K_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q5_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q5_K_f32_impl>>;
-template [[host_name("kernel_mul_mv_id_q6_K_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q6_K_f32_impl>>;
+typedef decltype(kernel_mul_mv_id<mmv_fn<kernel_mul_mv_impl<float, float4, float, float4>>>) kernel_mul_mv_id_t;
+
+template [[host_name("kernel_mul_mv_id_f32_f32")]]     kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_impl<float, float4, float, float4>>>;
+template [[host_name("kernel_mul_mv_id_f16_f32")]]     kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_impl<half, half4, float, float4>>>;
+template [[host_name("kernel_mul_mv_id_q8_0_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q8_0_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q4_0_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q4_1_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q5_0_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q5_1_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>>>;
+template [[host_name("kernel_mul_mv_id_q2_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q2_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q3_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q3_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q4_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q4_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q5_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q5_K_f32_impl>>;
+template [[host_name("kernel_mul_mv_id_q6_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q6_K_f32_impl>>;
 template [[host_name("kernel_mul_mv_id_iq1_s_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq1_s_f32_impl>>;
 template [[host_name("kernel_mul_mv_id_iq1_m_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq1_m_f32_impl>>;
 template [[host_name("kernel_mul_mv_id_iq2_xxs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_xxs_f32_impl>>;

From 17eb6aa8a992cda37ee65cf848d9289bd6cad860 Mon Sep 17 00:00:00 2001
From: bandoti <141645996+bandoti@users.noreply.github.com>
Date: Sat, 13 Jul 2024 13:12:39 -0300
Subject: [PATCH 045/125] vulkan : cmake integration (#8119)

* Add Vulkan to CMake pkg

* Add Sycl to CMake pkg

* Add OpenMP to CMake pkg

* Split generated shader file into separate translation unit

* Add CMake target for Vulkan shaders

* Update README.md

* Add make target for Vulkan shaders

* Use pkg-config to locate vulkan library

* Add vulkan SDK dep to ubuntu-22-cmake-vulkan workflow

* Clean up tabs

* Move sudo to apt-key invocation

* Forward GGML_EXTRA_LIBS to CMake config pkg

* Update vulkan obj file paths

* Add shaderc to nix pkg

* Add python3 to Vulkan nix build

* Link against ggml in cmake pkg

* Remove Python dependency from Vulkan build

* code review changes

* Remove trailing newline

* Add cflags from pkg-config to fix w64devkit build

* Update README.md

* Remove trailing whitespace

* Update README.md

* Remove trailing whitespace

* Fix doc heading

* Make glslc required Vulkan component

* remove clblast from nix pkg
---
 .devops/nix/package.nix                       |      2 +
 .github/workflows/build.yml                   |      6 +-
 CMakeLists.txt                                |     11 +-
 Makefile                                      |     35 +-
 cmake/llama-config.cmake.in                   |     29 +-
 docs/build.md                                 |     39 +
 ggml/.gitignore                               |      2 +
 ggml/ggml_vk_generate_shaders.py              |    220 -
 ggml/src/CMakeLists.txt                       |     37 +-
 ggml/src/ggml-vulkan-shaders.hpp              | 144956 ---------------
 ggml/src/vulkan-shaders/CMakeLists.txt        |      5 +
 .../src/vulkan-shaders/vulkan-shaders-gen.cpp |    524 +
 12 files changed, 674 insertions(+), 145192 deletions(-)
 create mode 100644 ggml/.gitignore
 delete mode 100644 ggml/ggml_vk_generate_shaders.py
 delete mode 100644 ggml/src/ggml-vulkan-shaders.hpp
 create mode 100644 ggml/src/vulkan-shaders/CMakeLists.txt
 create mode 100644 ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp

diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix
index 00be596ce3fb7a..911c42ecb16cf6 100644
--- a/.devops/nix/package.nix
+++ b/.devops/nix/package.nix
@@ -18,6 +18,7 @@
   vulkan-headers,
   vulkan-loader,
   curl,
+  shaderc,
   useBlas ? builtins.all (x: !x) [
     useCuda
     useMetalKit
@@ -146,6 +147,7 @@ let
   vulkanBuildInputs = [
     vulkan-headers
     vulkan-loader
+    shaderc
   ];
 in
 
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 1e344db6b09e9f..1f275603a3742f 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -355,8 +355,10 @@ jobs:
       - name: Dependencies
         id: depends
         run: |
-          sudo apt-get update
-          sudo apt-get install build-essential libvulkan-dev
+          wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
+          sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
+          sudo apt-get update -y
+          sudo apt-get install -y build-essential vulkan-sdk
 
       - name: Build
         id: cmake_build
diff --git a/CMakeLists.txt b/CMakeLists.txt
index befc4433cd0f83..67dcf86d4fab75 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -132,7 +132,16 @@ set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location o
 set(LLAMA_LIB_INSTALL_DIR     ${CMAKE_INSTALL_LIBDIR}     CACHE PATH "Location of library files")
 set(LLAMA_BIN_INSTALL_DIR     ${CMAKE_INSTALL_BINDIR}     CACHE PATH "Location of binary  files")
 
-get_directory_property(LLAMA_TRANSIENT_DEFINES COMPILE_DEFINITIONS)
+
+# At the moment some compile definitions are placed within the ggml/src
+# directory but not exported on the `ggml` target. This could be improved by
+# determining _precisely_ which defines are necessary for the llama-config
+# package.
+#
+get_directory_property(GGML_DIR_DEFINES DIRECTORY ggml/src COMPILE_DEFINITIONS)
+get_target_property(GGML_TARGET_DEFINES ggml COMPILE_DEFINITIONS)
+set(GGML_TRANSIENT_DEFINES ${GGML_TARGET_DEFINES} ${GGML_DIR_DEFINES})
+get_target_property(GGML_LINK_LIBRARIES ggml LINK_LIBRARIES)
 
 set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h)
 install(TARGETS llama LIBRARY PUBLIC_HEADER)
diff --git a/Makefile b/Makefile
index 4869d2ecf3be28..bec332ec57b551 100644
--- a/Makefile
+++ b/Makefile
@@ -197,6 +197,10 @@ ifdef GGML_RPC
 	BUILD_TARGETS += rpc-server
 endif
 
+ifdef GGML_VULKAN
+	BUILD_TARGETS += vulkan-shaders-gen
+endif
+
 default: $(BUILD_TARGETS) $(LEGACY_TARGETS_BUILD)
 
 test: $(TEST_TARGETS)
@@ -710,8 +714,8 @@ endif # GGML_CUDA
 
 ifdef GGML_VULKAN
 	MK_CPPFLAGS += -DGGML_USE_VULKAN
-	MK_LDFLAGS  += -lvulkan
-	OBJ_GGML    += ggml/src/ggml-vulkan.o
+	MK_LDFLAGS  += $(shell pkg-config --libs vulkan)
+	OBJ_GGML    += ggml/src/ggml-vulkan.o ggml/src/ggml-vulkan-shaders.o
 
 ifdef GGML_VULKAN_CHECK_RESULTS
 	MK_CPPFLAGS  += -DGGML_VULKAN_CHECK_RESULTS
@@ -733,10 +737,28 @@ ifdef GGML_VULKAN_RUN_TESTS
 	MK_CPPFLAGS  += -DGGML_VULKAN_RUN_TESTS
 endif
 
-ggml/src/ggml-vulkan.o: \
-	ggml/src/ggml-vulkan.cpp \
-	ggml/include/ggml-vulkan.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
+GLSLC_CMD  = glslc
+_ggml_vk_genshaders_cmd = $(shell pwd)/vulkan-shaders-gen
+_ggml_vk_header = ggml/src/ggml-vulkan-shaders.hpp
+_ggml_vk_source = ggml/src/ggml-vulkan-shaders.cpp
+_ggml_vk_input_dir = ggml/src/vulkan-shaders
+_ggml_vk_shader_deps = $(echo $(_ggml_vk_input_dir)/*.comp)
+
+ggml/src/ggml-vulkan.o: ggml/src/ggml-vulkan.cpp ggml/include/ggml-vulkan.h $(_ggml_vk_header) $(_ggml_vk_source)
+	$(CXX) $(CXXFLAGS) $(shell pkg-config --cflags vulkan) -c $< -o $@
+
+$(_ggml_vk_header): $(_ggml_vk_source)
+
+$(_ggml_vk_source): $(_ggml_vk_shader_deps) vulkan-shaders-gen
+	$(_ggml_vk_genshaders_cmd) \
+		--glslc      $(GLSLC_CMD) \
+		--input-dir  $(_ggml_vk_input_dir) \
+		--target-hpp $(_ggml_vk_header) \
+		--target-cpp $(_ggml_vk_source)
+
+vulkan-shaders-gen: ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
+	$(CXX) $(CXXFLAGS) -o $@ $(LDFLAGS) ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
+
 endif # GGML_VULKAN
 
 ifdef GGML_HIPBLAS
@@ -1116,6 +1138,7 @@ clean:
 	rm -vrf ggml/src/ggml-cuda/template-instances/*.o
 	rm -rvf $(BUILD_TARGETS)
 	rm -rvf $(TEST_TARGETS)
+	rm -f vulkan-shaders-gen ggml/src/ggml-vulkan-shaders.hpp ggml/src/ggml-vulkan-shaders.cpp
 	rm -rvf $(LEGACY_TARGETS_CLEAN)
 	find examples pocs -type f -name "*.o" -delete
 
diff --git a/cmake/llama-config.cmake.in b/cmake/llama-config.cmake.in
index 2e7da2f8ebd699..f072b76a39d2e1 100644
--- a/cmake/llama-config.cmake.in
+++ b/cmake/llama-config.cmake.in
@@ -8,6 +8,13 @@ set(GGML_CUDA       @GGML_CUDA@)
 set(GGML_METAL      @GGML_METAL@)
 set(GGML_HIPBLAS    @GGML_HIPBLAS@)
 set(GGML_ACCELERATE @GGML_ACCELERATE@)
+set(GGML_VULKAN @GGML_VULKAN@)
+set(GGML_VULKAN_CHECK_RESULTS @GGML_VULKAN_CHECK_RESULTS@)
+set(GGML_VULKAN_DEBUG @GGML_VULKAN_DEBUG@)
+set(GGML_VULKAN_MEMORY_DEBUG @GGML_VULKAN_MEMORY_DEBUG@)
+set(GGML_VULKAN_VALIDATE @GGML_VULKAN_VALIDATE@)
+set(GGML_SYCL @GGML_SYCL@)
+set(GGML_OPENMP @GGML_OPENMP@)
 
 @PACKAGE_INIT@
 
@@ -37,18 +44,36 @@ if (GGML_METAL)
     find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
 endif()
 
+if (GGML_VULKAN)
+    find_package(Vulkan REQUIRED)
+endif()
+
 if (GGML_HIPBLAS)
     find_package(hip REQUIRED)
     find_package(hipblas REQUIRED)
     find_package(rocblas REQUIRED)
 endif()
 
+if (GGML_SYCL)
+    find_package(IntelSYCL REQUIRED)
+    find_package(MKL REQUIRED)
+endif()
+
+if (GGML_OPENMP)
+    find_package(OpenMP REQUIRED)
+endif()
+
+
+find_library(ggml_LIBRARY ggml
+    REQUIRED
+    HINTS ${LLAMA_LIB_DIR})
+
 find_library(llama_LIBRARY llama
     REQUIRED
     HINTS ${LLAMA_LIB_DIR})
 
-set(_llama_link_deps "Threads::Threads" "@LLAMA_EXTRA_LIBS@")
-set(_llama_transient_defines "@LLAMA_TRANSIENT_DEFINES@")
+set(_llama_link_deps "${ggml_LIBRARY}" "@GGML_LINK_LIBRARIES@")
+set(_llama_transient_defines "@GGML_TRANSIENT_DEFINES@")
 
 add_library(llama UNKNOWN IMPORTED)
 
diff --git a/docs/build.md b/docs/build.md
index d70f72f4c7b82e..916fcf22d7924d 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -242,6 +242,45 @@ The following compilation options are also available to tweak performance (yes,
 
 ### Vulkan
 
+**Windows**
+
+#### w64devkit
+
+Download and extract [w64devkit](https://github.com/skeeto/w64devkit/releases).
+
+Download and install the [Vulkan SDK](https://vulkan.lunarg.com/sdk/home#windows). When selecting components, only the Vulkan SDK Core is required.
+
+Launch `w64devkit.exe` and run the following commands to copy Vulkan dependencies:
+```sh
+SDK_VERSION=1.3.283.0
+cp /VulkanSDK/$SDK_VERSION/Bin/glslc.exe $W64DEVKIT_HOME/bin/
+cp /VulkanSDK/$SDK_VERSION/Lib/vulkan-1.lib $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/
+cp -r /VulkanSDK/$SDK_VERSION/Include/* $W64DEVKIT_HOME/x86_64-w64-mingw32/include/
+cat > $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/pkgconfig/vulkan.pc <<EOF
+Name: Vulkan-Loader
+Description: Vulkan Loader
+Version: $SDK_VERSION
+Libs: -lvulkan-1
+EOF
+
+```
+Switch into the `llama.cpp` directory and run `make GGML_VULKAN=1`.
+
+#### MSYS2
+Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies.
+  ```sh
+  pacman -S git \
+      mingw-w64-ucrt-x86_64-gcc \
+      mingw-w64-ucrt-x86_64-cmake \
+      mingw-w64-ucrt-x86_64-vulkan-devel \
+      mingw-w64-ucrt-x86_64-shaderc
+  ```
+Switch into `llama.cpp` directory and build using CMake.
+```sh
+cmake -B build -DGGML_VULKAN=ON
+cmake --build build --config Release
+```
+
 **With docker**:
 
 You don't need to install Vulkan SDK. It will be installed inside the container.
diff --git a/ggml/.gitignore b/ggml/.gitignore
new file mode 100644
index 00000000000000..c82d8e69295ac8
--- /dev/null
+++ b/ggml/.gitignore
@@ -0,0 +1,2 @@
+src/ggml-vulkan-shaders.hpp
+src/ggml-vulkan-shaders.cpp
diff --git a/ggml/ggml_vk_generate_shaders.py b/ggml/ggml_vk_generate_shaders.py
deleted file mode 100644
index 41d5d9b8cf4c93..00000000000000
--- a/ggml/ggml_vk_generate_shaders.py
+++ /dev/null
@@ -1,220 +0,0 @@
-#!/usr/bin/env python
-
-import logging
-import argparse
-import asyncio
-import os
-from tempfile import gettempdir
-
-logger = logging.getLogger("ggml-vk-generate-shaders")
-
-GLSLC = "glslc"
-
-type_names = [
-    "f32",
-    "f16",
-    "q4_0",
-    "q4_1",
-    "q5_0",
-    "q5_1",
-    "q8_0",
-    "q2_k",
-    "q3_k",
-    "q4_k",
-    "q5_k",
-    "q6_k",
-]
-
-ASYNCIO_CONCURRENCY = 64
-
-input_dir = "vulkan-shaders"
-output_dir = gettempdir()
-
-lock = asyncio.Lock()
-shader_fnames = []
-
-
-async def string_to_spv(name, in_fname, defines, fp16=True):
-    name = f"{name}{'_fp32' if not fp16 else ''}"
-    out_fname = os.path.join(output_dir, f"{name}.spv")
-
-    in_path = os.path.join(input_dir, in_fname)
-
-    cmd = [GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", in_path, "-o", out_fname]
-
-    cmd.extend([f"-D{key}={value}" for key, value in defines.items()])
-
-    proc = await asyncio.create_subprocess_exec(*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE)
-
-    stdout, stderr = await proc.communicate()
-
-    stdout = stdout.decode()
-    error = stderr.decode()
-
-    if proc.returncode:
-        cmd = " ".join(cmd)
-        logger.error(f"cannot compile {name}\n\n{cmd}\n\n{error}")
-        return
-
-    async with lock:
-        shader_fnames.append((name, out_fname))
-
-
-def matmul_shaders(tasks, fp16, matmul_id):
-    if fp16:
-        load_vec = "8"
-        aligned_b_type_f32 = "mat2x4"
-        aligned_b_type_f16 = "f16mat2x4"
-    else:
-        load_vec = "4"
-        aligned_b_type_f32 = "vec4"
-        aligned_b_type_f16 = "f16vec4"
-
-    base_dict = {"FLOAT_TYPE": "float" if not fp16 else "float16_t"}
-    shader_name = "matmul"
-
-    if matmul_id:
-        base_dict["MUL_MAT_ID"] = "1"
-        shader_name = "matmul_id"
-
-    if fp16:
-        base_dict["FLOAT16"] = "1"
-
-    # Shaders with f16 B_TYPE
-    tasks.append(string_to_spv(f"{shader_name}_f32_f16", "mul_mm.comp", base_dict | {"DATA_A_F32": "1", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
-    tasks.append(string_to_spv(f"{shader_name}_f32_f16_aligned", "mul_mm.comp", base_dict | {"DATA_A_F32": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "B_TYPE": aligned_b_type_f16, "D_TYPE": "float"}, fp16))
-
-    tasks.append(string_to_spv(f"{shader_name}_f16", "mul_mm.comp", base_dict | {"DATA_A_F16": "1", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16))
-    tasks.append(string_to_spv(f"{shader_name}_f16_aligned", "mul_mm.comp", base_dict | {"DATA_A_F16": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "B_TYPE": aligned_b_type_f16, "D_TYPE": "float"}, fp16))
-
-    for tname in type_names:
-        data_a_key = f"DATA_A_{tname.upper()}"
-        load_vec_a = load_vec if tname in ("f32", "f16") else "2"
-        tasks.append(string_to_spv(f"{shader_name}_{tname}_f32", "mul_mm.comp", base_dict | {data_a_key: "1", "B_TYPE": "float", "D_TYPE": "float"}, fp16))
-        tasks.append(string_to_spv(f"{shader_name}_{tname}_f32_aligned", "mul_mm.comp", base_dict | {data_a_key: "2", "LOAD_VEC_A": load_vec_a, "LOAD_VEC_B": load_vec, "B_TYPE": aligned_b_type_f32, "D_TYPE": "float"}, fp16))
-
-
-async def main():
-    logger.info("ggml_vulkan: Generating and compiling shaders to SPIR-V")
-
-    tasks = []
-
-    base_dict = {"FLOAT_TYPE": "float"}
-
-    for fp16 in (False, True):
-        # MUL_MAT
-        matmul_shaders(tasks, fp16, False)
-        # MUL_MAT_ID
-        matmul_shaders(tasks, fp16, True)
-
-    for tname in type_names:
-        # mul mat vec
-        data_a_key = f"DATA_A_{tname.upper()}"
-        shader = f"mul_mat_vec_{tname}.comp" if tname.endswith("_k") else "mul_mat_vec.comp"
-
-        tasks.append(string_to_spv(f"mul_mat_vec_{tname}_f32_f32", shader, base_dict | {data_a_key: "1", "B_TYPE": "float", "D_TYPE": "float"}))
-        tasks.append(string_to_spv(f"mul_mat_vec_{tname}_f16_f32", shader, base_dict | {data_a_key: "1", "B_TYPE": "float16_t", "D_TYPE": "float"}))
-
-        tasks.append(string_to_spv(f"mul_mat_vec_id_{tname}_f32", shader, base_dict | {"MUL_MAT_ID": "1", data_a_key: "1", "B_TYPE": "float", "D_TYPE": "float"}))
-
-        # Dequant shaders
-        if tname != "f16":
-            tasks.append(string_to_spv(f"dequant_{tname}", f"dequant_{tname}.comp", base_dict | {data_a_key: "1", "D_TYPE": "float16_t"}))
-
-        # get_rows
-        if not tname.endswith("_k"):
-            shader = "get_rows.comp" if tname in ("f32", "f16") else "get_rows_quant.comp"
-
-            if tname == "f16":
-                tasks.append(string_to_spv(f"get_rows_{tname}", shader, {data_a_key: "1", "B_TYPE": "int", "D_TYPE": "float16_t", "OPTIMIZATION_ERROR_WORKAROUND": "1"}))
-            else:
-                tasks.append(string_to_spv(f"get_rows_{tname}", shader, {data_a_key: "1", "B_TYPE": "int", "D_TYPE": "float16_t"}))
-            tasks.append(string_to_spv(f"get_rows_{tname}_f32", shader, {data_a_key: "1", "B_TYPE": "int", "D_TYPE": "float"}))
-
-    tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}))
-
-    # Norms
-    tasks.append(string_to_spv("norm_f32", "norm.comp", base_dict | {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("rms_norm_f32", "rms_norm.comp", base_dict | {"A_TYPE": "float", "D_TYPE": "float"}))
-
-    tasks.append(string_to_spv("cpy_f32_f32", "copy.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("cpy_f32_f16", "copy.comp", {"A_TYPE": "float", "D_TYPE": "float16_t"}))
-    tasks.append(string_to_spv("cpy_f16_f16", "copy.comp", {"A_TYPE": "float16_t", "D_TYPE": "float16_t", "OPTIMIZATION_ERROR_WORKAROUND": "1"}))
-
-    tasks.append(string_to_spv("add_f32", "add.comp", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {}))
-
-    tasks.append(string_to_spv("mul_f32", "mul.comp", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("div_f32", "div.comp", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("scale_f32", "scale.comp", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("sqr_f32", "square.comp", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("clamp_f32", "clamp.comp", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"}))
-
-    tasks.append(string_to_spv("gelu_f32", "gelu.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("silu_f32", "silu.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("relu_f32", "relu.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
-
-    tasks.append(string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
-
-    tasks.append(string_to_spv("soft_max_f32", "soft_max.comp", base_dict | {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("soft_max_f32_f16", "soft_max.comp", base_dict | {"A_TYPE": "float", "B_TYPE": "float16_t", "D_TYPE": "float"}))
-
-    tasks.append(string_to_spv("rope_norm_f32", "rope_norm.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("rope_norm_f16", "rope_norm.comp", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
-
-    tasks.append(string_to_spv("rope_neox_f32", "rope_neox.comp", {"A_TYPE": "float", "D_TYPE": "float"}))
-    tasks.append(string_to_spv("rope_neox_f16", "rope_neox.comp", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}))
-
-    tasks.append(string_to_spv("argsort_f32", "argsort.comp", {"A_TYPE": "float"}))
-
-    tasks.append(string_to_spv("sum_rows_f32", "sum_rows.comp", base_dict | {"A_TYPE": "float", "D_TYPE": "float"}))
-
-    # Helper to decorate tasks with semaphore acquisition.
-    async def withSemaphore(sem, task):
-        async with sem:
-            return await task
-
-    # Run tasks concurrently guarded by a concurrency limit.
-    sem = asyncio.Semaphore(ASYNCIO_CONCURRENCY)
-    await asyncio.gather(*(withSemaphore(sem, task) for task in tasks))
-
-    with open("ggml-vulkan-shaders.hpp", "w") as f:
-        f.write("#include <cstdint>\n\n")
-        for name, path in sorted(shader_fnames):
-
-            with open(path, "rb") as spv:
-                counter = 0
-                newline_counter = 0
-                f.write(f"unsigned char {name}_data[] = {{\n")
-                for val in spv.read():
-                    f.write(f"0x{val:02x},")
-                    newline_counter += 1
-                    counter += 1
-                    if newline_counter >= 12:
-                        newline_counter = 0
-                        f.write("\n")
-            f.write("\n};\n")
-            f.write(f"const uint64_t {name}_len = {counter};\n\n")
-            os.remove(path)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="GGML Vulkan Shader Generator")
-
-    parser.add_argument("--glslc", help="Path to glslc")
-    parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
-
-    args = parser.parse_args()
-
-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
-
-    if args.glslc:
-        GLSLC = args.glslc
-
-    asyncio.run(main())
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index c5ee7e4255ee5f..41e50ad2c9e337 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -527,14 +527,11 @@ if (GGML_RPC)
 endif()
 
 if (GGML_VULKAN)
-    find_package(Vulkan)
+    find_package(Vulkan COMPONENTS glslc REQUIRED)
 
     if (Vulkan_FOUND)
         message(STATUS "Vulkan found")
 
-        set(GGML_HEADERS_VULKAN ../include/ggml-vulkan.h)
-        set(GGML_SOURCES_VULKAN ggml-vulkan.cpp)
-
         list(APPEND GGML_CDEF_PUBLIC GGML_USE_VULKAN)
 
         # Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build
@@ -563,7 +560,37 @@ if (GGML_VULKAN)
             add_compile_definitions(GGML_VULKAN_RUN_TESTS)
         endif()
 
-        set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} Vulkan::Vulkan)
+        add_subdirectory(vulkan-shaders)
+
+        set (_ggml_vk_genshaders_cmd vulkan-shaders-gen)
+        set (_ggml_vk_header     ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp)
+        set (_ggml_vk_source     ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.cpp)
+        set (_ggml_vk_input_dir  ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders)
+        set (_ggml_vk_output_dir ${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv)
+
+        file(GLOB _ggml_vk_shader_deps "${_ggml_vk_input_dir}/*.comp")
+
+        add_custom_command(
+            OUTPUT ${_ggml_vk_header}
+                   ${_ggml_vk_source}
+
+            COMMAND ${_ggml_vk_genshaders_cmd}
+                --glslc      ${Vulkan_GLSLC_EXECUTABLE}
+                --input-dir  ${_ggml_vk_input_dir}
+                --output-dir ${_ggml_vk_output_dir}
+                --target-hpp ${_ggml_vk_header}
+                --target-cpp ${_ggml_vk_source}
+                --no-clean
+
+            DEPENDS ${_ggml_vk_shader_deps}
+            COMMENT "Generate vulkan shaders"
+        )
+
+        set(GGML_HEADERS_VULKAN ${CMAKE_CURRENT_SOURCE_DIR}/../include/ggml-vulkan.h ${_ggml_vk_header})
+        set(GGML_SOURCES_VULKAN ggml-vulkan.cpp ${_ggml_vk_source})
+
+        set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS} Vulkan::Vulkan)
+        set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${CMAKE_CURRENT_BINARY_DIR})
     else()
         message(WARNING "Vulkan not found")
     endif()
diff --git a/ggml/src/ggml-vulkan-shaders.hpp b/ggml/src/ggml-vulkan-shaders.hpp
deleted file mode 100644
index f0c4c6baf592b2..00000000000000
--- a/ggml/src/ggml-vulkan-shaders.hpp
+++ /dev/null
@@ -1,144956 +0,0 @@
-#include <cstdint>
-
-unsigned char add_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x67,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,
-0x4b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,
-0x58,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xec,0x01,0x00,0x00,0x59,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,
-0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x60,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
-0x60,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x62,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x61,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
-0x62,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
-0x5b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x66,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x81,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
-0x5e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x47,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00,
-0x5d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t add_f32_len = 4276;
-
-unsigned char argsort_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x41,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x12,0x01,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x04,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x30,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x30,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x89,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x8a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0f,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x10,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x10,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x12,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x12,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1c,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x00,0x04,0x00,0x00,0x1c,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x22,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x22,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x25,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x22,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x1e,0x00,0x05,0x00,0x30,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x31,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x34,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x37,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x46,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x88,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x93,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x0f,0x01,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x10,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,
-0x20,0x00,0x04,0x00,0x11,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x10,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x11,0x01,0x00,0x00,
-0x12,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1a,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x21,0x00,0x00,0x00,0x1c,0x01,0x00,0x00,
-0x10,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x1d,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0x24,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x1e,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x25,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x34,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x37,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x3a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x39,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x39,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1d,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x34,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x44,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x46,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x48,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x48,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x45,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x4b,0x00,0x00,0x00,
-0xb2,0x00,0x05,0x00,0x37,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x3c,0x01,0x00,0x00,0x36,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x4a,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0xec,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x93,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x88,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf5,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x93,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xba,0x00,0x05,0x00,0x37,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd5,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x37,0x00,0x00,0x00,
-0x3f,0x01,0x00,0x00,0xe6,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x37,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xcd,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,
-0xd5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc6,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc6,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x37,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xfb,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x32,0x01,0x00,0x00,
-0x13,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x37,0x01,0x00,0x00,
-0xbe,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x32,0x01,0x00,0x00,
-0x37,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xbe,0x00,0x00,0x00,
-0x33,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x6c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x65,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x65,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x57,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x57,0x00,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
-0x3d,0x01,0x00,0x00,0x45,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x54,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x56,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4b,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x07,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,0x45,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x48,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4a,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x37,0x00,0x00,0x00,
-0x0c,0x01,0x00,0x00,0x2f,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x0e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0c,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,
-0x0e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
-0x41,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x44,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x1a,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
-0x12,0x01,0x00,0x00,0x3e,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1b,0x01,0x00,0x00,0x19,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x0e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x1d,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1d,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t argsort_f32_len = 4096;
-
-unsigned char clamp_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb6,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc7,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xd7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xd7,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd9,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd9,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xe2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe4,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
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-0xa6,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
-0xa6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa8,0x01,0x00,0x00,0xa3,0x01,0x00,0x00,0xa7,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xaa,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xac,0x01,0x00,0x00,
-0x95,0x01,0x00,0x00,0xab,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00,0xa8,0x01,0x00,0x00,
-0xac,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xaf,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb0,0x01,0x00,0x00,
-0xaf,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb1,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,0xb0,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,
-0xad,0x01,0x00,0x00,0xb1,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0xb2,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0xef,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xb8,0x00,0x05,0x00,0xcf,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xf5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf2,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf8,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xef,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0xba,0x00,0x05,0x00,0xcf,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xa9,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf5,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,
-0xb4,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xb5,0x01,0x00,0x00,0xf8,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xde,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0xe4,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x07,0x01,0x00,0x00,0xb4,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0b,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t clamp_f32_len = 3512;
-
-unsigned char cpy_f16_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x98,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0xc7,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xd6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xd6,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xd6,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd8,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe1,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xe2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xe2,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xe2,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe4,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xef,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0xc6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0xc9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0xd4,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0xd5,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
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-0xf0,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t cpy_f16_f16_len = 3252;
-
-unsigned char cpy_f32_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
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-0x97,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x98,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
-0x97,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x99,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x98,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xed,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf2,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t cpy_f32_f16_len = 3284;
-
-unsigned char cpy_f32_f32_data[] = {
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-0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,
-0x3f,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x42,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00,
-0x42,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x44,0x01,0x00,0x00,0x32,0x01,0x00,0x00,0x43,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x01,0x00,0x00,
-0x40,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x45,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x4f,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x50,0x01,0x00,0x00,
-0x4f,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x51,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x52,0x01,0x00,0x00,
-0x51,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x53,0x01,0x00,0x00,0x50,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00,
-0x53,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,
-0x56,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x71,0x01,0x00,0x00,
-0x6e,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x64,0x01,0x00,0x00,
-0x71,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x79,0x01,0x00,0x00,0x76,0x01,0x00,0x00,0x55,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x82,0x01,0x00,0x00,
-0x79,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x82,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x76,0x01,0x00,0x00,
-0x82,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
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-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x89,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,0x89,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,
-0x6a,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
-0x8b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x8e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,
-0x8e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,
-0x8c,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,0x83,0x01,0x00,0x00,
-0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x96,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xe8,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xeb,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xef,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t cpy_f32_f32_len = 3232;
-
-unsigned char dequant_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x1f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x13,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
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-0x32,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x33,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x33,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
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-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x44,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
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-0x14,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x3b,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x39,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,
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-0x2d,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x1c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x39,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x31,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3d,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x00,0x01,0x00,0x00,
-0xff,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x39,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
-0x35,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x0a,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
-0x0c,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3d,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x0d,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x45,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x45,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_f32_len = 3200;
-
-unsigned char dequant_q2_k_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x10,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x5e,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x5a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x5c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x5c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x5c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x5e,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x5e,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x77,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x79,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x00,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,0x11,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3d,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x80,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x55,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x57,0x00,0x00,0x00,
-0x52,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x58,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x59,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x1e,0x00,0x05,0x00,0x5a,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x57,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x5c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x5d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x5d,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x64,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x67,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x76,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x77,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x78,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x78,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xa1,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xe2,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x14,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x56,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x2a,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
-0x29,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x01,0x01,0x00,0x00,
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-0xd6,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
-0xd8,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
-0x7f,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
-0xd8,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x67,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6c,0x00,0x00,0x00,
-0xdb,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xdb,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xde,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x64,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,
-0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
-0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x52,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xe8,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x67,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x52,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0x7f,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x67,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6c,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xde,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,0x01,0x01,0x00,0x00,
-0x07,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x01,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x01,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q2_k_len = 4032;
-
-unsigned char dequant_q3_k_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x73,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x73,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x73,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x75,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x06,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,0x11,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x37,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x55,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x70,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x71,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x72,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1e,0x00,0x06,0x00,0x73,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x74,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
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-0x21,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
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-0x4d,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xd2,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
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-0x73,0x00,0x04,0x00,0xdb,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
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-0xf1,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
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-0x06,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x44,0x00,0x00,0x00,
-0x6b,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
-0x44,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
-0x01,0x01,0x00,0x00,0xf6,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x02,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
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-0x39,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x41,0x00,0x08,0x00,0x7c,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
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-0x10,0x01,0x00,0x00,0x5d,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x12,0x01,0x00,0x00,
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-0x13,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
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-0x72,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
-0x15,0x01,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x17,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
-0x7c,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,0x77,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x39,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x1b,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,
-0x57,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
-0xab,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
-0x21,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x17,0x01,0x00,0x00,
-0x21,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0xdb,0x00,0x00,0x00,
-0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
-0xdb,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0xea,0x00,0x00,0x00,
-0x23,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
-0x25,0x01,0x00,0x00,0x24,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xdf,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,
-0x35,0x01,0x00,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
-0x2f,0x01,0x00,0x00,0x0a,0x00,0x00,0x00,0x32,0x01,0x00,0x00,
-0x2c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x33,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x3e,0x01,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x33,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q3_k_len = 4804;
-
-unsigned char dequant_q4_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x83,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
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-
-};
-const uint64_t dequant_q4_0_len = 5184;
-
-unsigned char dequant_q4_1_data[] = {
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-0x68,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
-0x6a,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x5e,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x6a,0x01,0x00,0x00,
-0x69,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6c,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x84,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x6f,0x01,0x00,0x00,
-0x61,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x46,0x00,0x00,0x00,
-0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x71,0x01,0x00,0x00,
-0x70,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
-0x73,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x71,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x74,0x01,0x00,0x00,
-0x73,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
-0x75,0x01,0x00,0x00,0x68,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x6c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x75,0x01,0x00,0x00,
-0x74,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x97,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q4_1_len = 5248;
-
-unsigned char dequant_q4_k_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xae,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x14,0x00,0x02,0x00,0x11,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xa1,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x98,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xa1,0x01,0x00,0x00,
-0xa0,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x45,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x3a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x3a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x3a,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x34,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x34,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q4_k_len = 5916;
-
-unsigned char dequant_q5_0_data[] = {
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-0x3d,0x00,0x04,0x00,0x49,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,
-0x8c,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x8e,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
-0xb5,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x86,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x01,0x00,0x00,
-0x64,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0xc4,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x93,0x01,0x00,0x00,0x92,0x01,0x00,0x00,
-0x2d,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
-0x91,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
-0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
-0x96,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x42,0x00,0x00,0x00,0x98,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
-0x97,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0x99,0x01,0x00,0x00,0x98,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x53,0x00,0x00,0x00,0x9a,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x9a,0x01,0x00,0x00,0x99,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
-0xb6,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,
-0x40,0x00,0x00,0x00,0xaf,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x64,0x00,0x00,0x00,
-0x9e,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,0x4a,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
-0x9d,0x01,0x00,0x00,0xa0,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0xa1,0x01,0x00,0x00,
-0x83,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,
-0xa2,0x01,0x00,0x00,0x8f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x42,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x56,0x00,0x00,0x00,
-0xa3,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0xa5,0x01,0x00,0x00,0xa4,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x53,0x00,0x00,0x00,0xa6,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xa6,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xac,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q5_0_len = 6644;
-
-unsigned char dequant_q5_1_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x49,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x49,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x78,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
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-0x00,0x04,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x42,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x45,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x46,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
-0x46,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x49,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x4a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9a,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0xac,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
-0x5f,0x00,0x00,0x00,0x9a,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x9b,0x01,0x00,0x00,
-0x47,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x01,0x00,0x00,0x99,0x01,0x00,0x00,0x9c,0x01,0x00,0x00,
-0x70,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,
-0x9d,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x42,0x00,0x00,0x00,
-0xa0,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,
-0xa0,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x00,0x00,0x00,
-0xa2,0x01,0x00,0x00,0x7a,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x98,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xa2,0x01,0x00,0x00,
-0xa1,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa8,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q5_1_len = 6412;
-
-unsigned char dequant_q5_k_data[] = {
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-0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x0a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x11,0x00,0x00,0x00,0x97,0x01,0x00,0x00,
-0x8a,0x01,0x00,0x00,0x0a,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,
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-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x97,0x01,0x00,0x00,
-0x88,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x88,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q5_k_len = 5980;
-
-unsigned char dequant_q6_k_data[] = {
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-0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x8e,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
-0x72,0x00,0x04,0x00,0x58,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x93,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x21,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x68,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x68,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xde,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfe,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x05,0x01,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0c,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
-0x09,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x03,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x03,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t dequant_q6_k_len = 4272;
-
-unsigned char dequant_q8_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x47,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x48,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x48,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x4a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x5f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x5f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x5f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x61,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
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-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1e,0x00,0x07,0x00,
-0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x32,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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-0x16,0x00,0x03,0x00,0x45,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x47,0x00,0x00,0x00,
-0x46,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,
-0x48,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
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-0x20,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x4b,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0x20,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x44,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,
-0xe2,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0xe2,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x42,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x4f,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xe7,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x45,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x45,0x01,0x00,0x00,
-0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x46,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x42,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x4f,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xf1,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf9,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x46,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x46,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
-0x6b,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0x52,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x45,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x4f,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x00,0x01,0x00,0x00,0xff,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
-0x41,0x00,0x00,0x00,0x47,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x40,0x00,0x00,0x00,
-0x47,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,
-0x05,0x01,0x00,0x00,0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
-0x05,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,
-0x07,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
-0x42,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x52,0x00,0x00,0x00,
-0x07,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0x09,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x4f,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x0a,0x01,0x00,0x00,0x09,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x12,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
-0x48,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x13,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x01,0x00,0x00,
-0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x46,0x00,0x00,0x00,0x15,0x01,0x00,0x00,0x14,0x01,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
-0x15,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x42,0x00,0x00,0x00,
-0x17,0x01,0x00,0x00,0x52,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x18,0x01,0x00,0x00,
-0x17,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4f,0x00,0x00,0x00,
-0x19,0x01,0x00,0x00,0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x12,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x19,0x01,0x00,0x00,
-0x18,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1b,0x01,0x00,0x00,0x41,0x00,0x00,0x00,0x49,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
-0x40,0x00,0x00,0x00,0x49,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
-0x6b,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x1d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,
-0x85,0x00,0x05,0x00,0x42,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
-0x52,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
-0x45,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x21,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x4f,0x00,0x00,0x00,0x23,0x01,0x00,0x00,
-0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,
-0x41,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0x40,0x00,0x00,0x00,
-0x4a,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,
-0x2f,0x01,0x00,0x00,0x2e,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
-0x42,0x00,0x00,0x00,0x30,0x01,0x00,0x00,0x52,0x00,0x00,0x00,
-0x2f,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0x31,0x01,0x00,0x00,0x30,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x4f,0x00,0x00,0x00,0x32,0x01,0x00,0x00,0x61,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x41,0x00,0x00,0x00,
-0x4b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x36,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x41,0x00,0x08,0x00,0x6b,0x00,0x00,0x00,0x37,0x01,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x46,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x37,0x01,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x42,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0x38,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x42,0x00,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x52,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0x73,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x4f,0x00,0x00,0x00,
-0x3c,0x01,0x00,0x00,0x61,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x34,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x3c,0x01,0x00,0x00,
-0x3b,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x88,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t dequant_q8_0_len = 4780;
-
-unsigned char diag_mask_inf_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x38,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x43,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x43,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x43,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x1e,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x36,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x37,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x38,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x39,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x39,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x42,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x43,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x44,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x44,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00,
-0x09,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x36,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x00,0x00,0x80,0xff,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x4c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4d,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0xae,0x00,0x05,0x00,0x1d,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
-0x1d,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x35,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x34,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3e,0x00,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x3f,0x00,0x00,0x00,
-0x52,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x35,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x40,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3e,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x36,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3e,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x49,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x35,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x35,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4c,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4c,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t diag_mask_inf_f32_len = 1504;
-
-unsigned char div_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x67,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,
-0x4b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x15,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2f,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x3d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3d,0x01,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0x79,0x01,0x00,0x00,
-0xa8,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0xab,0x01,0x00,0x00,0x17,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xac,0x01,0x00,0x00,
-0xab,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xad,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0xac,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x01,0x00,0x00,
-0xa9,0x01,0x00,0x00,0xad,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0xb0,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
-0x20,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xb1,0x01,0x00,0x00,0xb0,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,0x9b,0x01,0x00,0x00,
-0xb1,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb3,0x01,0x00,0x00,0xae,0x01,0x00,0x00,0xb2,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,
-0x17,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb6,0x01,0x00,0x00,0xb5,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x01,0x00,0x00,
-0xa5,0x01,0x00,0x00,0xb6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb8,0x01,0x00,0x00,0xb3,0x01,0x00,0x00,
-0xb7,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x47,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0xb8,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xc2,0x01,0x00,0x00,
-0x17,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc3,0x01,0x00,0x00,0xc2,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xc4,0x01,0x00,0x00,
-0x17,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc5,0x01,0x00,0x00,0xc4,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x01,0x00,0x00,
-0xc3,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0xc7,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc8,0x01,0x00,0x00,0xc7,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x01,0x00,0x00,0xc6,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xca,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xc9,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xce,0x01,0x00,0x00,
-0xca,0x01,0x00,0x00,0xc3,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd1,0x01,0x00,0x00,0xce,0x01,0x00,0x00,
-0xc5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd4,0x01,0x00,0x00,0xd1,0x01,0x00,0x00,0xc8,0x01,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x01,0x00,0x00,
-0x33,0x01,0x00,0x00,0xd4,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdc,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,0xdc,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe1,0x01,0x00,0x00,
-0xdd,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe4,0x01,0x00,0x00,0xe1,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe9,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,0xe4,0x01,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x01,0x00,0x00,
-0xe9,0x01,0x00,0x00,0xc8,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf5,0x01,0x00,0x00,0xec,0x01,0x00,0x00,
-0xc8,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x01,0x00,0x00,0xe9,0x01,0x00,0x00,0xf5,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0xf8,0x01,0x00,0x00,
-0x17,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xf9,0x01,0x00,0x00,0xf8,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x01,0x00,0x00,
-0xca,0x01,0x00,0x00,0xf9,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0xfc,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xfd,0x01,0x00,0x00,0xfc,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,
-0xfd,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xff,0x01,0x00,0x00,0xfa,0x01,0x00,0x00,0xfe,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x01,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x02,0x02,0x00,0x00,0x01,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x02,0x00,0x00,
-0xec,0x01,0x00,0x00,0x02,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x04,0x02,0x00,0x00,0xff,0x01,0x00,0x00,
-0x03,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x06,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x07,0x02,0x00,0x00,
-0x06,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x08,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x07,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x02,0x00,0x00,
-0x04,0x02,0x00,0x00,0x08,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x50,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x34,0x01,0x00,0x00,0x09,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,
-0xca,0x01,0x00,0x00,0x4a,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,0x17,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x4c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x50,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,
-0x50,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x52,0x02,0x00,0x00,0xdd,0x01,0x00,0x00,0x51,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x53,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
-0x52,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
-0x55,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x58,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,
-0x58,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xec,0x01,0x00,0x00,0x59,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,
-0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x60,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
-0x60,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x62,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x61,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
-0x62,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
-0x5b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x66,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x88,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
-0x5e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x47,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00,
-0x5d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t div_f32_len = 4276;
-
-unsigned char gelu_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x22,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x36,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x21,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x2a,0x42,0x4c,0x3f,
-0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x13,0x27,0x37,0x3d,0x1d,0x00,0x03,0x00,
-0x35,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x36,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x37,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x00,0x00,0x00,0x3f,0x2b,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x00,0x00,0x00,0x40,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,
-0x09,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x36,0x00,0x05,0x00,
-0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x49,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x49,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x11,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x88,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x26,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x49,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x49,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t gelu_f32_len = 1484;
-
-unsigned char get_rows_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7f,0x00,0x00,0x00,
-0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x70,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7f,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x82,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x85,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x85,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_f16_len = 2996;
-
-unsigned char get_rows_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x19,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x33,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x35,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x70,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x71,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x71,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x71,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x73,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1e,0x00,0x1e,0x00,0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x2c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x32,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x33,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x3b,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x48,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x70,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x71,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x72,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x77,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x78,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x79,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x7a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x7f,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x83,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
-0x0c,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x2f,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7f,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x77,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,
-0x82,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x87,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_f16_f32_len = 3028;
-
-unsigned char get_rows_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x89,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x2f,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x2f,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x45,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x53,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x57,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x56,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x5b,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x65,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7f,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x70,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,
-0x82,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x87,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_f32_len = 3028;
-
-unsigned char get_rows_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x35,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x33,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x33,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x70,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7e,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x7e,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x81,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x84,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t get_rows_f32_f32_len = 2976;
-
-unsigned char get_rows_q4_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x66,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x67,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x69,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x69,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xba,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbb,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xbb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd2,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x00,0x00,0x00,0x41,
-0x17,0x00,0x04,0x00,0x3f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x40,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x43,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x4f,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0xf2,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0xf4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xc6,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x20,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd0,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd3,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q4_0_len = 3704;
-
-unsigned char get_rows_q4_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x66,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x67,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x67,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x69,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x69,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xba,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbb,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xbb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd1,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x00,0x00,0x00,0x41,
-0x17,0x00,0x04,0x00,0x3f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x40,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x42,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x43,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x46,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x4f,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0xa2,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0xac,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xc5,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xc6,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xc5,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xcf,0x00,0x00,0x00,
-0xce,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd2,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd2,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q4_0_f32_len = 3688;
-
-unsigned char get_rows_q4_1_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x57,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x6f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x71,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc2,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xc3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc5,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xda,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x03,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0x01,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
-0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xce,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x20,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd8,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdb,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q4_1_len = 3788;
-
-unsigned char get_rows_q4_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x58,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x57,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x57,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x57,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x6f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x6f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x71,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x71,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc2,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xc3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc5,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
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-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x57,0x00,0x00,0x00,
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-0xfc,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0xfe,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x50,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x03,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x02,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0xaa,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0xb4,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xcd,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xce,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xcd,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xd7,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xda,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xda,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q4_1_f32_len = 3772;
-
-unsigned char get_rows_q5_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x72,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x7d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x94,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x95,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x95,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xe9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xe9,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xe9,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xeb,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xeb,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x00,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x1e,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x1a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
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-0x1b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x2c,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x17,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x17,0x01,0x00,0x00,
-0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
-0x18,0x01,0x00,0x00,0x30,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x2c,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0x19,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
-0xda,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x24,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x3f,0x00,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
-0x24,0x01,0x00,0x00,0x17,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00,
-0xda,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
-0x29,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x51,0x00,0x00,0x00,
-0x32,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x33,0x01,0x00,0x00,
-0x32,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x34,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x56,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x39,0x01,0x00,0x00,0x26,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x36,0x01,0x00,0x00,
-0x39,0x01,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x3b,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x40,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
-0x40,0x01,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x42,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x50,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,
-0x42,0x01,0x00,0x00,0x83,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x45,0x01,0x00,0x00,0x43,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
-0x45,0x01,0x00,0x00,0x12,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x47,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x23,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf4,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x47,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x23,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xfe,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x01,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q5_0_len = 4232;
-
-unsigned char get_rows_q5_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x72,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x7d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x94,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x95,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x95,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0xac,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x81,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,
-0xb4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0xb5,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb8,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0x7f,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x81,0x00,0x00,0x00,
-0xcd,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0xcd,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x23,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0x10,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x11,0x01,0x00,0x00,0x10,0x01,0x00,0x00,
-0x41,0x00,0x08,0x00,0x2c,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x15,0x01,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x01,0x00,0x00,
-0x16,0x01,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x18,0x01,0x00,0x00,0x17,0x01,0x00,0x00,0x30,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x2c,0x00,0x00,0x00,0x1c,0x01,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x1c,0x01,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x1d,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x18,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,
-0x1f,0x01,0x00,0x00,0xda,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,
-0x3f,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x24,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x17,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
-0x24,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x28,0x01,0x00,0x00,0xda,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,0x29,0x01,0x00,0x00,
-0x17,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
-0x51,0x00,0x00,0x00,0x31,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
-0x33,0x01,0x00,0x00,0x56,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x25,0x01,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0x35,0x01,0x00,0x00,0x38,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x33,0x01,0x00,0x00,0x3f,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,0x2b,0x01,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
-0x3c,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x40,0x01,0x00,0x00,
-0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x42,0x01,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x83,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0x42,0x01,0x00,0x00,
-0x4b,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x46,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0x46,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xf3,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf4,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x46,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xf3,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x00,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q5_0_f32_len = 4216;
-
-unsigned char get_rows_q5_1_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x43,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x79,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x8a,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x8d,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8b,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8d,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x95,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x97,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x97,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0x9b,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
-0x9b,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,
-0x98,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xa2,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0xa2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xa5,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,
-0x93,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
-0xa6,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xa8,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0xab,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,
-0xae,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xb8,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0xb8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xba,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x7d,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0xc2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x7d,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xcb,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd1,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0xba,0x00,0x00,0x00,0xd1,0x00,0x00,0x00,0x89,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x41,0x00,0x07,0x00,
-0x20,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
-0x0d,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x0f,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,0x41,0x00,0x07,0x00,
-0x20,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
-0x13,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x15,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0x41,0x00,0x07,0x00,
-0x31,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,
-0x19,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1d,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0xd6,0x00,0x00,0x00,
-0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x1d,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x20,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x24,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0x23,0x01,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
-0x24,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,
-0x41,0x00,0x08,0x00,0x4c,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0x4a,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,
-0x2d,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x30,0x01,0x00,0x00,0x2e,0x01,0x00,0x00,0x51,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x33,0x01,0x00,0x00,
-0x20,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x34,0x01,0x00,0x00,0x30,0x01,0x00,0x00,0x33,0x01,0x00,0x00,
-0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
-0x34,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x37,0x01,0x00,0x00,0x2e,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,
-0x26,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3b,0x01,0x00,0x00,0x37,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,
-0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x3b,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x3d,0x01,0x00,0x00,0x35,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,
-0x3d,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x50,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x15,0x01,0x00,0x00,
-0x15,0x01,0x00,0x00,0x81,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x42,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x41,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x42,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x12,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xe7,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xfa,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfd,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t get_rows_q5_1_len = 4128;
-
-unsigned char get_rows_q5_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x42,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
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-0x14,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0x81,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x3e,0x01,0x00,0x00,
-0x40,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x41,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xef,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x41,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xef,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xf9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q5_1_f32_len = 4112;
-
-unsigned char get_rows_q8_0_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x40,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x64,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x65,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x65,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xb8,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xcf,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x29,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x3f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x3f,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x4d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,
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-0x41,0x00,0x06,0x00,0x20,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,
-0xba,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xbf,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x12,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x20,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xcd,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd0,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q8_0_len = 3644;
-
-unsigned char get_rows_q8_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x4f,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0xba,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x40,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4d,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x64,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x65,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x65,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xb8,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xce,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x29,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x3f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x3f,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x1e,0x00,0x1e,0x00,0x4d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x4e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x51,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x5e,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x64,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xc2,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xcc,0x00,0x00,0x00,
-0xcb,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t get_rows_q8_0_f32_len = 3628;
-
-unsigned char matmul_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x51,0x01,0x00,0x00,
-0x38,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
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-0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x20,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x7f,0x02,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x81,0x02,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x81,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,
-0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x59,0x02,0x00,0x00,
-0x5b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x47,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x64,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x60,0x02,0x00,0x00,0x61,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x64,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
-0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x5f,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x69,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x68,0x02,0x00,0x00,
-0x69,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x6c,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x68,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x54,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x72,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x74,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x72,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0x74,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
-0x5c,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x79,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x74,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xc1,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
-0x67,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x7d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x7b,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
-0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x5c,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0x86,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x3c,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x92,0x02,0x00,0x00,
-0x93,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
-0x94,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
-0x9a,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x9c,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x81,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0x8e,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x9d,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x69,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x68,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x60,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x49,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x48,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x41,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_len = 10316;
-
-unsigned char matmul_f16_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x08,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x69,0x01,0x00,0x00,
-0x68,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x03,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x05,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3c,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x66,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
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-0x8e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
-0xd1,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x90,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x94,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x96,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x98,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x9c,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x97,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xa4,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xa8,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa4,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xad,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xab,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xb7,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
-0xb7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
-0x6c,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbe,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
-0xd9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc9,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
-0xca,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0xcc,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xb1,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xcd,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xad,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x98,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x91,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x91,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd3,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_aligned_len = 11200;
-
-unsigned char matmul_f16_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,
-0x50,0x01,0x00,0x00,0x38,0x02,0x00,0x00,0x81,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x01,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x02,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x04,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x23,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4e,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4e,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_aligned_fp32_len = 10288;
-
-unsigned char matmul_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0x3a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
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-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x49,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x63,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x66,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x62,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x66,0x02,0x00,0x00,
-0x61,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x61,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,
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-0x6a,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x6e,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
-0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x71,0x02,0x00,0x00,
-0x56,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x74,0x02,0x00,0x00,0x71,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x76,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x74,0x02,0x00,0x00,
-0x75,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x75,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x79,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
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-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,
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-0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x3e,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8e,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0x8e,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x94,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x5c,0x01,0x00,0x00,0x9e,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x42,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_f32_len = 10332;
-
-unsigned char matmul_f16_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x12,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
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-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
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-0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,
-0x73,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x8b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x8c,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
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-0x96,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x82,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x9b,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x9f,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xd9,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xa3,0x02,0x00,0x00,
-0xa4,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xa7,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xaf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xc2,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xb6,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xc1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc3,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xce,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd1,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
-0xd5,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xd5,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x01,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xd7,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_f32_aligned_len = 11360;
-
-unsigned char matmul_f16_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,
-0x51,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x01,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x02,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x04,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0xa8,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x47,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x46,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_f32_aligned_fp32_len = 10240;
-
-unsigned char matmul_f16_f32_fp32_data[] = {
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
-0x57,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x45,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x5f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x62,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x62,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x67,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,
-0xaa,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x66,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x6a,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x52,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x72,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x70,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x75,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x76,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,0x76,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x78,0x02,0x00,0x00,
-0x75,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x7b,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x79,0x02,0x00,0x00,
-0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x83,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,
-0x83,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x3a,0x02,0x00,0x00,
-0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x95,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x5c,0x01,0x00,0x00,0x9a,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f16_f32_fp32_len = 10260;
-
-unsigned char matmul_f16_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00,
-0x51,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x06,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x20,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x21,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x46,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f16_fp32_len = 10276;
-
-unsigned char matmul_f32_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
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-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0x47,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
-0x3a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x63,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x66,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
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-0x56,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x74,0x02,0x00,0x00,0x71,0x02,0x00,0x00,
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-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
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-0x99,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x11,0x01,0x00,0x00,0x9e,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x42,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f32_f16_len = 10332;
-
-unsigned char matmul_f32_f16_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x12,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
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-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0x05,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,0x73,0x01,0x00,0x00,
-0x73,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
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-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x10,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x03,0x01,0x00,0x00,
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-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x96,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x82,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x9b,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x9f,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xd9,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
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-0xa7,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xaf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xad,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xc2,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xb6,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xc1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xce,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd1,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
-0xd5,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xd5,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x07,0x01,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xd7,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_f16_aligned_len = 11360;
-
-unsigned char matmul_f32_f16_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x45,0x01,0x00,0x00,
-0x4d,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x00,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x01,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x01,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x47,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x46,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_f16_aligned_fp32_len = 10240;
-
-unsigned char matmul_f32_f16_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x59,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
-0x57,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x45,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x5f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x62,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x5e,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x62,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
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-0x66,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x6a,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x52,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x72,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x70,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x75,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x76,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,0x76,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x78,0x02,0x00,0x00,
-0x75,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x7b,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x79,0x02,0x00,0x00,
-0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x83,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,
-0x83,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x3a,0x02,0x00,0x00,
-0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x95,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x10,0x01,0x00,0x00,0x9a,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_f32_f16_fp32_len = 10260;
-
-unsigned char matmul_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x47,0x01,0x00,0x00,
-0x52,0x01,0x00,0x00,0x3a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x03,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x04,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x06,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x22,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x50,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0xf9,0x00,0x02,0x00,0x4b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x43,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x43,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x42,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_f32_len = 10324;
-
-unsigned char matmul_f32_f32_aligned_data[] = {
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-0xec,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
-0xda,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xda,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x07,0x01,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0xce,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xdc,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_f32_aligned_len = 11432;
-
-unsigned char matmul_f32_f32_aligned_fp32_data[] = {
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-0xce,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
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-0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x55,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x3e,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x58,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x57,0x02,0x00,0x00,0x58,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x5b,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
-0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x56,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5d,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
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-0x95,0x02,0x00,0x00,0x60,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x63,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
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-0x60,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
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-0x69,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x6b,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x69,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
-0x6b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
-0x53,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x70,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0xc2,0x00,0x00,0x00,0x72,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
-0x5e,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x74,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x72,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
-0x74,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,
-0x53,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0x7d,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x81,0x02,0x00,0x00,0x33,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x83,0x02,0x00,0x00,
-0x81,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,0x83,0x02,0x00,0x00,
-0xa3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x87,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0x87,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
-0xa3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0xca,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x05,0x01,0x00,0x00,
-0x93,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0x85,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x93,0x02,0x00,0x00,
-0x92,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x74,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0xa3,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x57,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x40,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x38,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x38,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x35,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x37,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_f32_aligned_fp32_len = 10124;
-
-unsigned char matmul_f32_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xd2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
-0x44,0x01,0x00,0x00,0x4f,0x01,0x00,0x00,0x33,0x02,0x00,0x00,
-0x7c,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x03,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1e,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1f,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4c,0x01,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x39,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x3b,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_f32_f32_fp32_len = 10208;
-
-unsigned char matmul_id_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xaf,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_f16_len = 10788;
-
-unsigned char matmul_id_f16_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x5e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x86,0x01,0x00,0x00,
-0x8c,0x01,0x00,0x00,0xc1,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
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-0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
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-0xe9,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xec,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_f16_aligned_len = 11888;
-
-unsigned char matmul_id_f16_aligned_fp32_data[] = {
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-0x2b,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xd9,0x00,0x00,0x00,
-0x31,0x02,0x00,0x00,0xe1,0x01,0x00,0x00,0x1f,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,0x32,0x02,0x00,0x00,
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-0x3e,0x00,0x03,0x00,0x34,0x02,0x00,0x00,0x36,0x02,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0x0d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x08,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x08,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3d,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x05,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x07,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3f,0x02,0x00,0x00,
-0xea,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfd,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xff,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9d,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x41,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9c,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x43,0x02,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x4e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xde,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x48,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x49,0x02,0x00,0x00,0xb5,0x00,0x00,0x00,
-0x48,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4e,0x02,0x00,0x00,0x3b,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4f,0x02,0x00,0x00,
-0xa4,0x00,0x00,0x00,0x4e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x51,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
-0x20,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x54,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,
-0x57,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0xcc,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x53,0x02,0x00,0x00,0x54,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x57,0x02,0x00,0x00,
-0x52,0x02,0x00,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0x52,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,0x5f,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0x42,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x5b,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x5f,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,
-0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0x43,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x49,0x02,0x00,0x00,
-0x63,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x66,0x02,0x00,0x00,0x46,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x67,0x02,0x00,0x00,
-0x64,0x02,0x00,0x00,0x66,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0xe9,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6c,0x02,0x00,0x00,0x4f,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
-0x4a,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0x6e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0x74,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x73,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x77,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
-0x73,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,
-0x6f,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xae,0x00,0x05,0x00,
-0x6c,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
-0xc7,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x80,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x98,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0x91,0x00,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x8d,0x00,0x00,0x00,
-0x85,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x20,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x44,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x89,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8d,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0x51,0x00,0x05,0x00,0x8c,0x00,0x00,0x00,
-0x93,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0x93,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x96,0x02,0x00,0x00,0x13,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
-0x96,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x98,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
-0x51,0x00,0x05,0x00,0x8c,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x85,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x13,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0x67,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
-0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
-0xad,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xd9,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0xd6,0x00,0x00,0x00,
-0xaf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,
-0xb1,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0xb2,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
-0x15,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb3,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x74,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xcd,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_f16_aligned_fp32_len = 10976;
-
-unsigned char matmul_id_f16_f32_data[] = {
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-0x7e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x51,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x49,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaf,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x46,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x48,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_f16_f32_len = 10804;
-
-unsigned char matmul_id_f16_f32_aligned_data[] = {
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-0xf6,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_f16_f32_aligned_len = 12048;
-
-unsigned char matmul_id_f16_f32_aligned_fp32_data[] = {
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-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb9,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_f16_f32_aligned_fp32_len = 10928;
-
-unsigned char matmul_id_f16_f32_fp32_data[] = {
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-0x60,0x02,0x00,0x00,0x5d,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x62,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x20,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0x68,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
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-0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
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-0x71,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0x44,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,0x84,0x02,0x00,0x00,
-0x76,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x87,0x02,0x00,0x00,
-0x13,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x87,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0x85,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x51,0x00,0x05,0x00,
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-0x8c,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x13,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x58,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x93,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xc8,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
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-0x9a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x44,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xd5,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0xa1,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x72,0x01,0x00,0x00,
-0xa3,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x15,0x00,0x00,0x00,
-0x95,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xa3,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4c,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x45,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x45,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x42,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x44,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xac,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_f16_f32_fp32_len = 10732;
-
-unsigned char matmul_id_f16_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x2e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x10,0x01,0x00,0x00,
-0x4e,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x82,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0x68,0x02,0x00,0x00,
-0xbe,0x02,0x00,0x00,0xc8,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x64,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x68,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
-0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,
-0x60,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xae,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x71,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x6f,0x02,0x00,0x00,
-0x70,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x70,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x97,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0x90,0x00,0x00,0x00,
-0x6c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,
-0x76,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x20,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
-0x7e,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x44,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,
-0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,
-0x84,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x84,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x87,0x02,0x00,0x00,0x13,0x00,0x00,0x00,0x86,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,
-0x87,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x89,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x76,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
-0x13,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0x90,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x93,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x58,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0x93,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
-0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xd8,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0xd5,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0x82,0x02,0x00,0x00,
-0x15,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x65,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xbe,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x45,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x45,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x42,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_f16_fp32_len = 10748;
-
-unsigned char matmul_id_f32_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x31,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0x05,0x01,0x00,0x00,0x10,0x01,0x00,0x00,0x4f,0x01,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x86,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa2,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc5,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0d,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
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-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0x57,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
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-0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa9,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x69,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x69,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x66,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x51,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x49,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaf,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x46,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x48,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_f32_f16_len = 10804;
-
-unsigned char matmul_id_f32_f16_aligned_data[] = {
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-0xf9,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xae,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
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-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x94,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x96,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,
-0x04,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf6,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_f32_f16_aligned_len = 12048;
-
-unsigned char matmul_id_f32_f16_aligned_fp32_data[] = {
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-0x6c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
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-0x58,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0x72,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,0x75,0x02,0x00,0x00,
-0xca,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
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-0x20,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,
-0x86,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0x44,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x87,0x02,0x00,0x00,0x86,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8b,0x02,0x00,0x00,
-0x86,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x86,0x02,0x00,0x00,0x51,0x00,0x05,0x00,0x8c,0x00,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
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-0xa0,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0xab,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xd9,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xd6,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,
-0xaf,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x0f,0x01,0x00,0x00,0xb0,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,
-0x15,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb0,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x87,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xca,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb6,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x52,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb9,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_f32_f16_aligned_fp32_len = 10928;
-
-unsigned char matmul_id_f32_f16_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x2d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,
-0x4c,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x82,0x02,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,
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-0x48,0x00,0x04,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc8,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x0d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x0d,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x0d,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
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-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x65,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x65,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4c,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x45,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x45,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x42,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x44,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xac,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_f32_f16_fp32_len = 10732;
-
-unsigned char matmul_id_f32_f32_data[] = {
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-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x46,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x48,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb0,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_f32_f32_len = 10796;
-
-unsigned char matmul_id_f32_f32_aligned_data[] = {
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
-0x12,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,0x0a,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x94,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x94,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
-0x09,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x91,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x93,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfb,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_f32_f32_aligned_len = 12120;
-
-unsigned char matmul_id_f32_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x24,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x68,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
-0xc0,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x48,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb2,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_f32_f32_aligned_fp32_len = 10812;
-
-unsigned char matmul_id_f32_f32_fp32_data[] = {
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-0xa2,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x44,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x77,0x02,0x00,0x00,
-0x76,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x7b,0x02,0x00,0x00,0x76,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
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-0x8b,0x00,0x00,0x00,0x81,0x02,0x00,0x00,0x73,0x02,0x00,0x00,
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-0x85,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
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-0x88,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x13,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,
-0x86,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
-0x55,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x92,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0xb7,0x02,0x00,0x00,0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x98,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x97,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0xd5,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xcf,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x1a,0x01,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x15,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xa0,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0xc0,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x62,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x49,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x42,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x42,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xb7,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x41,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa9,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_f32_f32_fp32_len = 10680;
-
-unsigned char matmul_id_q2_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x75,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0xdf,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xed,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xef,0x02,0x00,0x00,0x11,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf1,0x02,0x00,0x00,0x0b,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xac,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x97,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
-0x09,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x94,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x96,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf5,0x02,0x00,0x00,0x08,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q2_k_f32_len = 11692;
-
-unsigned char matmul_id_q2_k_f32_aligned_data[] = {
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-0x18,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,
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-0xba,0x01,0x00,0x00,0x1b,0x03,0x00,0x00,0xfa,0x02,0x00,0x00,
-0x15,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x1b,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf0,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdd,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x03,0x00,0x00,
-0x39,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xda,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x21,0x03,0x00,0x00,0x37,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x23,0x03,0x00,0x00,0x36,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xba,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x24,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x24,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q2_k_f32_aligned_len = 12488;
-
-unsigned char matmul_id_q2_k_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x75,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
-0x2a,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0xaf,0x01,0x00,0x00,
-0xb6,0x01,0x00,0x00,0xd1,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
-0xed,0x02,0x00,0x00,0x16,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0xd9,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0xd6,0x00,0x00,0x00,
-0xef,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,
-0xf1,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0xb8,0x01,0x00,0x00,0xf2,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,
-0x15,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xf2,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,0x16,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
-0x10,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf8,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x94,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x94,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x91,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x93,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xfb,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q2_k_f32_aligned_fp32_len = 11756;
-
-unsigned char matmul_id_q2_k_f32_fp32_data[] = {
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-0x6e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0xb9,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
-0x13,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,
-0xc8,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0x51,0x00,0x05,0x00,
-0x8b,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0x13,0x00,0x00,0x00,
-0xd0,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xd2,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0xd2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
-0x0a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xda,0x02,0x00,0x00,0x01,0x03,0x00,0x00,0xc8,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xda,0x02,0x00,0x00,0x04,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xdd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe0,0x02,0x00,0x00,0x02,0x03,0x00,0x00,0x44,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,
-0xde,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
-0x0a,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,
-0xe4,0x02,0x00,0x00,0xd5,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,
-0xe4,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0xb5,0x01,0x00,0x00,
-0xe6,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x15,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xe6,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe8,0x02,0x00,0x00,0x0a,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0x04,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x90,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
-0x02,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x88,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xee,0x02,0x00,0x00,0x01,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x87,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q2_k_f32_fp32_len = 11572;
-
-unsigned char matmul_id_q3_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xff,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0xb5,0x01,0x00,0x00,
-0x14,0x02,0x00,0x00,0x1f,0x02,0x00,0x00,0x4c,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x44,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x78,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x79,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x79,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc8,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3c,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x40,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x40,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x40,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x40,0x01,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x41,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x42,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x42,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x42,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x44,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x44,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf0,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xf1,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1c,0x02,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x1d,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1f,0x02,0x00,0x00,
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-0x1f,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x49,0x03,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4a,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4a,0x03,0x00,0x00,
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-0x4c,0x03,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
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-
-};
-const uint64_t matmul_id_q3_k_f32_len = 13596;
-
-unsigned char matmul_id_q3_k_f32_aligned_data[] = {
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-0xf9,0x00,0x02,0x00,0x3a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
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-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q3_k_f32_aligned_len = 14392;
-
-unsigned char matmul_id_q3_k_f32_aligned_fp32_data[] = {
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-0xf8,0x00,0x02,0x00,0x34,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x76,0x03,0x00,0x00,0x8c,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x31,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x33,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x1c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x1c,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x78,0x03,0x00,0x00,
-0x8a,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x19,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x1b,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x14,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x14,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7a,0x03,0x00,0x00,0x89,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x11,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x13,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x7b,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q3_k_f32_aligned_fp32_len = 13660;
-
-unsigned char matmul_id_q3_k_f32_fp32_data[] = {
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x68,0x03,0x00,0x00,
-0x86,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x3d,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x28,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x28,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6a,0x03,0x00,0x00,0x80,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x25,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x27,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x10,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x10,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6c,0x03,0x00,0x00,0x7e,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0f,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x03,0x00,0x00,
-0x7d,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x05,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6f,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_q3_k_f32_fp32_len = 13476;
-
-unsigned char matmul_id_q4_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x42,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x11,0x01,0x00,0x00,
-0x32,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
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-0x11,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x91,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x93,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xd8,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x66,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc2,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x65,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q4_0_f32_len = 11036;
-
-unsigned char matmul_id_q4_0_f32_aligned_data[] = {
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-0xf5,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_q4_0_f32_aligned_len = 11844;
-
-unsigned char matmul_id_q4_0_f32_aligned_fp32_data[] = {
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-0x6a,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
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-0x9d,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x44,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xaf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xd9,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0xd6,0x00,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,
-0xc1,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x87,0x01,0x00,0x00,0xc2,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
-0x15,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xc2,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
-0xde,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc8,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x69,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q4_0_f32_aligned_fp32_len = 11096;
-
-unsigned char matmul_id_q4_0_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x46,0x02,0x00,0x00,
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-0x20,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x57,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
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-0x55,0x02,0x00,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0x55,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x5f,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0x62,0x02,0x00,0x00,
-0xcf,0x02,0x00,0x00,0x42,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
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-0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x71,0x02,0x00,0x00,
-0x4a,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x72,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
-0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x74,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0x5d,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,
-0xd1,0x02,0x00,0x00,0xc8,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x76,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x7a,0x02,0x00,0x00,0x75,0x02,0x00,0x00,
-0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x75,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,
-0x72,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0xae,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0x81,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x83,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x81,0x02,0x00,0x00,
-0x82,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x97,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x90,0x00,0x00,0x00,
-0x7e,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
-0x20,0x00,0x00,0x00,0x83,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0x44,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x8c,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x90,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8b,0x02,0x00,0x00,0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,
-0x96,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
-0x96,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x99,0x02,0x00,0x00,0x13,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x13,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xd8,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0xd5,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x84,0x01,0x00,0x00,0xb5,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
-0x15,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xd1,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x74,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbb,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0xce,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x54,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q4_0_f32_fp32_len = 10916;
-
-unsigned char matmul_id_q4_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x43,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x11,0x01,0x00,0x00,
-0x37,0x01,0x00,0x00,0x68,0x01,0x00,0x00,0x73,0x01,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
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-0xc0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc3,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
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-0x98,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x68,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x63,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x63,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc9,0x02,0x00,0x00,0xda,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x62,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xca,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q4_1_f32_len = 11104;
-
-unsigned char matmul_id_q4_1_f32_aligned_data[] = {
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-0xb3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,
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-0x0d,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,
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-0xf7,0x02,0x00,0x00,0x0b,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x93,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0x0a,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x90,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x92,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xfa,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q4_1_f32_aligned_len = 11912;
-
-unsigned char matmul_id_q4_1_f32_aligned_fp32_data[] = {
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-0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0xd0,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q4_1_f32_aligned_fp32_len = 11164;
-
-unsigned char matmul_id_q4_1_f32_fp32_data[] = {
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-0x18,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x18,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x43,0x02,0x00,0x00,
-0xf9,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x15,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x17,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x10,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
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-0x45,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x0d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x08,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x08,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x47,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x05,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x07,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa5,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xa5,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x49,0x02,0x00,0x00,
-0xed,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa2,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xa4,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
-0x4e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xdd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xdf,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x50,0x02,0x00,0x00,0x37,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x51,0x02,0x00,0x00,0xb4,0x00,0x00,0x00,0x50,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,
-0x3b,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x57,0x02,0x00,0x00,0xa3,0x00,0x00,0x00,
-0x56,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0x5f,0x02,0x00,0x00,
-0xd3,0x02,0x00,0x00,0xcb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x5b,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x5f,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,
-0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xd4,0x02,0x00,0x00,0x20,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
-0xc0,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0x67,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
-0x42,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x63,0x02,0x00,0x00,
-0x64,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x67,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
-0x43,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6c,0x02,0x00,0x00,0x51,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,
-0x46,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0x6e,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x73,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0xf1,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x74,0x02,0x00,0x00,
-0x57,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x76,0x02,0x00,0x00,0x4a,0x00,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x77,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0x76,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xd6,0x02,0x00,0x00,0x20,0x00,0x00,0x00,0x62,0x02,0x00,0x00,
-0xbe,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xc8,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x7b,0x02,0x00,0x00,
-0x7c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x7f,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x83,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0xae,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
-0x86,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x88,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x86,0x02,0x00,0x00,0x87,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x97,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x90,0x00,0x00,0x00,0x83,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0x95,0x02,0x00,0x00,
-0xdc,0x02,0x00,0x00,0x44,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x91,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x95,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,
-0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x13,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0x9c,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x51,0x00,0x05,0x00,
-0x8b,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xa3,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0x13,0x00,0x00,0x00,
-0xa4,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xa8,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xdc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xae,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0xc8,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
-0xae,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,
-0xb1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0x44,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
-0xdc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xd5,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x89,0x01,0x00,0x00,
-0xba,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x15,0x00,0x00,0x00,
-0xac,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xba,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x91,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x64,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc2,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q4_1_f32_fp32_len = 10984;
-
-unsigned char matmul_id_q4_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb5,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
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-0x15,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x18,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x41,0x03,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x23,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
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-0x25,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,
-0x27,0x03,0x00,0x00,0x26,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0xf2,0x01,0x00,0x00,0x28,0x03,0x00,0x00,0x07,0x03,0x00,0x00,
-0x15,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x28,0x03,0x00,0x00,0x27,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2a,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfd,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xff,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xea,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,
-0x44,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2e,0x03,0x00,0x00,0x42,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xca,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,0x41,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x31,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x31,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q4_k_f32_len = 12524;
-
-unsigned char matmul_id_q4_k_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xdf,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x91,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0x84,0x01,0x00,0x00,
-0xec,0x01,0x00,0x00,0xf4,0x01,0x00,0x00,0x36,0x03,0x00,0x00,
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-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x42,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x44,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x79,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x7a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7c,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x59,0x03,0x00,0x00,0x79,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x2c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x19,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x19,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5b,0x03,0x00,0x00,0x73,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x16,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x18,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x03,0x00,0x00,
-0x71,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5f,0x03,0x00,0x00,0x70,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x60,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x60,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q4_k_f32_aligned_len = 13336;
-
-unsigned char matmul_id_q4_k_f32_aligned_fp32_data[] = {
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-0xb5,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x7c,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x83,0x01,0x00,0x00,0xe9,0x01,0x00,0x00,0xf0,0x01,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,
-0x4f,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x04,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xef,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x31,0x03,0x00,0x00,0x49,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xee,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x33,0x03,0x00,0x00,0x47,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcf,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x35,0x03,0x00,0x00,
-0x46,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xce,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x36,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x36,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_q4_k_f32_aligned_fp32_len = 12588;
-
-unsigned char matmul_id_q4_k_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xae,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
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-0x7b,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,
-0x83,0x01,0x00,0x00,0xcd,0x01,0x00,0x00,0xd8,0x01,0x00,0x00,
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-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0xaa,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd5,0x01,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x25,0x03,0x00,0x00,
-0x3d,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x27,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xca,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x29,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc0,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x2a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q4_k_f32_fp32_len = 12404;
-
-unsigned char matmul_id_q5_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x6c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x3e,0x00,0x03,0x00,0xe4,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,
-0x08,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe8,0x02,0x00,0x00,0x02,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0x00,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x86,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
-0xff,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x83,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x85,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xed,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xed,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_q5_0_f32_len = 11532;
-
-unsigned char matmul_id_q5_0_f32_aligned_data[] = {
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-0x3d,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,0x13,0x03,0x00,0x00,
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-0x06,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x14,0x03,0x00,0x00,
-0x13,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x16,0x03,0x00,0x00,0x38,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xeb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x18,0x03,0x00,0x00,0x32,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x30,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1c,0x03,0x00,0x00,0x2f,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x1d,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1d,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q5_0_f32_aligned_len = 12340;
-
-unsigned char matmul_id_q5_0_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x6d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
-0x13,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0xa6,0x01,0x00,0x00,
-0xad,0x01,0x00,0x00,0xc9,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
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-0xf3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_q5_0_f32_aligned_fp32_len = 11592;
-
-unsigned char matmul_id_q5_0_f32_fp32_data[] = {
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-0x3e,0x00,0x03,0x00,0xdd,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
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-0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe1,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x87,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,
-0xf8,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe6,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_q5_0_f32_fp32_len = 11412;
-
-unsigned char matmul_id_q5_1_f32_data[] = {
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-0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0xfc,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q5_1_f32_len = 11428;
-
-unsigned char matmul_id_q5_1_f32_aligned_data[] = {
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-0x10,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,0x34,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd2,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x14,0x03,0x00,0x00,
-0x2e,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd1,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xba,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xba,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x16,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x18,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x19,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x19,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q5_1_f32_aligned_len = 12236;
-
-unsigned char matmul_id_q5_1_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x65,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x2e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x91,0x00,0x00,0x00,
-0x12,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0xa2,0x01,0x00,0x00,
-0xa9,0x01,0x00,0x00,0xc5,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,
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-0xe6,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x15,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xe6,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe8,0x02,0x00,0x00,0x0a,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0x04,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x90,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,
-0x02,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x88,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xee,0x02,0x00,0x00,0x01,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x87,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q5_1_f32_aligned_fp32_len = 11488;
-
-unsigned char matmul_id_q5_1_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x5d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
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-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x24,0x02,0x00,0x00,
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-0xc1,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xc3,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
-0x4e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xdd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xdf,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
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-0x70,0x02,0x00,0x00,0xb4,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x75,0x02,0x00,0x00,
-0x3b,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
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-0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0x7e,0x02,0x00,0x00,
-0xf4,0x02,0x00,0x00,0xcb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x7a,0x02,0x00,0x00,0x7b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x80,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xf5,0x02,0x00,0x00,0x20,0x00,0x00,0x00,0x79,0x02,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
-0x42,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x82,0x02,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
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-0x43,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x8e,0x02,0x00,0x00,0x8b,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x92,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,0x10,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x93,0x02,0x00,0x00,
-0x76,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x4a,0x00,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x96,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x98,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xf7,0x02,0x00,0x00,0x20,0x00,0x00,0x00,0x81,0x02,0x00,0x00,
-0xdd,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,
-0xc8,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x9a,0x02,0x00,0x00,
-0x9b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x9e,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
-0xf7,0x02,0x00,0x00,0xae,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xa7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xa5,0x02,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa7,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x97,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0x90,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xfd,0x02,0x00,0x00,0x44,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xb0,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xaf,0x02,0x00,0x00,
-0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x13,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x51,0x00,0x05,0x00,
-0x8b,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc2,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0x13,0x00,0x00,0x00,
-0xc3,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc7,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
-0xc7,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
-0xfd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,0xc8,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0xd0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd3,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0x44,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
-0xd1,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
-0xfd,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xd5,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
-0xd7,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0xa8,0x01,0x00,0x00,
-0xd9,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0x15,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xd9,0x02,0x00,0x00,
-0xd8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x98,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x83,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xf5,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x80,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe1,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q5_1_f32_fp32_len = 11308;
-
-unsigned char matmul_id_q5_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xda,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
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-0x4c,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4f,0x03,0x00,0x00,0x6f,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x22,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x24,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x0f,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0f,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x51,0x03,0x00,0x00,0x69,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0c,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x53,0x03,0x00,0x00,
-0x67,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xef,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x55,0x03,0x00,0x00,0x66,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xec,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xee,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x56,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x56,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q5_k_f32_len = 13048;
-
-unsigned char matmul_id_q5_k_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x04,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
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-
-};
-const uint64_t matmul_id_q5_k_f32_aligned_len = 13860;
-
-unsigned char matmul_id_q5_k_f32_aligned_fp32_data[] = {
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-0x06,0x00,0x00,0x00,0x54,0x03,0x00,0x00,0x74,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x27,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x29,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x14,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x14,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x56,0x03,0x00,0x00,
-0x6e,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x11,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x13,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x58,0x03,0x00,0x00,0x6c,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5a,0x03,0x00,0x00,0x6b,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x5b,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q5_k_f32_aligned_fp32_len = 13112;
-
-unsigned char matmul_id_q5_k_f32_fp32_data[] = {
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-0x45,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x48,0x03,0x00,0x00,0x68,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1d,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x08,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4a,0x03,0x00,0x00,0x62,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x05,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x07,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xf0,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4c,0x03,0x00,0x00,
-0x60,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xed,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4e,0x03,0x00,0x00,0x5f,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x4f,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4f,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q5_k_f32_fp32_len = 12928;
-
-unsigned char matmul_id_q6_k_f32_data[] = {
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-0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,
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-0xd0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd0,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x2b,0x03,0x00,0x00,
-0x20,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0x15,0x03,0x00,0x00,
-0xd3,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
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-0x90,0x00,0x00,0x00,0xda,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x8c,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe6,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x31,0x03,0x00,0x00,0x20,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0x13,0x03,0x00,0x00,0xe7,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0x31,0x03,0x00,0x00,
-0x44,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xe8,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xec,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe7,0x02,0x00,0x00,0x51,0x00,0x05,0x00,
-0x8b,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xf3,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0x13,0x00,0x00,0x00,
-0xf4,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
-0xf6,0x02,0x00,0x00,0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,
-0xf9,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,
-0xf9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0xfc,0x02,0x00,0x00,0x13,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
-0xfc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfe,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xff,0x02,0x00,0x00,
-0xf7,0x02,0x00,0x00,0xfe,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x01,0x03,0x00,0x00,0xff,0x02,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x03,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0x31,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x05,0x03,0x00,0x00,
-0x28,0x03,0x00,0x00,0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x05,0x03,0x00,0x00,
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-0x09,0x03,0x00,0x00,0x07,0x03,0x00,0x00,0x08,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0b,0x03,0x00,0x00,
-0x29,0x03,0x00,0x00,0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,0x09,0x03,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0e,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,0x31,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,0x0f,0x03,0x00,0x00,
-0xd5,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xcf,0x00,0x00,0x00,0x10,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0xdb,0x01,0x00,0x00,0x11,0x03,0x00,0x00,
-0xf0,0x02,0x00,0x00,0x15,0x00,0x00,0x00,0x03,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x11,0x03,0x00,0x00,0x10,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x13,0x03,0x00,0x00,
-0x31,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe8,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x15,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0x29,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xba,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
-0x28,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1a,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_q6_k_f32_len = 12216;
-
-unsigned char matmul_id_q6_k_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xba,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x91,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,0x4e,0x01,0x00,0x00,
-0xd4,0x01,0x00,0x00,0xdc,0x01,0x00,0x00,0x1e,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x3e,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x41,0x03,0x00,0x00,0x5f,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x14,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x16,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x01,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x43,0x03,0x00,0x00,0x59,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfe,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x03,0x00,0x00,
-0x57,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe8,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x47,0x03,0x00,0x00,0x56,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xde,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x48,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x48,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q6_k_f32_aligned_len = 13012;
-
-unsigned char matmul_id_q6_k_f32_aligned_fp32_data[] = {
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-0x91,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x7c,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
-0x4f,0x01,0x00,0x00,0xd2,0x01,0x00,0x00,0xd9,0x01,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
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-0x15,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x18,0x03,0x00,0x00,0x36,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xed,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,0x30,0x03,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc0,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x2e,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbf,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x1f,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1f,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q6_k_f32_aligned_fp32_len = 12280;
-
-unsigned char matmul_id_q6_k_f32_fp32_data[] = {
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-0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0x3b,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xa3,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa9,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x21,0x03,0x00,0x00,0x20,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
-0x12,0x03,0x00,0x00,0xac,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0x21,0x03,0x00,0x00,
-0xcb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xab,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xaf,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x22,0x03,0x00,0x00,
-0x20,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x10,0x03,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
-0xb7,0x02,0x00,0x00,0x22,0x03,0x00,0x00,0x42,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xb3,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb7,0x02,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x22,0x03,0x00,0x00,0x43,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
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-0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbf,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x21,0x03,0x00,0x00,0x41,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x4a,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x24,0x03,0x00,0x00,
-0x20,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
-0xcc,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
-0xcf,0x02,0x00,0x00,0x24,0x03,0x00,0x00,0xc8,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xcf,0x02,0x00,0x00,
-0xca,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xca,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd3,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x24,0x03,0x00,0x00,
-0xae,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xd3,0x02,0x00,0x00,0x1e,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xd8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd6,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd8,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x97,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
-0x90,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x8c,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xdf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdf,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x2a,0x03,0x00,0x00,0x20,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
-0x0c,0x03,0x00,0x00,0xe0,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0x2a,0x03,0x00,0x00,
-0x44,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xe1,0x02,0x00,0x00,
-0xe0,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe5,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0x51,0x00,0x05,0x00,
-0x8b,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xec,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0xee,0x02,0x00,0x00,0x13,0x00,0x00,0x00,
-0xed,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xef,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf0,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
-0xef,0x02,0x00,0x00,0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
-0xf2,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0xf5,0x02,0x00,0x00,0x13,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
-0xf5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf7,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,
-0xf0,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfc,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,0x2a,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,
-0x21,0x03,0x00,0x00,0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0xfe,0x02,0x00,0x00,
-0x24,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x02,0x03,0x00,0x00,0x00,0x03,0x00,0x00,0x01,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,
-0x22,0x03,0x00,0x00,0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x02,0x03,0x00,0x00,
-0x04,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x07,0x03,0x00,0x00,0x05,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
-0xd5,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xcf,0x00,0x00,0x00,0x09,0x03,0x00,0x00,0x08,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0xd9,0x01,0x00,0x00,0x0a,0x03,0x00,0x00,
-0xe9,0x02,0x00,0x00,0x15,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x0a,0x03,0x00,0x00,0x09,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x2a,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0e,0x03,0x00,0x00,0x24,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00,0x22,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xac,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
-0x21,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xab,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x13,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x13,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_id_q6_k_f32_fp32_len = 12096;
-
-unsigned char matmul_id_q8_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
-0x32,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
-0x9c,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
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-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,
-0xd9,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x67,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc3,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x66,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x5c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q8_0_f32_len = 11060;
-
-unsigned char matmul_id_q8_0_f32_aligned_data[] = {
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-};
-const uint64_t matmul_id_q8_0_f32_aligned_len = 11888;
-
-unsigned char matmul_id_q8_0_f32_aligned_fp32_data[] = {
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-0xab,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xa1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,
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-0x4e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xde,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
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-0x35,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xb5,0x00,0x00,0x00,0x59,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5f,0x02,0x00,0x00,
-0x3b,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0xa4,0x00,0x00,0x00,
-0x5f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0xe0,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0x65,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,0x68,0x02,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xcc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x64,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x68,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
-0x64,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x63,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6a,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x02,0x00,0x00,0x20,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6c,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0x42,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x6c,0x02,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0x6b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
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-0x75,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0x74,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x77,0x02,0x00,0x00,
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-0x77,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7c,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0xfa,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
-0x60,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,0x4a,0x00,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x80,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x20,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,
-0xc7,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x6c,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xc9,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x84,0x02,0x00,0x00,
-0x85,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x88,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,0x80,0x02,0x00,0x00,
-0xdf,0x02,0x00,0x00,0xae,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,
-0x8f,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x91,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x8f,0x02,0x00,0x00,0x90,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,
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-0x95,0x02,0x00,0x00,0x91,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x8d,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x98,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x98,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6c,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0x44,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x9a,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x9e,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,
-0x51,0x00,0x05,0x00,0x8c,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,
-0x96,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
-0x13,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0x51,0x00,0x05,0x00,
-0x8c,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
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-0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0x13,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xaf,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xaf,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb1,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xb1,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb7,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xb7,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0x44,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xd9,0x00,0x00,0x00,
-0xc1,0x02,0x00,0x00,0xd6,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
-0xc1,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x88,0x01,0x00,0x00,
-0xc3,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x15,0x00,0x00,0x00,
-0xb5,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xc3,0x02,0x00,0x00,
-0xc2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
-0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
-0xdd,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x6c,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x65,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x65,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x62,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x64,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q8_0_f32_aligned_fp32_len = 11140;
-
-unsigned char matmul_id_q8_0_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x38,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x02,0x00,0x00,
-0xce,0x02,0x00,0x00,0x4e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdf,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4d,0x02,0x00,0x00,0xb4,0x00,0x00,0x00,
-0x4c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x52,0x02,0x00,0x00,0x3b,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x53,0x02,0x00,0x00,
-0xa3,0x00,0x00,0x00,0x52,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x20,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x58,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
-0x5b,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0xcb,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x57,0x02,0x00,0x00,0x58,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x5b,0x02,0x00,0x00,
-0x56,0x02,0x00,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x56,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0x56,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x60,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
-0xd0,0x02,0x00,0x00,0x42,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x5f,0x02,0x00,0x00,0x60,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x63,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
-0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x67,0x02,0x00,0x00,
-0xd0,0x02,0x00,0x00,0x43,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x68,0x02,0x00,0x00,0x4d,0x02,0x00,0x00,
-0x67,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6a,0x02,0x00,0x00,0x46,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,
-0x68,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0xed,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x70,0x02,0x00,0x00,0x53,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x72,0x02,0x00,0x00,
-0x4a,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x73,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
-0x72,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0x20,0x00,0x00,0x00,
-0x5e,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,
-0xd2,0x02,0x00,0x00,0xc8,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x77,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x7b,0x02,0x00,0x00,0x76,0x02,0x00,0x00,
-0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00,
-0x73,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xae,0x00,0x05,0x00,
-0x6b,0x00,0x00,0x00,0x82,0x02,0x00,0x00,0x7f,0x02,0x00,0x00,
-0xcc,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x84,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x82,0x02,0x00,0x00,
-0x83,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x97,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x90,0x00,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,
-0x89,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8b,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
-0x20,0x00,0x00,0x00,0x84,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0x6b,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0x44,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x8d,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x91,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8c,0x02,0x00,0x00,0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
-0x97,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x13,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9c,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x51,0x00,0x05,0x00,0x8b,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x89,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
-0x13,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,
-0x9f,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xa3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xac,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
-0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb1,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xb1,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xd8,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xd5,0x00,0x00,0x00,
-0xb3,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,
-0xb5,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x85,0x01,0x00,0x00,0xb6,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
-0x15,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb6,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8b,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
-0xd2,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x60,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbf,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_id_q8_0_f32_fp32_len = 10940;
-
-unsigned char matmul_q2_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x4e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
-0x8b,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x80,0x02,0x00,0x00,
-0xc9,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0xa1,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x8f,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
-0xa9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0xac,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
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-0x9c,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xbc,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xba,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbf,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
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-0xbb,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xc5,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc3,0x02,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
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-0xcd,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
-0xd1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xda,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
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-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,
-0xdf,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xe1,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0xe3,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0xa1,0x01,0x00,0x00,0xe4,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xe4,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe6,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x91,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x91,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,
-0xf4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xec,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q2_k_f32_len = 11220;
-
-unsigned char matmul_q2_k_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x67,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
-0x8d,0x01,0x00,0x00,0x95,0x01,0x00,0x00,0xa1,0x02,0x00,0x00,
-0xea,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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-0xc7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x15,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q2_k_f32_aligned_len = 11800;
-
-unsigned char matmul_q2_k_f32_aligned_fp32_data[] = {
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-0xe0,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xa4,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
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-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0xb3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xee,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xb8,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
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-0xba,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
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-0xbb,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xcc,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0x94,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xce,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,
-0xee,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd9,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
-0xca,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0xda,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x95,0x01,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xc1,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xce,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xdc,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
-0xee,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x9e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe2,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q2_k_f32_aligned_fp32_len = 11068;
-
-unsigned char matmul_q2_k_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x02,0x00,0x00,
-0x05,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb9,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xbb,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xb1,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x62,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
-0x6d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd3,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x67,0x02,0x00,0x00,0x56,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x68,0x02,0x00,0x00,0x96,0x00,0x00,0x00,0x67,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,
-0x5a,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6e,0x02,0x00,0x00,0xa7,0x00,0x00,0x00,
-0x6d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x72,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x71,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x73,0x02,0x00,0x00,
-0x72,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x74,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0x73,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x78,0x02,0x00,0x00,
-0x48,0x00,0x00,0x00,0x73,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7d,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xec,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0xe5,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x85,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
-0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x81,0x02,0x00,0x00,
-0x82,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x85,0x02,0x00,0x00,0x80,0x02,0x00,0x00,0x81,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x87,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x8d,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x89,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8d,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x02,0x00,0x00,0xed,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,
-0x68,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x94,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x95,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x94,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0x08,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0x6e,0x02,0x00,0x00,
-0x99,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9c,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
-0xa2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xa1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa5,0x02,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xf1,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xad,0x02,0x00,0x00,
-0xf1,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xa9,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xad,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xb5,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb3,0x02,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb8,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
-0xb3,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
-0xb4,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xbe,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xbc,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
-0xc6,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,
-0xca,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0xec,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd3,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
-0xd3,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xda,0x02,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xda,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0xdc,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x9f,0x01,0x00,0x00,0xdd,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xdd,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xaa,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0xf1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,
-0xed,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x81,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q2_k_f32_fp32_len = 11100;
-
-unsigned char matmul_q3_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xa7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x3a,0x01,0x00,0x00,0xaa,0x01,0x00,0x00,0x0b,0x02,0x00,0x00,
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-0x63,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x45,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x45,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x31,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x31,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x66,0x03,0x00,0x00,
-0x74,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x2e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x30,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x29,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x29,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x68,0x03,0x00,0x00,0x72,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x26,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x28,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x11,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x11,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6a,0x03,0x00,0x00,0x70,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0e,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x10,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x09,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6c,0x03,0x00,0x00,
-0x6f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x06,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q3_k_f32_len = 13124;
-
-unsigned char matmul_q3_k_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xc5,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
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-0x29,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q3_k_f32_aligned_len = 13704;
-
-unsigned char matmul_q3_k_f32_aligned_fp32_data[] = {
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-0x3d,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x3d,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x29,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x29,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5e,0x03,0x00,0x00,0x6c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x26,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x28,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x21,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x21,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x60,0x03,0x00,0x00,0x6a,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1e,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x20,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x09,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x62,0x03,0x00,0x00,
-0x68,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x06,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x01,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x64,0x03,0x00,0x00,0x67,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x00,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q3_k_f32_aligned_fp32_len = 12972;
-
-unsigned char matmul_q3_k_f32_fp32_data[] = {
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-0xf9,0x00,0x02,0x00,0x22,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x22,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x61,0x03,0x00,0x00,0x6b,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x21,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x63,0x03,0x00,0x00,0x69,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x07,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x03,0x00,0x00,
-0x68,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xff,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q3_k_f32_fp32_len = 13004;
-
-unsigned char matmul_q4_0_f32_data[] = {
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-0xf9,0x00,0x02,0x00,0x94,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x60,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_0_f32_len = 10564;
-
-unsigned char matmul_q4_0_f32_aligned_data[] = {
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
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-0xf8,0x00,0x02,0x00,0xb7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
-0xe8,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xda,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x98,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9a,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x83,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0xe3,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x78,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_0_f32_aligned_len = 11156;
-
-unsigned char matmul_q4_0_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x0c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
-0x28,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x62,0x01,0x00,0x00,
-0x48,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
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-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa9,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xca,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc4,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x64,0x01,0x00,0x00,0xac,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_0_f32_aligned_fp32_len = 10408;
-
-unsigned char matmul_q4_0_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x14,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
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-0x48,0x02,0x00,0x00,0x91,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0xf9,0x00,0x02,0x00,0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6e,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x57,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x74,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x70,0x02,0x00,0x00,
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-0x7f,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x82,0x02,0x00,0x00,
-0x7f,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x84,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x82,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x83,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x87,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x82,0x02,0x00,0x00,0x77,0x02,0x00,0x00,
-0x8a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x8d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x8b,0x02,0x00,0x00,0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x95,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,
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-0x99,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0x98,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x4c,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa9,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,
-0xc9,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xaa,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x6e,0x01,0x00,0x00,0xac,0x02,0x00,0x00,
-0x91,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x71,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb2,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x58,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x51,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_0_f32_fp32_len = 10444;
-
-unsigned char matmul_q4_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x5f,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x54,0x02,0x00,0x00,
-0x9d,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x5c,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q4_1_f32_len = 10632;
-
-unsigned char matmul_q4_1_f32_aligned_data[] = {
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-0xa8,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,
-0x93,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
-0xe8,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
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-0xbc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x88,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe1,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x87,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x80,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_1_f32_aligned_len = 11224;
-
-unsigned char matmul_q4_1_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xee,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
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-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
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-0x2d,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x67,0x01,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x04,0x01,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
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-0xb9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q4_1_f32_aligned_fp32_len = 10476;
-
-unsigned char matmul_q4_1_f32_fp32_data[] = {
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-0xc1,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x84,0x02,0x00,0x00,
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-0x88,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x71,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
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-0x8c,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
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-0x88,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0x92,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x51,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa1,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,0x69,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xac,0x02,0x00,0x00,0xa9,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xae,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x73,0x01,0x00,0x00,0xb1,0x02,0x00,0x00,0x96,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb1,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x92,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb3,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xbf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x56,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x55,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q4_1_f32_fp32_len = 10512;
-
-unsigned char matmul_q4_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x8d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,
-0x21,0x01,0x00,0x00,0x78,0x01,0x00,0x00,0xc6,0x01,0x00,0x00,
-0xd1,0x01,0x00,0x00,0xbb,0x02,0x00,0x00,0x04,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x23,0x03,0x00,0x00,
-0x2f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x25,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_k_f32_len = 12052;
-
-unsigned char matmul_q4_k_f32_aligned_data[] = {
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-0x41,0x03,0x00,0x00,0x40,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x22,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x0e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0e,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x43,0x03,0x00,0x00,0x53,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0d,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x06,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x45,0x03,0x00,0x00,0x51,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x03,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xee,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x03,0x00,0x00,
-0x4f,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xeb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xed,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x49,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe5,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q4_k_f32_aligned_len = 12648;
-
-unsigned char matmul_q4_k_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x7d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x78,0x01,0x00,0x00,
-0xc6,0x01,0x00,0x00,0xcd,0x01,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xfc,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x0f,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x0e,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x11,0x03,0x00,0x00,0x25,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x11,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x14,0x03,0x00,0x00,0x12,0x03,0x00,0x00,
-0x29,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,
-0x15,0x03,0x00,0x00,0xca,0x00,0x00,0x00,0x14,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x16,0x03,0x00,0x00,
-0x15,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0xcf,0x01,0x00,0x00,
-0x17,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0x09,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x17,0x03,0x00,0x00,
-0x16,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xf8,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
-0x29,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1b,0x03,0x00,0x00,0x27,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0x25,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x03,0x00,0x00,
-0x24,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_k_f32_aligned_fp32_len = 11900;
-
-unsigned char matmul_q4_k_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x86,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
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-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x78,0x01,0x00,0x00,
-0xc4,0x01,0x00,0x00,0xcf,0x01,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xfd,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x01,0x00,0x00,
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-0xd4,0x02,0x00,0x00,0x25,0x03,0x00,0x00,0x43,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,
-0xa9,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xda,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xda,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x28,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,
-0x1c,0x03,0x00,0x00,0xdd,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,0x28,0x03,0x00,0x00,
-0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xdc,0x02,0x00,0x00,
-0xdd,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe0,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x2a,0x03,0x00,0x00,
-0x3f,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,0x1a,0x03,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0xe8,0x02,0x00,0x00,0x2a,0x03,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xe4,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe8,0x02,0x00,0x00,
-0xe3,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xeb,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0x2a,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xee,0x02,0x00,0x00,
-0xeb,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xf0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xee,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,
-0x28,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xf4,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,
-0xf4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf0,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
-0xf7,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xf9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xf7,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0xf9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf8,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x01,0x03,0x00,0x00,0xd8,0x02,0x00,0x00,
-0x28,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x03,0x03,0x00,0x00,0x14,0x00,0x00,0x00,0x02,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,
-0x03,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x05,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0x04,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
-0xb8,0x02,0x00,0x00,0x05,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x08,0x03,0x00,0x00,0x06,0x03,0x00,0x00,
-0xd0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0a,0x03,0x00,0x00,0x08,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x25,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x28,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x10,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x12,0x03,0x00,0x00,
-0x26,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x13,0x03,0x00,0x00,0x10,0x03,0x00,0x00,
-0x12,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x15,0x03,0x00,0x00,0x13,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x16,0x03,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x15,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0x17,0x03,0x00,0x00,0x16,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0xda,0x01,0x00,0x00,0x18,0x03,0x00,0x00,
-0xfd,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x18,0x03,0x00,0x00,0x17,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdd,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x28,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xda,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdc,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x03,0x00,0x00,0x26,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x20,0x03,0x00,0x00,0x25,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xba,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q4_k_f32_fp32_len = 11932;
-
-unsigned char matmul_q5_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x46,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x50,0x01,0x00,0x00,
-0x83,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x78,0x02,0x00,0x00,
-0xc1,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
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-0xb3,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb3,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb7,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
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-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xba,0x02,0x00,0x00,
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-0xd2,0x02,0x00,0x00,0xd0,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xde,0x02,0x00,0x00,0xf0,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,0xee,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x81,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe4,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x80,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_0_f32_len = 11076;
-
-unsigned char matmul_q5_0_f32_aligned_data[] = {
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-0x9f,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xef,0x02,0x00,0x00,0xed,0x02,0x00,0x00,
-0xb7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf1,0x02,0x00,0x00,0xef,0x02,0x00,0x00,0x13,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,
-0x0e,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x01,0x03,0x00,0x00,0x13,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x03,0x00,0x00,
-0x11,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xac,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x05,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xab,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q5_0_f32_aligned_len = 11668;
-
-unsigned char matmul_q5_0_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x37,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
-0x51,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,
-0x71,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x97,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,
-0x9a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0x9d,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xbc,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x99,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x9d,0x02,0x00,0x00,
-0x98,0x02,0x00,0x00,0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x98,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xe9,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x98,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xe9,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xa1,0x02,0x00,0x00,0xa2,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xa5,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xa1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa0,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xad,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xab,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xac,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb0,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb6,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,
-0xb5,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbe,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
-0xbe,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x75,0x02,0x00,0x00,
-0xc2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcd,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
-0xd2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x8d,0x01,0x00,0x00,0xd5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd5,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_0_f32_aligned_fp32_len = 10920;
-
-unsigned char matmul_q5_0_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x09,0x01,0x00,0x00,
-0x50,0x01,0x00,0x00,0x81,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,
-0x71,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbb,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x06,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x07,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x07,0x01,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x09,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x09,0x01,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x5b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0xb0,0x02,0x00,0x00,0x95,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xac,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb6,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,
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-0x14,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xc3,0x02,0x00,0x00,0x75,0x02,0x00,0x00,
-0xc2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc5,0x02,0x00,0x00,0xc3,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,
-0xc5,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd2,0x02,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xd2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0xd4,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x97,0x01,0x00,0x00,0xd5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd5,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x97,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x99,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_0_f32_fp32_len = 10956;
-
-unsigned char matmul_q5_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x16,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x7e,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x73,0x02,0x00,0x00,
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-0xa3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
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-0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_1_f32_len = 10956;
-
-unsigned char matmul_q5_1_f32_aligned_data[] = {
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-0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0x0c,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xd6,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xd7,0x02,0x00,0x00,
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-0xd8,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,0xd7,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
-0xb2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xec,0x02,0x00,0x00,0xea,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0xdb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbf,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,
-0x0c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbe,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x00,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa6,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00,0x09,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q5_1_f32_aligned_len = 11548;
-
-unsigned char matmul_q5_1_f32_aligned_fp32_data[] = {
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-0x0f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
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-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x88,0x01,0x00,0x00,0xd0,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd0,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd2,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x95,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x75,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x74,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_1_f32_aligned_fp32_len = 10800;
-
-unsigned char matmul_q5_1_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x0f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
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-0x4b,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
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-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x11,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xaf,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xaf,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x53,0x02,0x00,0x00,
-0xf8,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xac,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xae,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xa4,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
-0x6d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd3,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,0x56,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5b,0x02,0x00,0x00,0x96,0x00,0x00,0x00,0x5a,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x02,0x00,0x00,
-0x5a,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,0xa7,0x00,0x00,0x00,
-0x60,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x65,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x64,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x67,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0x66,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,
-0x48,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x6d,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x6e,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x6b,0x02,0x00,0x00,
-0x6e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x70,0x02,0x00,0x00,0x67,0x02,0x00,0x00,0x6f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x72,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0x75,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x74,0x02,0x00,0x00,
-0x75,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x78,0x02,0x00,0x00,0x73,0x02,0x00,0x00,0x74,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x73,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x73,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
-0x7d,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x80,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0x61,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x7c,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x80,0x02,0x00,0x00,
-0x7b,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7b,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x84,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,
-0x5b,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0x87,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x02,0x00,0x00,
-0xdf,0x02,0x00,0x00,0xfb,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8d,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8f,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0x8d,0x02,0x00,0x00,0x8f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x92,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x7b,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x98,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x94,0x02,0x00,0x00,0x95,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x98,0x02,0x00,0x00,
-0x93,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x93,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x93,0x02,0x00,0x00,0xd2,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xe4,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x9c,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xa0,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,
-0x88,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xa8,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa6,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xab,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xae,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xad,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,
-0xa6,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,0xae,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xb1,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xaf,0x02,0x00,0x00,
-0xb0,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x90,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0x70,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc0,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x88,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,
-0xc0,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc6,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
-0xc6,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0xe0,0x02,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xc8,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xcb,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xcd,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0xcf,0x02,0x00,0x00,0xce,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0x92,0x01,0x00,0x00,0xd0,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xc2,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xd0,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xb1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd2,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x95,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x95,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x92,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x94,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x75,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd8,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x74,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_1_f32_fp32_len = 10836;
-
-unsigned char matmul_q5_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xe9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4c,0x03,0x00,0x00,0x51,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
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-0xe8,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_k_f32_len = 12576;
-
-unsigned char matmul_q5_k_f32_aligned_data[] = {
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-0xf9,0x00,0x02,0x00,0x2b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6a,0x03,0x00,0x00,0x76,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x28,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x13,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x13,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6c,0x03,0x00,0x00,0x74,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x10,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x12,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x03,0x00,0x00,
-0x73,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q5_k_f32_aligned_len = 13172;
-
-unsigned char matmul_q5_k_f32_aligned_fp32_data[] = {
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-0x1b,0x03,0x00,0x00,0x1c,0x03,0x00,0x00,0x1d,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1c,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
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-0x06,0x00,0x00,0x00,0x2c,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,
-0xf4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2e,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x30,0x03,0x00,0x00,
-0x49,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x32,0x03,0x00,0x00,0x30,0x03,0x00,0x00,
-0x4c,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x39,0x03,0x00,0x00,0x37,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcd,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,
-0xca,0x00,0x00,0x00,0x39,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
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-0x41,0x00,0x06,0x00,0xf4,0x01,0x00,0x00,0x3c,0x03,0x00,0x00,
-0x21,0x03,0x00,0x00,0x35,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x3c,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1d,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1d,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x09,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x3e,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x03,0x00,0x00,
-0x4c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe9,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x42,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x44,0x03,0x00,0x00,0x49,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xde,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe0,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q5_k_f32_aligned_fp32_len = 12424;
-
-unsigned char matmul_q5_k_f32_fp32_data[] = {
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-0xab,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
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-0xe9,0x01,0x00,0x00,0xf4,0x01,0x00,0x00,0xd9,0x02,0x00,0x00,
-0x22,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,
-0x48,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
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-0x4a,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0x45,0x03,0x00,0x00,0xe2,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,0x4a,0x03,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,
-0x4a,0x03,0x00,0x00,0x68,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
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-0xfa,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xff,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xff,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x4d,0x03,0x00,0x00,
-0x3f,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,0x41,0x03,0x00,0x00,
-0x02,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x05,0x03,0x00,0x00,0x4d,0x03,0x00,0x00,0xbb,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x01,0x03,0x00,0x00,0x02,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x05,0x03,0x00,0x00,
-0x00,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
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-0x00,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,
-0x4f,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x09,0x03,0x00,0x00,0x0a,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0d,0x03,0x00,0x00,0x08,0x03,0x00,0x00,
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-0xf5,0x02,0x00,0x00,0x4f,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x13,0x03,0x00,0x00,0x10,0x03,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x15,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x13,0x03,0x00,0x00,
-0x14,0x03,0x00,0x00,0x15,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x14,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x18,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,0x4d,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x19,0x03,0x00,0x00,
-0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,0x19,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,
-0x18,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x15,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x15,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,
-0x13,0x03,0x00,0x00,0x08,0x03,0x00,0x00,0x1b,0x03,0x00,0x00,
-0x14,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x1e,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x1c,0x03,0x00,0x00,
-0x1d,0x03,0x00,0x00,0x1e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1d,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x26,0x03,0x00,0x00,0xfd,0x02,0x00,0x00,0x4d,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x28,0x03,0x00,0x00,
-0x14,0x00,0x00,0x00,0x27,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x29,0x03,0x00,0x00,0x28,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2a,0x03,0x00,0x00,
-0x26,0x03,0x00,0x00,0x29,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x03,0x00,0x00,0xdd,0x02,0x00,0x00,
-0x2a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2d,0x03,0x00,0x00,0x2b,0x03,0x00,0x00,0xf5,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,
-0x2d,0x03,0x00,0x00,0x4f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x31,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x33,0x03,0x00,0x00,0x31,0x03,0x00,0x00,0x4d,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x35,0x03,0x00,0x00,
-0x33,0x03,0x00,0x00,0x34,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x37,0x03,0x00,0x00,0x4b,0x03,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x38,0x03,0x00,0x00,0x35,0x03,0x00,0x00,0x37,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3a,0x03,0x00,0x00,
-0x38,0x03,0x00,0x00,0x4f,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x3a,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,
-0x3c,0x03,0x00,0x00,0x3b,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0xff,0x01,0x00,0x00,0x3d,0x03,0x00,0x00,0x22,0x03,0x00,0x00,
-0x35,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x3d,0x03,0x00,0x00,0x3c,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x1e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x1e,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3f,0x03,0x00,0x00,0x4f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x07,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x09,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x02,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x41,0x03,0x00,0x00,0x4d,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xff,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xea,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xea,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x43,0x03,0x00,0x00,
-0x4b,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x45,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xdf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe1,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q5_k_f32_fp32_len = 12456;
-
-unsigned char matmul_q6_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x43,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x07,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,
-0x18,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x03,0x00,0x00,0x16,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xca,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0x14,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00,
-0x13,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q6_k_f32_len = 11744;
-
-unsigned char matmul_q6_k_f32_aligned_data[] = {
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-0x29,0x03,0x00,0x00,0x28,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x03,0x00,0x00,0x39,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xee,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2d,0x03,0x00,0x00,0x37,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xeb,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xed,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,
-0x35,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xce,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xce,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x31,0x03,0x00,0x00,0x34,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcd,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q6_k_f32_aligned_len = 12324;
-
-unsigned char matmul_q6_k_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x45,0x01,0x00,0x00,
-0xaf,0x01,0x00,0x00,0xb6,0x01,0x00,0x00,0x9c,0x02,0x00,0x00,
-0xe5,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x83,0x02,0x00,0x00,
-0x24,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdc,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xde,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xd4,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xd7,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd7,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x85,0x02,0x00,0x00,0x0a,0x03,0x00,0x00,
-0x6d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd4,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x56,0x00,0x00,0x00,
-0x54,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x97,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x02,0x00,0x00,
-0x5a,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,0xa8,0x00,0x00,0x00,
-0x90,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x95,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x94,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x95,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x97,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,0x96,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x48,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9c,0x02,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x9e,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x02,0x00,0x00,0x97,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa2,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x08,0x03,0x00,0x00,0xa5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0x0b,0x03,0x00,0x00,
-0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xa4,0x02,0x00,0x00,
-0xa5,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xa8,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xaa,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x3f,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x06,0x03,0x00,0x00,
-0xad,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xb0,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xac,0x02,0x00,0x00,0xad,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb0,0x02,0x00,0x00,
-0xab,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xab,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb4,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,0x62,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,0x65,0x00,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x2b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0x91,0x02,0x00,0x00,
-0xbc,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbf,0x02,0x00,0x00,0x69,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x3f,0x00,0x00,0x00,0xab,0x02,0x00,0x00,0x04,0x03,0x00,0x00,
-0xc5,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,
-0xc8,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,0xbc,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xc4,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc8,0x02,0x00,0x00,
-0xc3,0x02,0x00,0x00,0xc4,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xca,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xca,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00,0x3f,0x00,0x00,0x00,
-0xc3,0x02,0x00,0x00,0x02,0x03,0x00,0x00,0xcd,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,
-0x10,0x03,0x00,0x00,0x63,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xcc,0x02,0x00,0x00,0xcd,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xd0,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
-0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0x10,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc2,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,
-0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xd8,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd6,0x02,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xd8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xdd,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xd8,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0xc2,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,0xde,0x02,0x00,0x00,
-0xd7,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xe1,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xdf,0x02,0x00,0x00,
-0xe0,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe0,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe9,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xeb,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0xeb,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x02,0x00,0x00,
-0xe9,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xee,0x02,0x00,0x00,0xa0,0x02,0x00,0x00,
-0xed,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf0,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,
-0xf0,0x02,0x00,0x00,0x10,0x03,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,0x0b,0x03,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,0x0e,0x03,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,0x0c,0x03,0x00,0x00,
-0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfb,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,0xfa,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfd,0x02,0x00,0x00,
-0xfb,0x02,0x00,0x00,0x10,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcd,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xca,0x00,0x00,0x00,
-0xfd,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,
-0xff,0x02,0x00,0x00,0xfe,0x02,0x00,0x00,0x41,0x00,0x06,0x00,
-0xb8,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0xe5,0x02,0x00,0x00,
-0x35,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x00,0x03,0x00,0x00,0xff,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x02,0x03,0x00,0x00,0x10,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xca,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x0e,0x03,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
-0x0c,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x08,0x03,0x00,0x00,0x0b,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa4,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q6_k_f32_aligned_fp32_len = 11592;
-
-unsigned char matmul_q6_k_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,
-0xf9,0x02,0x00,0x00,0xfb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,0xfc,0x02,0x00,0x00,
-0x11,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
-0xff,0x02,0x00,0x00,0xc9,0x00,0x00,0x00,0xfe,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x00,0x03,0x00,0x00,
-0xff,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0xc3,0x01,0x00,0x00,
-0x01,0x03,0x00,0x00,0xe6,0x02,0x00,0x00,0x35,0x00,0x00,0x00,
-0xf3,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x01,0x03,0x00,0x00,
-0x00,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xce,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xce,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x03,0x03,0x00,0x00,
-0x11,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xcd,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x05,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x0d,0x03,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xab,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa6,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x03,0x00,0x00,
-0x0c,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q6_k_f32_fp32_len = 11624;
-
-unsigned char matmul_q8_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf1,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,
-0xc4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x61,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xba,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x60,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q8_0_f32_len = 10588;
-
-unsigned char matmul_q8_0_f32_aligned_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0x06,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0xc5,0x02,0x00,0x00,
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-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
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-0x3e,0x00,0x03,0x00,0xd7,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa4,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0xe9,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x9c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9c,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdb,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x99,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x9b,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x83,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q8_0_f32_aligned_len = 11200;
-
-unsigned char matmul_q8_0_f32_aligned_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xea,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x29,0x01,0x00,0x00,0x5c,0x01,0x00,0x00,0x63,0x01,0x00,0x00,
-0x49,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x89,0x02,0x00,0x00,
-0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,0x89,0x02,0x00,0x00,
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-0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa3,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x02,0x00,0x00,
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-0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaf,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x51,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t matmul_q8_0_f32_aligned_fp32_len = 10452;
-
-unsigned char matmul_q8_0_f32_fp32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xea,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
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-0x49,0x02,0x00,0x00,0x92,0x02,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0xfc,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfe,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xf7,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf7,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x2c,0x02,0x00,0x00,0xdd,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf6,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xef,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xef,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2e,0x02,0x00,0x00,
-0xd9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xec,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xee,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8c,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x30,0x02,0x00,0x00,0xd3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x89,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8b,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x81,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd6,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00,
-0xb9,0x02,0x00,0x00,0x6d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd5,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00,
-0x56,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x38,0x02,0x00,0x00,0x96,0x00,0x00,0x00,
-0x37,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3d,0x02,0x00,0x00,0x5a,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3e,0x02,0x00,0x00,
-0xa7,0x00,0x00,0x00,0x3d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x42,0x02,0x00,0x00,0x14,0x00,0x00,0x00,
-0x41,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x43,0x02,0x00,0x00,0x42,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x44,0x02,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x43,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x48,0x02,0x00,0x00,0x48,0x00,0x00,0x00,0x43,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,
-0x49,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,
-0x48,0x02,0x00,0x00,0x4b,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4d,0x02,0x00,0x00,0x44,0x02,0x00,0x00,
-0x4c,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,
-0xd5,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0x52,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0xbe,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x51,0x02,0x00,0x00,0x52,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x55,0x02,0x00,0x00,0x50,0x02,0x00,0x00,
-0x51,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x57,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x50,0x02,0x00,0x00,
-0xb3,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x59,0x02,0x00,0x00,
-0x5a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x5d,0x02,0x00,0x00,0x58,0x02,0x00,0x00,0x59,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,
-0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x62,0x02,0x00,0x00,0x38,0x02,0x00,0x00,0x61,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,
-0x65,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,0x62,0x02,0x00,0x00,
-0x64,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x69,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xd8,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6a,0x02,0x00,0x00,
-0x3e,0x02,0x00,0x00,0x69,0x02,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6c,0x02,0x00,0x00,0x69,0x00,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6d,0x02,0x00,0x00,0x6a,0x02,0x00,0x00,0x6c,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x6f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x58,0x02,0x00,0x00,
-0xb1,0x02,0x00,0x00,0x72,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x75,0x02,0x00,0x00,0xbd,0x02,0x00,0x00,
-0xbb,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x71,0x02,0x00,0x00,
-0x72,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x75,0x02,0x00,0x00,0x70,0x02,0x00,0x00,0x71,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x77,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
-0x3f,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0xaf,0x02,0x00,0x00,
-0x7a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x7d,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x79,0x02,0x00,0x00,0x7a,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x7d,0x02,0x00,0x00,
-0x78,0x02,0x00,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x78,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x80,0x02,0x00,0x00,0x65,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x83,0x02,0x00,0x00,
-0x80,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x85,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x83,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0x85,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x84,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x88,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x89,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x8a,0x02,0x00,0x00,
-0x89,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x88,0x02,0x00,0x00,0x8a,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x85,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x85,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,
-0x8c,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x78,0x02,0x00,0x00,
-0x8b,0x02,0x00,0x00,0x84,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x8e,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x8c,0x02,0x00,0x00,0x8d,0x02,0x00,0x00,0x8e,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8d,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x6d,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x98,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x97,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x99,0x02,0x00,0x00,
-0x98,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9a,0x02,0x00,0x00,0x96,0x02,0x00,0x00,0x99,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x4d,0x02,0x00,0x00,0x9a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x9b,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9f,0x02,0x00,0x00,0x9d,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00,
-0xba,0x02,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa1,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa5,0x02,0x00,0x00,0xa3,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0xa5,0x02,0x00,0x00,
-0xa7,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaa,0x02,0x00,0x00,0xa8,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0xab,0x02,0x00,0x00,
-0xc9,0x00,0x00,0x00,0xaa,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0xab,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0x6f,0x01,0x00,0x00,0xad,0x02,0x00,0x00,
-0x92,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xad,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x77,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x72,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x72,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x6f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x5a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x5a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xb3,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x52,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t matmul_q8_0_f32_fp32_len = 10468;
-
-unsigned char mul_f32_data[] = {
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-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x51,0x02,0x00,0x00,
-0x50,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x52,0x02,0x00,0x00,0xdd,0x01,0x00,0x00,0x51,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x53,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0x53,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x02,0x00,0x00,
-0x52,0x02,0x00,0x00,0x54,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x56,0x02,0x00,0x00,0x4e,0x02,0x00,0x00,
-0x55,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x58,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x59,0x02,0x00,0x00,
-0x58,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5a,0x02,0x00,0x00,0xec,0x01,0x00,0x00,0x59,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x5b,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x02,0x00,0x00,
-0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x56,0x02,0x00,0x00,
-0x5d,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x60,0x02,0x00,0x00,0x17,0x00,0x00,0x00,0xca,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x02,0x00,0x00,
-0x60,0x02,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x62,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x61,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x63,0x02,0x00,0x00,
-0x17,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x63,0x02,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00,
-0x62,0x02,0x00,0x00,0x64,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x66,0x02,0x00,0x00,0x5e,0x02,0x00,0x00,
-0x65,0x02,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
-0x5b,0x01,0x00,0x00,0x56,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x66,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x85,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x50,0x01,0x00,0x00,
-0x5e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0x47,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00,
-0x5d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x62,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_f32_len = 4276;
-
-unsigned char mul_mat_vec_f16_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x48,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0e,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0xbf,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x38,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x38,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x38,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x38,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x69,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x76,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x83,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbc,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbd,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xbd,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbf,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbf,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfc,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xfd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xfd,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xff,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xff,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x07,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x08,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x16,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x17,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x18,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x31,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x0d,0x00,
-0x38,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x1b,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x3c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
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-0x5a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0x69,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x70,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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-0xac,0x00,0x05,0x00,0x95,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0x47,0x01,0x00,0x00,0x6a,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xe1,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe5,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0xe1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x95,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0x78,0x00,0x00,0x00,0x47,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xea,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x47,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x89,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8a,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xea,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xea,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x70,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe2,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x47,0x01,0x00,0x00,0xf5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xdf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe1,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x95,0x00,0x00,0x00,
-0xf9,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xfb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
-0x33,0x01,0x00,0x00,0x74,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x89,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x86,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x05,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0xff,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x06,0x01,0x00,0x00,0x04,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_f16_f16_f32_len = 3716;
-
-unsigned char mul_mat_vec_f16_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x46,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0e,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0xbf,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x38,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x38,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x38,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x38,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x38,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x69,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x76,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0xbc,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbd,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xbd,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbf,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbf,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfb,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xfc,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xfc,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfe,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfe,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x05,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x16,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x17,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x18,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x31,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x0d,0x00,
-0x38,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0xdf,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x95,0x00,0x00,0x00,
-0xe7,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x45,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xe9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe7,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x78,0x00,0x00,0x00,0x45,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x89,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x86,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x8a,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x70,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
-0xdb,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x45,0x01,0x00,0x00,
-0xf4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xde,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x95,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x6a,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xfa,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,
-0xf9,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x74,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x89,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
-0x86,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x02,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xc5,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfa,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_f16_f32_f32_len = 3684;
-
-unsigned char mul_mat_vec_f32_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x44,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0e,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x6a,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x35,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x35,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x35,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x35,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x35,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x66,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x80,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xba,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbb,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xbb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbd,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfb,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xfc,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xfc,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfe,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfe,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x05,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x16,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x17,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x0d,0x00,
-0x35,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
-0x37,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x39,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x57,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x2e,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x81,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x3b,0x00,0x04,0x00,0x82,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x85,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x86,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x92,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
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-0xe9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x86,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x83,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x87,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x87,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0xdb,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x43,0x01,0x00,0x00,
-0xf4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xde,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x92,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xfa,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,
-0xf9,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x71,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x86,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
-0x83,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x02,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x1f,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfa,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_f32_f16_f32_len = 3684;
-
-unsigned char mul_mat_vec_f32_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x40,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x83,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x17,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x35,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x35,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x35,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x35,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x66,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x73,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x80,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xba,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xba,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbc,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xf8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xf8,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x01,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x25,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x30,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x1e,0x00,0x0d,0x00,0x35,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x36,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x39,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x61,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x2e,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
-0x6a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x80,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x81,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x82,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x82,0x00,0x00,0x00,
-0x83,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x86,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x92,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0xb9,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0xba,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xba,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0xf9,0x00,0x02,0x00,0xe5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe5,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,
-0x6d,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdd,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x3f,0x01,0x00,0x00,0xf0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xda,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdc,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x92,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xf6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xf4,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x71,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x86,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x83,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x1f,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0xfa,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x00,0x01,0x00,0x00,
-0xff,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf6,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf6,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_f32_f32_f32_len = 3616;
-
-unsigned char mul_mat_vec_id_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x22,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x43,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x18,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x38,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x38,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x41,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x41,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x41,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x41,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x41,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x41,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x41,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x41,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x41,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x41,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x58,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x5c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xab,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xac,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xac,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xac,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xae,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xae,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xea,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0xeb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xeb,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0xeb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xed,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xed,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xf4,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf5,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x08,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x16,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x17,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x18,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x30,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x31,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x37,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x38,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x39,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x39,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
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-0x09,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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-0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x45,0x00,0x00,0x00,
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-0xe9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe8,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0x0d,0x01,0x00,0x00,0x63,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x78,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xb4,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_id_f16_f32_len = 3608;
-
-unsigned char mul_mat_vec_id_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x1c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x40,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2f,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x34,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x35,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x35,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x37,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3e,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x3e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x3e,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3e,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x3e,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x3e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x3e,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x3e,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x3e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x55,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x59,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xa9,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xa9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xa9,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xab,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xab,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xe6,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe7,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xe7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe7,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe9,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe9,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf0,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf1,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x25,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x2d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x30,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x34,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x35,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x36,0x00,0x00,0x00,
-0x37,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x39,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1e,0x00,0x0b,0x00,0x3e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x3f,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x3f,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x42,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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-0x02,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
-0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x70,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x71,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x70,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x75,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
-0x81,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xa8,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xa9,0x00,0x00,0x00,
-0xa8,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xaa,0x00,0x00,0x00,
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-0xe8,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0xe8,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00,
-0x2d,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
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-0x1f,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
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-0xf6,0x00,0x04,0x00,0xcb,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xca,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x81,0x00,0x00,0x00,
-0xd2,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xd4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xd2,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd3,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x75,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0xd8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0xdc,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x76,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd4,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x5c,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcc,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
-0xdf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc9,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcb,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x81,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x64,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xe5,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe3,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x60,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x75,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x1f,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe5,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_id_f32_f32_len = 3540;
-
-unsigned char mul_mat_vec_id_q2_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x1b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0xb4,0x02,0x00,0x00,0x26,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb6,0x02,0x00,0x00,0xb5,0x02,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0xb7,0x02,0x00,0x00,
-0xb6,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xb8,0x02,0x00,0x00,0xb7,0x02,0x00,0x00,0xda,0x00,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x80,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,
-0xb8,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x80,0x00,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xad,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0xa4,0x02,0x00,0x00,
-0x81,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,
-0x17,0x03,0x00,0x00,0xbb,0x02,0x00,0x00,0x80,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x15,0x03,0x00,0x00,
-0xe1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xbb,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbd,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x80,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,0xb6,0x00,0x00,0x00,
-0x17,0x03,0x00,0x00,0x7f,0x00,0x04,0x00,0x80,0x00,0x00,0x00,
-0x1a,0x03,0x00,0x00,0xc9,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x80,0x00,0x00,0x00,0xca,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x16,0x03,0x00,0x00,
-0x1a,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x80,0x00,0x00,0x00,
-0xcc,0x02,0x00,0x00,0x8b,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x80,0x00,0x00,0x00,0xcd,0x02,0x00,0x00,0xcc,0x02,0x00,0x00,
-0xca,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x8b,0x00,0x00,0x00,
-0xcd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x91,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x91,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0x12,0x03,0x00,0x00,
-0x41,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x90,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd3,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x13,0x03,0x00,0x00,0x84,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0xea,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xac,0x00,0x05,0x00,
-0x95,0x00,0x00,0x00,0xd9,0x02,0x00,0x00,0x13,0x03,0x00,0x00,
-0x3b,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xd5,0x02,0x00,0x00,
-0xd6,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd9,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd4,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x95,0x00,0x00,0x00,0xdc,0x02,0x00,0x00,0x60,0x00,0x00,0x00,
-0x13,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0xde,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xdc,0x02,0x00,0x00,
-0xdd,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xdd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe2,0x02,0x00,0x00,0x60,0x00,0x00,0x00,0x13,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x8a,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,
-0x83,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x80,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0xe3,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0x8a,0x00,0x00,0x00,0xe5,0x02,0x00,0x00,
-0x83,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x80,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
-0x81,0x00,0x05,0x00,0x80,0x00,0x00,0x00,0xe7,0x02,0x00,0x00,
-0xe6,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xe5,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xde,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xde,0x02,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0xd1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd6,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0x13,0x03,0x00,0x00,
-0xe1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xd3,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd5,0x02,0x00,0x00,0xaa,0x00,0x05,0x00,
-0x95,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0x60,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xee,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xec,0x02,0x00,0x00,
-0xed,0x02,0x00,0x00,0xee,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xed,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf5,0x02,0x00,0x00,0x11,0x03,0x00,0x00,0x45,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x8a,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
-0x83,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x80,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,0xf6,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0xcd,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,
-0xf2,0x02,0x00,0x00,0x1c,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf8,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xee,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xee,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_id_q2_k_f32_len = 8412;
-
-unsigned char mul_mat_vec_id_q3_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x63,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x8b,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0x3e,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xab,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xaf,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xaf,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xaf,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xaf,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xb1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xb1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xb1,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb3,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb3,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc5,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xc6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xc6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xc6,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc8,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc8,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3b,0x03,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x3c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x3c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x3c,0x03,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3e,0x03,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x03,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x45,0x03,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
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-0xb6,0x02,0x00,0x00,0x21,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x6e,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,
-0x94,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xba,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xbb,0x02,0x00,0x00,0xba,0x02,0x00,0x00,
-0xab,0x00,0x05,0x00,0xa0,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,
-0xbb,0x02,0x00,0x00,0x1c,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,
-0x17,0x00,0x00,0x00,0xbd,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x82,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0xac,0x02,0x00,0x00,
-0xbd,0x02,0x00,0x00,0x6f,0x00,0x04,0x00,0x88,0x00,0x00,0x00,
-0xbf,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x88,0x00,0x00,0x00,0xc1,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,
-0x77,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc9,0x02,0x00,0x00,0xce,0x00,0x00,0x00,0xc8,0x02,0x00,0x00,
-0x41,0x00,0x06,0x00,0xd0,0x00,0x00,0x00,0xca,0x02,0x00,0x00,
-0xc8,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0xc9,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x88,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,
-0xca,0x02,0x00,0x00,0x41,0x00,0x08,0x00,0xd6,0x00,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xb3,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,
-0xd0,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,0x6e,0x00,0x00,0x00,
-0xd3,0x02,0x00,0x00,0xd1,0x02,0x00,0x00,0x74,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,
-0xd3,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0xd5,0x02,0x00,0x00,0xd4,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xd6,0x02,0x00,0x00,0xd5,0x02,0x00,0x00,
-0xdd,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x46,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x6e,0x00,0x00,0x00,0xde,0x02,0x00,0x00,0xdb,0x02,0x00,0x00,
-0x71,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xe0,0x02,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,
-0xe0,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xe2,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,
-0x26,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xe3,0x02,0x00,0x00,0xd6,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,
-0x72,0x00,0x04,0x00,0x76,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,
-0xe3,0x02,0x00,0x00,0x72,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0xe5,0x02,0x00,0x00,0xe4,0x02,0x00,0x00,0x82,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0xe6,0x02,0x00,0x00,0xe5,0x02,0x00,0x00,
-0xee,0x00,0x00,0x00,0x6f,0x00,0x04,0x00,0x88,0x00,0x00,0x00,
-0xe7,0x02,0x00,0x00,0xe6,0x02,0x00,0x00,0x85,0x00,0x05,0x00,
-0x88,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,0xcb,0x02,0x00,0x00,
-0xe7,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0x14,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x6e,0x00,0x00,0x00,0xf3,0x02,0x00,0x00,0xf2,0x02,0x00,0x00,
-0xab,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xf4,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0xf4,0x02,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xf6,0x02,0x00,0x00,
-0xf5,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x6e,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x21,0x02,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x6e,0x00,0x00,0x00,0x03,0x03,0x00,0x00,
-0x00,0x03,0x00,0x00,0xdc,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x03,0x03,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x05,0x03,0x00,0x00,
-0x04,0x03,0x00,0x00,0xab,0x00,0x05,0x00,0xa0,0x00,0x00,0x00,
-0x06,0x03,0x00,0x00,0x05,0x03,0x00,0x00,0x1c,0x00,0x00,0x00,
-0xa9,0x00,0x06,0x00,0x17,0x00,0x00,0x00,0x07,0x03,0x00,0x00,
-0x06,0x03,0x00,0x00,0x1c,0x00,0x00,0x00,0x26,0x00,0x00,0x00,
-0x82,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
-0xf6,0x02,0x00,0x00,0x07,0x03,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x88,0x00,0x00,0x00,0x09,0x03,0x00,0x00,0x08,0x03,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x88,0x00,0x00,0x00,0x0b,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,
-0x09,0x03,0x00,0x00,0xc1,0x02,0x00,0x00,0x81,0x00,0x05,0x00,
-0x88,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,0x62,0x03,0x00,0x00,
-0x0b,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x0f,0x03,0x00,0x00,0x61,0x03,0x00,0x00,0x71,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbe,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x88,0x00,0x00,0x00,
-0x18,0x03,0x00,0x00,0x93,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x88,0x00,0x00,0x00,0x19,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x62,0x03,0x00,0x00,
-0x18,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x93,0x00,0x00,0x00,
-0x19,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x9c,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x5e,0x03,0x00,0x00,
-0x41,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x99,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9b,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1f,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x5f,0x03,0x00,0x00,0x8c,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
-0x36,0x03,0x00,0x00,0x22,0x03,0x00,0x00,0xac,0x00,0x05,0x00,
-0xa0,0x00,0x00,0x00,0x25,0x03,0x00,0x00,0x5f,0x03,0x00,0x00,
-0x3b,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x21,0x03,0x00,0x00,
-0x22,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x25,0x03,0x00,0x00,0x20,0x03,0x00,0x00,0x21,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x20,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xa0,0x00,0x00,0x00,0x28,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
-0x5f,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0x2a,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x28,0x03,0x00,0x00,
-0x29,0x03,0x00,0x00,0x2a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x29,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2e,0x03,0x00,0x00,0x60,0x00,0x00,0x00,0x5f,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x92,0x00,0x00,0x00,0x2f,0x03,0x00,0x00,
-0x8b,0x00,0x00,0x00,0x2e,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x88,0x00,0x00,0x00,0x30,0x03,0x00,0x00,0x2f,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x92,0x00,0x00,0x00,0x31,0x03,0x00,0x00,
-0x8b,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x88,0x00,0x00,0x00,0x32,0x03,0x00,0x00,0x31,0x03,0x00,0x00,
-0x81,0x00,0x05,0x00,0x88,0x00,0x00,0x00,0x33,0x03,0x00,0x00,
-0x32,0x03,0x00,0x00,0x30,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x31,0x03,0x00,0x00,0x33,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x2a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0x1d,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x22,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x22,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x36,0x03,0x00,0x00,0x5f,0x03,0x00,0x00,
-0x71,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1f,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x21,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xa0,0x00,0x00,0x00,0x38,0x03,0x00,0x00,0x60,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x3a,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x38,0x03,0x00,0x00,
-0x39,0x03,0x00,0x00,0x3a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x39,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x41,0x03,0x00,0x00,0x5d,0x03,0x00,0x00,0x45,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x92,0x00,0x00,0x00,0x42,0x03,0x00,0x00,
-0x8b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x88,0x00,0x00,0x00,0x43,0x03,0x00,0x00,0x42,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0xd0,0x00,0x00,0x00,0x44,0x03,0x00,0x00,
-0x3e,0x03,0x00,0x00,0x1c,0x00,0x00,0x00,0x41,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x44,0x03,0x00,0x00,0x43,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x3a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3a,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_id_q3_k_f32_len = 10020;
-
-unsigned char mul_mat_vec_id_q4_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x44,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0xe5,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe8,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe3,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x99,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
-0x41,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0xed,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xeb,0x00,0x00,0x00,
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-0xf1,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x41,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x8d,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
-0x8e,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xf6,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x8e,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xed,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xed,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x74,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe5,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,
-0x41,0x01,0x00,0x00,0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe4,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x99,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x7c,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xfd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xfb,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x21,0x01,0x00,0x00,
-0x78,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x8d,0x00,0x00,0x00,
-0x05,0x01,0x00,0x00,0x8a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x06,0x01,0x00,0x00,
-0x05,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0xc9,0x00,0x00,0x00,
-0x07,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x04,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x07,0x01,0x00,0x00,
-0x06,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_id_q4_0_f32_len = 4000;
-
-unsigned char mul_mat_vec_id_q4_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0xfc,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x95,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xec,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xec,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x50,0x01,0x00,0x00,
-0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe9,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xa0,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x83,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x04,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x02,0x01,0x00,0x00,
-0x03,0x01,0x00,0x00,0x04,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x03,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0b,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0x7f,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x94,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,
-0x91,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xd0,0x00,0x00,0x00,0x0e,0x01,0x00,0x00,
-0x08,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x0e,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x04,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x04,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_id_q4_1_f32_len = 4068;
-
-unsigned char mul_mat_vec_id_q4_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xce,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x25,0x02,0x00,0x00,
-0xa9,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xac,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xae,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xae,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xae,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xb0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xb0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xb0,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb2,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x02,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x23,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x23,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x25,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x25,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x03,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xa7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xa7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xa7,0x03,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xa9,0x03,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xa9,0x03,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x03,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x0f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
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-0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
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-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
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-0x2b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
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-0x70,0x00,0x04,0x00,0x86,0x00,0x00,0x00,0x70,0x03,0x00,0x00,
-0xe0,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x86,0x00,0x00,0x00,
-0x71,0x03,0x00,0x00,0x77,0x02,0x00,0x00,0x70,0x03,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x86,0x00,0x00,0x00,0x72,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x4c,0x02,0x00,0x00,
-0x6c,0x03,0x00,0x00,0x71,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
-0x86,0x00,0x00,0x00,0x75,0x03,0x00,0x00,0x1a,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x86,0x00,0x00,0x00,0x77,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,
-0x75,0x03,0x00,0x00,0x72,0x03,0x00,0x00,0x70,0x00,0x04,0x00,
-0x86,0x00,0x00,0x00,0x7a,0x03,0x00,0x00,0x36,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x86,0x00,0x00,0x00,0x7c,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xc6,0x02,0x00,0x00,
-0x7a,0x03,0x00,0x00,0x77,0x03,0x00,0x00,0x85,0x00,0x05,0x00,
-0x86,0x00,0x00,0x00,0x80,0x03,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x64,0x03,0x00,0x00,0x7f,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
-0xcd,0x03,0x00,0x00,0x80,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x86,0x00,0x00,0x00,0x81,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x7c,0x03,0x00,0x00,
-0xcd,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
-0x83,0x03,0x00,0x00,0x91,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x86,0x00,0x00,0x00,0x84,0x03,0x00,0x00,0x83,0x03,0x00,0x00,
-0x81,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x91,0x00,0x00,0x00,
-0x84,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x87,0x03,0x00,0x00,0xc9,0x03,0x00,0x00,0x41,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x94,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x96,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x41,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x88,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x8a,0x03,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xca,0x03,0x00,0x00,
-0x8a,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0xa1,0x03,0x00,0x00,
-0x8d,0x03,0x00,0x00,0xac,0x00,0x05,0x00,0x9b,0x00,0x00,0x00,
-0x90,0x03,0x00,0x00,0xca,0x03,0x00,0x00,0x3b,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x8c,0x03,0x00,0x00,0x8d,0x03,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x90,0x03,0x00,0x00,
-0x8b,0x03,0x00,0x00,0x8c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8b,0x03,0x00,0x00,0xb0,0x00,0x05,0x00,0x9b,0x00,0x00,0x00,
-0x93,0x03,0x00,0x00,0x60,0x00,0x00,0x00,0xca,0x03,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x95,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x93,0x03,0x00,0x00,0x94,0x03,0x00,0x00,
-0x95,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x94,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x03,0x00,0x00,
-0x60,0x00,0x00,0x00,0xca,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x90,0x00,0x00,0x00,0x9a,0x03,0x00,0x00,0x89,0x00,0x00,0x00,
-0x99,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
-0x9b,0x03,0x00,0x00,0x9a,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x90,0x00,0x00,0x00,0x9c,0x03,0x00,0x00,0x89,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
-0x9d,0x03,0x00,0x00,0x9c,0x03,0x00,0x00,0x81,0x00,0x05,0x00,
-0x86,0x00,0x00,0x00,0x9e,0x03,0x00,0x00,0x9d,0x03,0x00,0x00,
-0x9b,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x9c,0x03,0x00,0x00,
-0x9e,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x95,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x95,0x03,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x88,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8d,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8d,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa1,0x03,0x00,0x00,0xca,0x03,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x8a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8c,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,0x9b,0x00,0x00,0x00,
-0xa3,0x03,0x00,0x00,0x60,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xa5,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xa3,0x03,0x00,0x00,0xa4,0x03,0x00,0x00,
-0xa5,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xa4,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xac,0x03,0x00,0x00,
-0xc8,0x03,0x00,0x00,0x45,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x90,0x00,0x00,0x00,0xad,0x03,0x00,0x00,0x89,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
-0xae,0x03,0x00,0x00,0xad,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0x29,0x02,0x00,0x00,0xaf,0x03,0x00,0x00,0xa9,0x03,0x00,0x00,
-0x1c,0x00,0x00,0x00,0xac,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xaf,0x03,0x00,0x00,0xae,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa5,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xa5,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_id_q4_k_f32_len = 9944;
-
-unsigned char mul_mat_vec_id_q5_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x98,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x80,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
-0x9f,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1f,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x20,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x23,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x7e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x7e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x80,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x80,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x87,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x87,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x87,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x87,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x87,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x9c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb4,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xed,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xee,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xee,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xee,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf0,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xf0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2b,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x2c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x2c,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2e,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2e,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x35,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x36,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
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-0xca,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xcd,0x00,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
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-0xda,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
-0xcd,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x43,0x01,0x00,0x00,
-0xd5,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x57,0x01,0x00,0x00,0xe3,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x41,0x00,0x07,0x00,0x29,0x00,0x00,0x00,0x58,0x01,0x00,0x00,
-0x23,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x57,0x01,0x00,0x00,
-0x25,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x59,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x73,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0x59,0x01,0x00,0x00,
-0x41,0x00,0x08,0x00,0x32,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,
-0x23,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x57,0x01,0x00,0x00,
-0x31,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x60,0x01,0x00,0x00,
-0x5f,0x01,0x00,0x00,0xc4,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x61,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x36,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x32,0x00,0x00,0x00,0x65,0x01,0x00,0x00,
-0x23,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x57,0x01,0x00,0x00,
-0x31,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x65,0x01,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x67,0x01,0x00,0x00,
-0x66,0x01,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x68,0x01,0x00,0x00,0x61,0x01,0x00,0x00,0x67,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6b,0x01,0x00,0x00,
-0x68,0x01,0x00,0x00,0xda,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6c,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,
-0x45,0x00,0x00,0x00,0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6d,0x01,0x00,0x00,0x6c,0x01,0x00,0x00,0x1d,0x00,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,
-0x6d,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x71,0x01,0x00,0x00,0xda,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x72,0x01,0x00,0x00,
-0x68,0x01,0x00,0x00,0x71,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x73,0x01,0x00,0x00,0x72,0x01,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x24,0x00,0x00,0x00,
-0x74,0x01,0x00,0x00,0x73,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
-0x57,0x00,0x00,0x00,0x7a,0x01,0x00,0x00,0x23,0x00,0x00,0x00,
-0x25,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0x55,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x7b,0x01,0x00,0x00,0x7a,0x01,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,
-0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,
-0x7c,0x01,0x00,0x00,0x5c,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x81,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x82,0x01,0x00,0x00,
-0x7e,0x01,0x00,0x00,0x81,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x82,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x85,0x01,0x00,0x00,
-0x7c,0x01,0x00,0x00,0x45,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x88,0x01,0x00,0x00,0x74,0x01,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x89,0x01,0x00,0x00,
-0x85,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x70,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,0x89,0x01,0x00,0x00,
-0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,
-0x83,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,0x83,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,
-0x97,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x8f,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0x8f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf5,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xf6,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
-0xf5,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xf6,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0xf0,0x00,0x00,0x00,
-0x25,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x02,0x01,0x00,0x00,
-0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x08,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x04,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x07,0x01,0x00,0x00,0xbb,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x07,0x01,0x00,0x00,
-0x05,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xbb,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x0b,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x1a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbf,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0f,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0f,0x01,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
-0x0e,0x01,0x00,0x00,0xbf,0x00,0x00,0x00,0x26,0x01,0x00,0x00,
-0x12,0x01,0x00,0x00,0xac,0x00,0x05,0x00,0xc6,0x00,0x00,0x00,
-0x15,0x01,0x00,0x00,0x95,0x01,0x00,0x00,0x5e,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x11,0x01,0x00,0x00,0x12,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x15,0x01,0x00,0x00,
-0x10,0x01,0x00,0x00,0x11,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x10,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0xc6,0x00,0x00,0x00,
-0x18,0x01,0x00,0x00,0xa9,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x1a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x18,0x01,0x00,0x00,0x19,0x01,0x00,0x00,
-0x1a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x19,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
-0xa9,0x00,0x00,0x00,0x95,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0xba,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0xb7,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x20,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0xbb,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0x23,0x01,0x00,0x00,
-0x22,0x01,0x00,0x00,0x20,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xbb,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x1a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x1a,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x0c,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x12,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x12,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x95,0x01,0x00,0x00,
-0x31,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0f,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x11,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xc6,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0xa9,0x00,0x00,0x00,
-0x5e,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x2a,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x28,0x01,0x00,0x00,
-0x29,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x29,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x31,0x01,0x00,0x00,0x4e,0x01,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0xba,0x00,0x00,0x00,0x32,0x01,0x00,0x00,
-0xb7,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xf6,0x00,0x00,0x00,0x34,0x01,0x00,0x00,
-0x2e,0x01,0x00,0x00,0x25,0x00,0x00,0x00,0x31,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x34,0x01,0x00,0x00,0x33,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x2a,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2a,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_id_q5_0_f32_len = 4512;
-
-unsigned char mul_mat_vec_id_q5_1_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x90,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x96,0x00,0x00,0x00,
-0x99,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0xf6,0x00,0x04,0x00,0x0c,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x10,0x01,0x00,0x00,
-0x0b,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0b,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,
-0x13,0x01,0x00,0x00,0xa4,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x15,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x13,0x01,0x00,0x00,0x14,0x01,0x00,0x00,
-0x15,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x14,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
-0xa4,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0xb5,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,0xb2,0x00,0x00,0x00,
-0x19,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x1b,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0xb6,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x1d,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb6,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x15,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x15,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x9c,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
-0x07,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0d,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,
-0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0c,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xc1,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0xa4,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x25,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x23,0x01,0x00,0x00,
-0x24,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x24,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2c,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0xb5,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x2e,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xf1,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,
-0x29,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x2f,0x01,0x00,0x00,0x2e,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x25,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x25,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_id_q5_1_f32_len = 4392;
-
-unsigned char mul_mat_vec_id_q5_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb9,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x96,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x2f,0x02,0x00,0x00,
-0x94,0x04,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb8,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xba,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xbb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbb,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xbb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xbb,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xbd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xbd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xbd,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbf,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbf,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2c,0x02,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x2d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x2d,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2f,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2f,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x91,0x04,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x92,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x92,0x04,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x92,0x04,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x94,0x04,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x94,0x04,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9b,0x04,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x0f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x1e,0x00,0x0b,0x00,0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x10,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x89,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x75,0x04,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x77,0x04,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xa8,0x00,0x00,0x00,0x8e,0x04,0x00,0x00,0x60,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x90,0x04,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x8e,0x04,0x00,0x00,
-0x8f,0x04,0x00,0x00,0x90,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x8f,0x04,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x97,0x04,0x00,0x00,0xb3,0x04,0x00,0x00,0x45,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0x98,0x04,0x00,0x00,
-0x96,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x93,0x00,0x00,0x00,0x99,0x04,0x00,0x00,0x98,0x04,0x00,0x00,
-0x41,0x00,0x06,0x00,0x33,0x02,0x00,0x00,0x9a,0x04,0x00,0x00,
-0x94,0x04,0x00,0x00,0x1c,0x00,0x00,0x00,0x97,0x04,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x9a,0x04,0x00,0x00,0x99,0x04,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x90,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x90,0x04,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_id_q5_k_f32_len = 12816;
-
-unsigned char mul_mat_vec_id_q6_k_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf8,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x8f,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xda,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x19,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x19,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x3a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x3e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5d,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xab,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xac,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xae,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xb0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb0,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xb0,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xb0,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb1,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xb2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xb2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xb2,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb4,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb4,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xc7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xc7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xc7,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc9,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd7,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xd8,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xd8,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xd8,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xda,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xda,0x01,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe1,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x0f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x19,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x26,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,
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-0x72,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0xe5,0x03,0x00,0x00,
-0xe4,0x03,0x00,0x00,0x82,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0xe6,0x03,0x00,0x00,0xe5,0x03,0x00,0x00,0x01,0x01,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0xe7,0x03,0x00,0x00,
-0xe6,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,0x8c,0x00,0x00,0x00,
-0xe9,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xcf,0x03,0x00,0x00,0xe7,0x03,0x00,0x00,0xc2,0x03,0x00,0x00,
-0x81,0x00,0x05,0x00,0x8c,0x00,0x00,0x00,0xea,0x03,0x00,0x00,
-0x46,0x03,0x00,0x00,0xe9,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x8c,0x00,0x00,0x00,0xb4,0x01,0x00,0x00,0x97,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x8c,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,
-0xb4,0x01,0x00,0x00,0xea,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x97,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xb8,0x01,0x00,0x00,0xfa,0x01,0x00,0x00,
-0x41,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9a,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9c,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x41,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0xb9,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xbb,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xbb,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0xfb,0x01,0x00,0x00,0x90,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
-0xd2,0x01,0x00,0x00,0xbe,0x01,0x00,0x00,0xac,0x00,0x05,0x00,
-0xa1,0x00,0x00,0x00,0xc1,0x01,0x00,0x00,0xfb,0x01,0x00,0x00,
-0x3b,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xbd,0x01,0x00,0x00,
-0xbe,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xc1,0x01,0x00,0x00,0xbc,0x01,0x00,0x00,0xbd,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbc,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xa1,0x00,0x00,0x00,0xc4,0x01,0x00,0x00,0x60,0x00,0x00,0x00,
-0xfb,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0xc6,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xc4,0x01,0x00,0x00,
-0xc5,0x01,0x00,0x00,0xc6,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xc5,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xca,0x01,0x00,0x00,0x60,0x00,0x00,0x00,0xfb,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x96,0x00,0x00,0x00,0xcb,0x01,0x00,0x00,
-0x8f,0x00,0x00,0x00,0xca,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x8c,0x00,0x00,0x00,0xcc,0x01,0x00,0x00,0xcb,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x96,0x00,0x00,0x00,0xcd,0x01,0x00,0x00,
-0x8f,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x8c,0x00,0x00,0x00,0xce,0x01,0x00,0x00,0xcd,0x01,0x00,0x00,
-0x81,0x00,0x05,0x00,0x8c,0x00,0x00,0x00,0xcf,0x01,0x00,0x00,
-0xce,0x01,0x00,0x00,0xcc,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xcd,0x01,0x00,0x00,0xcf,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc6,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xc6,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x41,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0xb9,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xbe,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbe,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd2,0x01,0x00,0x00,0xfb,0x01,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xbb,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbd,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xa1,0x00,0x00,0x00,0xd4,0x01,0x00,0x00,0x60,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xd6,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd4,0x01,0x00,0x00,
-0xd5,0x01,0x00,0x00,0xd6,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd5,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x01,0x00,0x00,0xf9,0x01,0x00,0x00,0x45,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x96,0x00,0x00,0x00,0xde,0x01,0x00,0x00,
-0x8f,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x8c,0x00,0x00,0x00,0xdf,0x01,0x00,0x00,0xde,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xd1,0x00,0x00,0x00,0xe0,0x01,0x00,0x00,
-0xda,0x01,0x00,0x00,0x1c,0x00,0x00,0x00,0xdd,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xe0,0x01,0x00,0x00,0xdf,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd6,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd6,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_id_q6_k_f32_len = 11760;
-
-unsigned char mul_mat_vec_id_q8_0_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x41,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x70,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
-0xc1,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x1e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x20,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x46,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x55,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x55,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x55,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x55,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x55,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x55,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x55,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x55,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x70,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x79,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x86,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbe,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xbf,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xbf,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xbf,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc1,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc1,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xfc,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xfd,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xfd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xfd,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xff,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xff,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x06,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1e,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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-0xab,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x41,0x00,0x08,0x00,
-0x2f,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x20,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x35,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
-0x37,0x01,0x00,0x00,0x36,0x01,0x00,0x00,0x72,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x37,0x01,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0x38,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x30,0x01,0x00,0x00,0x39,0x01,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x3a,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xc7,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0x3c,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xc7,0x00,0x00,0x00,
-0xd3,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xd2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0xd3,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,
-0xd4,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,
-0xd6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xbd,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x8d,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
-0x3f,0x01,0x00,0x00,0x73,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x91,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0xdd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0xdf,0x00,0x00,0x00,
-0x91,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0xac,0x00,0x05,0x00,0x98,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
-0x40,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xe2,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe6,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0xe2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x98,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xeb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xe9,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xea,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,
-0x40,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x8c,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x89,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xf0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xf3,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8d,0x00,0x00,0x00,
-0xf4,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xeb,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x73,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe3,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe2,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x98,0x00,0x00,0x00,
-0xf9,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xfb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf9,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xfb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfa,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x77,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x8c,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x89,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xc7,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0xff,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x05,0x01,0x00,0x00,0x04,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_id_q8_0_f32_len = 3956;
-
-unsigned char mul_mat_vec_nc_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0xab,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1b,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1b,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1b,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x6d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x6d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x6d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x6f,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6f,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x78,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x7a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa8,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xa9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xa9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xa9,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xab,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xab,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x1e,0x00,0x09,0x00,0x1b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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-0xad,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,
-0xaf,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xaf,0x00,0x00,0x00,
-0xae,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa7,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa7,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_nc_f16_f32_len = 2824;
-
-unsigned char mul_mat_vec_p021_f16_f32_data[] = {
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-0x3e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x3d,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x44,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x49,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3e,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x25,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x68,0x00,0x00,0x00,
-0x69,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x5f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x62,0x00,0x00,0x00,
-0x6a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x73,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x7b,0x00,0x00,0x00,
-0x7c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
-0x7d,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x32,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,
-0x7d,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x3a,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x3f,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0xb6,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x3e,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x1e,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,
-0x8e,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
-0x92,0x00,0x00,0x00,0xad,0x00,0x05,0x00,0x44,0x00,0x00,0x00,
-0x95,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x91,0x00,0x00,0x00,0x92,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x95,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x90,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x98,0x00,0x00,0x00,0xb7,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x44,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x98,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x9b,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x99,0x00,0x00,0x00,
-0x9a,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa0,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x39,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x32,0x00,0x00,0x00,
-0xa5,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x3a,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x9b,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x92,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x92,0x00,0x00,0x00,
-0xc3,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,
-0xb7,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x91,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0x44,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xac,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xaa,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xab,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x39,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x32,0x00,0x00,0x00,
-0xb3,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7b,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xac,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_p021_f16_f32_len = 2768;
-
-unsigned char mul_mat_vec_q2_k_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x50,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x94,0x00,0x00,0x00,
-0xb9,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x13,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb0,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb5,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xb5,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xb5,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb6,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xb7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xb7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xb7,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb9,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xd5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xd5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xd5,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd7,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x10,0x03,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x11,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x11,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x11,0x03,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x13,0x03,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x48,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,0xff,0x02,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xfd,0x02,0x00,0x00,
-0xfe,0x02,0x00,0x00,0xff,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfe,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x03,0x03,0x00,0x00,0x71,0x00,0x00,0x00,0x48,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x9b,0x00,0x00,0x00,0x04,0x03,0x00,0x00,
-0x94,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x91,0x00,0x00,0x00,0x05,0x03,0x00,0x00,0x04,0x03,0x00,0x00,
-0x41,0x00,0x05,0x00,0x9b,0x00,0x00,0x00,0x06,0x03,0x00,0x00,
-0x94,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x91,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x06,0x03,0x00,0x00,
-0x81,0x00,0x05,0x00,0x91,0x00,0x00,0x00,0x08,0x03,0x00,0x00,
-0x07,0x03,0x00,0x00,0x05,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x06,0x03,0x00,0x00,0x08,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xff,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xff,0x02,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0xf2,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf7,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0b,0x03,0x00,0x00,0x48,0x03,0x00,0x00,
-0xf3,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf6,0x02,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xa6,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,0x71,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x0f,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x0d,0x03,0x00,0x00,
-0x0e,0x03,0x00,0x00,0x0f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0e,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x16,0x03,0x00,0x00,0x46,0x03,0x00,0x00,0x55,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x9b,0x00,0x00,0x00,0x17,0x03,0x00,0x00,
-0x94,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x91,0x00,0x00,0x00,0x18,0x03,0x00,0x00,0x17,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0x19,0x03,0x00,0x00,0x1a,0x03,0x00,0x00,
-0x13,0x03,0x00,0x00,0x61,0x00,0x00,0x00,0x16,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1a,0x03,0x00,0x00,0x18,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x0f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0f,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q2_k_f16_f32_len = 8760;
-
-unsigned char mul_mat_vec_q2_k_f32_f32_data[] = {
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-0x40,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0xd5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0x91,0x00,0x00,0x00,0x91,0x02,0x00,0x00,0xa5,0x01,0x00,0x00,
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-0xad,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0xaf,0x00,0x00,0x00,
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-0xaf,0x02,0x00,0x00,0x3b,0x00,0x00,0x00,0x71,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xb1,0x02,0x00,0x00,0xb0,0x02,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0xb2,0x02,0x00,0x00,
-0xb1,0x02,0x00,0x00,0xc7,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0xb3,0x02,0x00,0x00,0xb2,0x02,0x00,0x00,0xec,0x00,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x91,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,
-0xb3,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x91,0x00,0x00,0x00,
-0xb6,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xa8,0x02,0x00,0x00,0xb4,0x02,0x00,0x00,0x9f,0x02,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x91,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,
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-0xc6,0x02,0x00,0x00,0xfd,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0xaf,0x00,0x00,0x00,0xc7,0x02,0x00,0x00,0xc6,0x02,0x00,0x00,
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-0xc8,0x02,0x00,0x00,0xc7,0x02,0x00,0x00,0x7c,0x00,0x04,0x00,
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-0x91,0x00,0x00,0x00,0xcb,0x02,0x00,0x00,0xca,0x02,0x00,0x00,
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-0xcb,0x02,0x00,0x00,0xb6,0x02,0x00,0x00,0x81,0x00,0x05,0x00,
-0x91,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0x3c,0x03,0x00,0x00,
-0xcd,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0xd1,0x02,0x00,0x00,0x3a,0x03,0x00,0x00,0xf3,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcf,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x91,0x00,0x00,0x00,
-0xdb,0x02,0x00,0x00,0xc8,0x00,0x00,0x00,0x3c,0x03,0x00,0x00,
-0x7f,0x00,0x04,0x00,0x91,0x00,0x00,0x00,0x3f,0x03,0x00,0x00,
-0xdb,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,0x91,0x00,0x00,0x00,
-0xdc,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xc1,0x00,0x00,0x00,0x3b,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x91,0x00,0x00,0x00,0xde,0x02,0x00,0x00,
-0x9c,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x91,0x00,0x00,0x00,
-0xdf,0x02,0x00,0x00,0xde,0x02,0x00,0x00,0xdc,0x02,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x9c,0x00,0x00,0x00,0xdf,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa2,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe2,0x02,0x00,0x00,0x37,0x03,0x00,0x00,0x51,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x9f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa1,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x51,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x38,0x03,0x00,0x00,
-0x95,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0xfc,0x02,0x00,0x00,
-0xe8,0x02,0x00,0x00,0xac,0x00,0x05,0x00,0xa6,0x00,0x00,0x00,
-0xeb,0x02,0x00,0x00,0x38,0x03,0x00,0x00,0x4b,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xe7,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xeb,0x02,0x00,0x00,
-0xe6,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe6,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xa6,0x00,0x00,0x00,
-0xee,0x02,0x00,0x00,0x71,0x00,0x00,0x00,0x38,0x03,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xf0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xee,0x02,0x00,0x00,0xef,0x02,0x00,0x00,
-0xf0,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xef,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf4,0x02,0x00,0x00,
-0x71,0x00,0x00,0x00,0x38,0x03,0x00,0x00,0x41,0x00,0x05,0x00,
-0x9b,0x00,0x00,0x00,0xf5,0x02,0x00,0x00,0x94,0x00,0x00,0x00,
-0xf4,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0x91,0x00,0x00,0x00,
-0xf6,0x02,0x00,0x00,0xf5,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0x9b,0x00,0x00,0x00,0xf7,0x02,0x00,0x00,0x94,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x91,0x00,0x00,0x00,
-0xf8,0x02,0x00,0x00,0xf7,0x02,0x00,0x00,0x81,0x00,0x05,0x00,
-0x91,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0xf8,0x02,0x00,0x00,
-0xf6,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xf7,0x02,0x00,0x00,
-0xf9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xf0,0x02,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf0,0x02,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x51,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe8,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe8,0x02,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfc,0x02,0x00,0x00,0x38,0x03,0x00,0x00,0xf3,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe7,0x02,0x00,0x00,0xaa,0x00,0x05,0x00,0xa6,0x00,0x00,0x00,
-0xfe,0x02,0x00,0x00,0x71,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xfe,0x02,0x00,0x00,0xff,0x02,0x00,0x00,
-0x00,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0xff,0x02,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,
-0x36,0x03,0x00,0x00,0x55,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x9b,0x00,0x00,0x00,0x08,0x03,0x00,0x00,0x94,0x00,0x00,0x00,
-0x61,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x91,0x00,0x00,0x00,
-0x09,0x03,0x00,0x00,0x08,0x03,0x00,0x00,0x41,0x00,0x06,0x00,
-0xdf,0x00,0x00,0x00,0x0a,0x03,0x00,0x00,0x04,0x03,0x00,0x00,
-0x61,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x0a,0x03,0x00,0x00,0x09,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x00,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x00,0x03,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q2_k_f32_f32_len = 8504;
-
-unsigned char mul_mat_vec_q3_k_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x8c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
-0xc4,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0x53,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
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-0x41,0x00,0x05,0x00,0xa3,0x00,0x00,0x00,0x57,0x03,0x00,0x00,
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-0x53,0x03,0x00,0x00,0x61,0x00,0x00,0x00,0x56,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x5a,0x03,0x00,0x00,0x58,0x03,0x00,0x00,
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-0x4f,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q3_k_f16_f32_len = 10176;
-
-unsigned char mul_mat_vec_q3_k_f32_f32_data[] = {
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-0x51,0x00,0x00,0x00,0x2b,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x30,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x30,0x03,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x44,0x03,0x00,0x00,
-0x80,0x03,0x00,0x00,0x82,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x2d,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x2f,0x03,0x00,0x00,
-0xaa,0x00,0x05,0x00,0xb1,0x00,0x00,0x00,0x46,0x03,0x00,0x00,
-0x71,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x48,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x46,0x03,0x00,0x00,0x47,0x03,0x00,0x00,0x48,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x47,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x4f,0x03,0x00,0x00,0x7e,0x03,0x00,0x00,
-0x55,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xa3,0x00,0x00,0x00,
-0x50,0x03,0x00,0x00,0x9c,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x99,0x00,0x00,0x00,0x51,0x03,0x00,0x00,
-0x50,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0xe3,0x00,0x00,0x00,
-0x52,0x03,0x00,0x00,0x4c,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
-0x4f,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0x52,0x03,0x00,0x00,
-0x51,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x48,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x48,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q3_k_f32_f32_len = 10048;
-
-unsigned char mul_mat_vec_q4_0_f16_f32_data[] = {
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-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
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-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x7e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x82,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
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-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x98,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xd1,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd2,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x66,0x01,0x00,0x00,
-0x7f,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0xb1,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0x86,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
-0x85,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,0xc7,0x00,0x00,0x00,
-0xba,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x26,0x00,0x00,0x00,
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-0x4e,0x01,0x00,0x00,0x2e,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
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-0x56,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x58,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
-0x35,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
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-0x06,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
-0x39,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x5e,0x01,0x00,0x00,0x5d,0x01,0x00,0x00,0x50,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,
-0x5e,0x01,0x00,0x00,0x83,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x61,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,0x69,0x01,0x00,0x00,
-0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x63,0x01,0x00,0x00,
-0x61,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x63,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x26,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
-0xdb,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x63,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x26,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0xe7,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xdc,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x9f,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x9f,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0x66,0x01,0x00,0x00,
-0x85,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa1,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa3,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x85,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf4,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x67,0x01,0x00,0x00,0xf3,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,
-0x0b,0x01,0x00,0x00,0xf7,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
-0xaa,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x67,0x01,0x00,0x00,
-0x7f,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xf6,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xfa,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xaa,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
-0x67,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0xff,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xfd,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfe,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x03,0x01,0x00,0x00,0x8d,0x00,0x00,0x00,0x67,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x9e,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0x9b,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x04,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x07,0x01,0x00,0x00,
-0x9f,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0x07,0x01,0x00,0x00,0x05,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x9f,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xff,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xff,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x85,0x00,0x00,0x00,
-0x85,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf7,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x67,0x01,0x00,0x00,0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf6,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0xaa,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x7f,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x0f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x0d,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0e,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x47,0x01,0x00,0x00,
-0x89,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x9e,0x00,0x00,0x00,
-0x17,0x01,0x00,0x00,0x9b,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x18,0x01,0x00,0x00,
-0x17,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x19,0x01,0x00,0x00,
-0x1a,0x01,0x00,0x00,0x13,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x16,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x1a,0x01,0x00,0x00,
-0x18,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x0f,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x0f,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q4_0_f16_f32_len = 4108;
-
-unsigned char mul_mat_vec_q4_0_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x68,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x47,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x8b,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0xd4,0x00,0x00,0x00,
-0x12,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x20,0x00,0x00,0x00,
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-0x0e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0e,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q4_0_f32_f32_len = 4076;
-
-unsigned char mul_mat_vec_q4_1_f16_f32_data[] = {
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-0x13,0x01,0x00,0x00,0xff,0x00,0x00,0x00,0xac,0x00,0x05,0x00,
-0xb2,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x77,0x01,0x00,0x00,
-0x87,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xfe,0x00,0x00,0x00,
-0xff,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x02,0x01,0x00,0x00,0xfd,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb2,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x95,0x00,0x00,0x00,
-0x77,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0x07,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x05,0x01,0x00,0x00,
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-0xa3,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,
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-0x10,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xa7,0x00,0x00,0x00,0x10,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x07,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x07,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x8d,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xff,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xff,0x00,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,
-0x77,0x01,0x00,0x00,0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfe,0x00,0x00,0x00,
-0xaa,0x00,0x05,0x00,0xb2,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
-0x95,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x17,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x15,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x17,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x16,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0x4f,0x01,0x00,0x00,
-0x91,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xa6,0x00,0x00,0x00,
-0x1f,0x01,0x00,0x00,0xa3,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x21,0x01,0x00,0x00,
-0x22,0x01,0x00,0x00,0x1b,0x01,0x00,0x00,0x22,0x00,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x22,0x01,0x00,0x00,
-0x20,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x17,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x17,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q4_1_f16_f32_len = 4192;
-
-unsigned char mul_mat_vec_q4_1_f32_f32_data[] = {
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-0x76,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
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-0x09,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,0x6c,0x01,0x00,0x00,
-0x58,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x70,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,
-0x81,0x00,0x05,0x00,0x09,0x00,0x00,0x00,0x71,0x01,0x00,0x00,
-0x6e,0x01,0x00,0x00,0x70,0x01,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xd8,0x00,0x00,0x00,0x71,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x49,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xe2,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x71,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0xe2,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0xdc,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,
-0xf1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xd8,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0xa7,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xf4,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xa7,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0x74,0x01,0x00,0x00,0x8d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa9,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xab,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x8d,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,
-0xf8,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x75,0x01,0x00,0x00,0xfa,0x00,0x00,0x00,
-0xab,0x00,0x00,0x00,0x12,0x01,0x00,0x00,0xfe,0x00,0x00,0x00,
-0xac,0x00,0x05,0x00,0xb2,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
-0x75,0x01,0x00,0x00,0x87,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xfd,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x01,0x01,0x00,0x00,0xfc,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xb2,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0x95,0x00,0x00,0x00,0x75,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x06,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x04,0x01,0x00,0x00,0x05,0x01,0x00,0x00,0x06,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x95,0x00,0x00,0x00,
-0x75,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0xa6,0x00,0x00,0x00,
-0x0b,0x01,0x00,0x00,0xa3,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,
-0x0b,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x0e,0x01,0x00,0x00,0xa7,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0x0f,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,
-0x0c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xa7,0x00,0x00,0x00,
-0x0f,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x06,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x8d,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfe,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfe,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x12,0x01,0x00,0x00,0x75,0x01,0x00,0x00,0x2e,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfd,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0xb2,0x00,0x00,0x00,
-0x14,0x01,0x00,0x00,0x95,0x00,0x00,0x00,0x87,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x16,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x14,0x01,0x00,0x00,0x15,0x01,0x00,0x00,
-0x16,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x15,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
-0x4d,0x01,0x00,0x00,0x91,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0xa6,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0xa3,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x1f,0x01,0x00,0x00,0x1e,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xe2,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x20,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x16,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x16,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q4_1_f32_f32_len = 4160;
-
-unsigned char mul_mat_vec_q4_k_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x13,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0x37,0x02,0x00,0x00,0xda,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x18,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbd,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbe,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbf,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xbf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x06,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xc1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xc1,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc3,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x34,0x02,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x35,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x35,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x35,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x37,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x37,0x02,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd7,0x03,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xd8,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xd8,0x03,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0xda,0x03,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xda,0x03,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe2,0x03,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x0f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,
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-0xe0,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0xb9,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xbe,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbe,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd2,0x03,0x00,0x00,0x0f,0x04,0x00,0x00,
-0xd7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xbb,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xbd,0x03,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xac,0x00,0x00,0x00,0xd4,0x03,0x00,0x00,0x71,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xd6,0x03,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd4,0x03,0x00,0x00,
-0xd5,0x03,0x00,0x00,0xd6,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd5,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdd,0x03,0x00,0x00,0x0d,0x04,0x00,0x00,0x55,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,0xde,0x03,0x00,0x00,
-0x9a,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x97,0x00,0x00,0x00,0xdf,0x03,0x00,0x00,0xde,0x03,0x00,0x00,
-0x41,0x00,0x06,0x00,0xe0,0x03,0x00,0x00,0xe1,0x03,0x00,0x00,
-0xda,0x03,0x00,0x00,0x61,0x00,0x00,0x00,0xdd,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xe1,0x03,0x00,0x00,0xdf,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd6,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd6,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q4_k_f16_f32_len = 10548;
-
-unsigned char mul_mat_vec_q4_k_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xf3,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
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-0x4e,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0x37,0x02,0x00,0x00,0xbb,0x03,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xbd,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xbe,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x35,0x02,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
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-0x72,0x03,0x00,0x00,0xd3,0x02,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x97,0x00,0x00,0x00,0x76,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x72,0x03,0x00,0x00,0xfc,0x02,0x00,0x00,
-0x6c,0x03,0x00,0x00,0x70,0x00,0x04,0x00,0x97,0x00,0x00,0x00,
-0x7e,0x03,0x00,0x00,0xe3,0x00,0x00,0x00,0x70,0x00,0x04,0x00,
-0x97,0x00,0x00,0x00,0x82,0x03,0x00,0x00,0xf1,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x97,0x00,0x00,0x00,0x83,0x03,0x00,0x00,
-0x89,0x02,0x00,0x00,0x82,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x97,0x00,0x00,0x00,0x84,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x5e,0x02,0x00,0x00,0x7e,0x03,0x00,0x00,
-0x83,0x03,0x00,0x00,0x70,0x00,0x04,0x00,0x97,0x00,0x00,0x00,
-0x87,0x03,0x00,0x00,0x2c,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x97,0x00,0x00,0x00,0x89,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xb0,0x02,0x00,0x00,0x87,0x03,0x00,0x00,
-0x84,0x03,0x00,0x00,0x70,0x00,0x04,0x00,0x97,0x00,0x00,0x00,
-0x8c,0x03,0x00,0x00,0x48,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x97,0x00,0x00,0x00,0x8e,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,0x8c,0x03,0x00,0x00,
-0x89,0x03,0x00,0x00,0x85,0x00,0x05,0x00,0x97,0x00,0x00,0x00,
-0x92,0x03,0x00,0x00,0xd1,0x00,0x00,0x00,0x76,0x03,0x00,0x00,
-0x7f,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0xf2,0x03,0x00,0x00,
-0x92,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,0x97,0x00,0x00,0x00,
-0x93,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0x8e,0x03,0x00,0x00,0xf2,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0x95,0x03,0x00,0x00,
-0xa2,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x97,0x00,0x00,0x00,
-0x96,0x03,0x00,0x00,0x95,0x03,0x00,0x00,0x93,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xa2,0x00,0x00,0x00,0x96,0x03,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x99,0x03,0x00,0x00,
-0xee,0x03,0x00,0x00,0x51,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa7,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0x9a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x9c,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x9c,0x03,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0xef,0x03,0x00,0x00,0x9b,0x00,0x00,0x00,
-0xa7,0x00,0x00,0x00,0xb3,0x03,0x00,0x00,0x9f,0x03,0x00,0x00,
-0xac,0x00,0x05,0x00,0xac,0x00,0x00,0x00,0xa2,0x03,0x00,0x00,
-0xef,0x03,0x00,0x00,0x4b,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x9e,0x03,0x00,0x00,0x9f,0x03,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xa2,0x03,0x00,0x00,0x9d,0x03,0x00,0x00,
-0x9e,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x9d,0x03,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xac,0x00,0x00,0x00,0xa5,0x03,0x00,0x00,
-0x71,0x00,0x00,0x00,0xef,0x03,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xa7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xa5,0x03,0x00,0x00,0xa6,0x03,0x00,0x00,0xa7,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xa6,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xab,0x03,0x00,0x00,0x71,0x00,0x00,0x00,
-0xef,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,
-0xac,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0xab,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0xad,0x03,0x00,0x00,
-0xac,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,
-0xae,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0x71,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0xaf,0x03,0x00,0x00,
-0xae,0x03,0x00,0x00,0x81,0x00,0x05,0x00,0x97,0x00,0x00,0x00,
-0xb0,0x03,0x00,0x00,0xaf,0x03,0x00,0x00,0xad,0x03,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xae,0x03,0x00,0x00,0xb0,0x03,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa7,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa7,0x03,0x00,0x00,0xe0,0x00,0x04,0x00,0x51,0x00,0x00,0x00,
-0x51,0x00,0x00,0x00,0x9a,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x9f,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x9f,0x03,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb3,0x03,0x00,0x00,
-0xef,0x03,0x00,0x00,0xd7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x9c,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x9e,0x03,0x00,0x00,
-0xaa,0x00,0x05,0x00,0xac,0x00,0x00,0x00,0xb5,0x03,0x00,0x00,
-0x71,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xb7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xb5,0x03,0x00,0x00,0xb6,0x03,0x00,0x00,0xb7,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb6,0x03,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xbe,0x03,0x00,0x00,0xed,0x03,0x00,0x00,
-0x55,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xa1,0x00,0x00,0x00,
-0xbf,0x03,0x00,0x00,0x9a,0x00,0x00,0x00,0x61,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0xc0,0x03,0x00,0x00,
-0xbf,0x03,0x00,0x00,0x41,0x00,0x06,0x00,0x3b,0x02,0x00,0x00,
-0xc1,0x03,0x00,0x00,0xbb,0x03,0x00,0x00,0x61,0x00,0x00,0x00,
-0xbe,0x03,0x00,0x00,0x3e,0x00,0x03,0x00,0xc1,0x03,0x00,0x00,
-0xc0,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0xb7,0x03,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb7,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q4_k_f32_f32_len = 10036;
-
-unsigned char mul_mat_vec_q5_0_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xbf,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x78,0x00,0x00,0x00,
-0x80,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,
-0xb9,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x02,0x01,0x00,0x00,
-0x41,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x1e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1f,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x20,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x23,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x23,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7e,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x7e,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x7e,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x7e,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x7e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x7e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
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-0x7e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xb1,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xff,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x00,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x00,0x01,0x00,0x00,
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-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x02,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x47,0x00,0x04,0x00,0x49,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4a,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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-0x5e,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x24,0x01,0x00,0x00,
-0x25,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x28,0x01,0x00,0x00,0x23,0x01,0x00,0x00,0x24,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x23,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xd8,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0xbb,0x00,0x00,0x00,
-0xbc,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0x2d,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x2b,0x01,0x00,0x00,
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-0x31,0x01,0x00,0x00,0xbb,0x00,0x00,0x00,0xbc,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,0x32,0x01,0x00,0x00,
-0xc9,0x00,0x00,0x00,0x31,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
-0xcd,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0x36,0x01,0x00,0x00,0x35,0x01,0x00,0x00,0x33,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xcd,0x00,0x00,0x00,0x36,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x2d,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x25,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x25,0x01,0x00,0x00,
-0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0xbc,0x01,0x00,0x00,0x31,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x22,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x24,0x01,0x00,0x00,
-0xaa,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,
-0xbb,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x3d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x3b,0x01,0x00,0x00,0x3c,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3c,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0x75,0x01,0x00,0x00,
-0xb7,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xcc,0x00,0x00,0x00,
-0x45,0x01,0x00,0x00,0xc9,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x46,0x01,0x00,0x00,
-0x45,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x47,0x01,0x00,0x00,
-0x48,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x25,0x00,0x00,0x00,
-0x44,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x48,0x01,0x00,0x00,
-0x46,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x3d,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x3d,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_0_f16_f32_len = 4636;
-
-unsigned char mul_mat_vec_q5_0_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0x05,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,
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-0x08,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x09,0x01,0x00,0x00,
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-0xb4,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x07,0x01,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x08,0x01,0x00,0x00,
-0x14,0x01,0x00,0x00,0x02,0x01,0x00,0x00,0x25,0x00,0x00,0x00,
-0x13,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
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-0x08,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,
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-0x17,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
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-0xac,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,0x27,0x01,0x00,0x00,
-0xba,0x01,0x00,0x00,0x5e,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x23,0x01,0x00,0x00,0x24,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x27,0x01,0x00,0x00,0x22,0x01,0x00,0x00,
-0x23,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x01,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,
-0xbb,0x00,0x00,0x00,0xba,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x2c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x2a,0x01,0x00,0x00,0x2b,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,
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-0x35,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x2c,0x01,0x00,0x00,
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-0xf9,0x00,0x02,0x00,0x24,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x24,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x38,0x01,0x00,0x00,0xba,0x01,0x00,0x00,0x31,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x21,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x23,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,0xd8,0x00,0x00,0x00,
-0x3a,0x01,0x00,0x00,0xbb,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x3c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x3a,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,
-0x3c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x3b,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00,
-0x73,0x01,0x00,0x00,0xb7,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0xcc,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x25,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x45,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x08,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x40,0x01,0x00,0x00,
-0x25,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x46,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x3c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x3c,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_0_f32_f32_len = 4604;
-
-unsigned char mul_mat_vec_q5_1_f16_f32_data[] = {
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-0xb8,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
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-0x47,0x00,0x04,0x00,0xc2,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0xaf,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x21,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x21,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x35,0x01,0x00,0x00,0xb7,0x01,0x00,0x00,0x2e,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x20,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,0xd4,0x00,0x00,0x00,
-0x37,0x01,0x00,0x00,0xb7,0x00,0x00,0x00,0x59,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x39,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x37,0x01,0x00,0x00,0x38,0x01,0x00,0x00,
-0x39,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x38,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
-0x71,0x01,0x00,0x00,0xb3,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc8,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x42,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x43,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x44,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x39,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x39,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_1_f16_f32_len = 4532;
-
-unsigned char mul_mat_vec_q5_1_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb6,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x7b,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0xac,0x00,0x00,0x00,
-0xb5,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0x3c,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
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-0xcd,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,
-0xaf,0x00,0x00,0x00,0x1a,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x1d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x1d,0x01,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,
-0x1c,0x01,0x00,0x00,0xcd,0x00,0x00,0x00,0x34,0x01,0x00,0x00,
-0x20,0x01,0x00,0x00,0xac,0x00,0x05,0x00,0xd4,0x00,0x00,0x00,
-0x23,0x01,0x00,0x00,0xb5,0x01,0x00,0x00,0x59,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x1f,0x01,0x00,0x00,0x20,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x23,0x01,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1e,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0xd4,0x00,0x00,0x00,
-0x26,0x01,0x00,0x00,0xb7,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x28,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x26,0x01,0x00,0x00,0x27,0x01,0x00,0x00,
-0x28,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
-0xb7,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0xc8,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,0xc5,0x00,0x00,0x00,
-0x2c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x2e,0x01,0x00,0x00,0x2d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x30,0x01,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0x31,0x01,0x00,0x00,
-0x30,0x01,0x00,0x00,0x2e,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xc9,0x00,0x00,0x00,0x31,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x28,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x28,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,
-0x1a,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x20,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x20,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0xb5,0x01,0x00,0x00,
-0x2e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1d,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1f,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xd4,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0xb7,0x00,0x00,0x00,
-0x59,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x38,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x36,0x01,0x00,0x00,
-0x37,0x01,0x00,0x00,0x38,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x37,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3f,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0xb3,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0xc8,0x00,0x00,0x00,0x40,0x01,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x40,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x04,0x01,0x00,0x00,0x42,0x01,0x00,0x00,
-0x3c,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x42,0x01,0x00,0x00,0x41,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x38,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x38,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q5_1_f32_f32_len = 4500;
-
-unsigned char mul_mat_vec_q5_k_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xff,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
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-0xd0,0x00,0x00,0x00,0x42,0x02,0x00,0x00,0xc6,0x04,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x4a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
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-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc9,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xca,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xcb,0x00,0x00,0x00,
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-0xce,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd0,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3f,0x02,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
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-0xa4,0x00,0x00,0x00,0x89,0x04,0x00,0x00,0xed,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,0x8d,0x04,0x00,0x00,
-0xfb,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0xa4,0x00,0x00,0x00,
-0x8e,0x04,0x00,0x00,0x1e,0x03,0x00,0x00,0x8d,0x04,0x00,0x00,
-0x0c,0x00,0x08,0x00,0xa4,0x00,0x00,0x00,0x8f,0x04,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xac,0x02,0x00,0x00,
-0x89,0x04,0x00,0x00,0x8e,0x04,0x00,0x00,0x70,0x00,0x04,0x00,
-0xa4,0x00,0x00,0x00,0x92,0x04,0x00,0x00,0x36,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0xa4,0x00,0x00,0x00,0x94,0x04,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x88,0x03,0x00,0x00,
-0x92,0x04,0x00,0x00,0x8f,0x04,0x00,0x00,0x70,0x00,0x04,0x00,
-0xa4,0x00,0x00,0x00,0x97,0x04,0x00,0x00,0x52,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0xa4,0x00,0x00,0x00,0x99,0x04,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xf7,0x03,0x00,0x00,
-0x97,0x04,0x00,0x00,0x94,0x04,0x00,0x00,0x85,0x00,0x05,0x00,
-0xa4,0x00,0x00,0x00,0x9d,0x04,0x00,0x00,0xde,0x00,0x00,0x00,
-0x81,0x04,0x00,0x00,0x7f,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,
-0xfe,0x04,0x00,0x00,0x9d,0x04,0x00,0x00,0x0c,0x00,0x08,0x00,
-0xa4,0x00,0x00,0x00,0x9e,0x04,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x99,0x04,0x00,0x00,
-0xfe,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,
-0xa0,0x04,0x00,0x00,0xaf,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0xa4,0x00,0x00,0x00,0xa1,0x04,0x00,0x00,0xa0,0x04,0x00,0x00,
-0x9e,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,0xaf,0x00,0x00,0x00,
-0xa1,0x04,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xa4,0x04,0x00,0x00,0xfa,0x04,0x00,0x00,0x51,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xb2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
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-0xa7,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0xa7,0x04,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xfb,0x04,0x00,0x00,
-0xa8,0x00,0x00,0x00,0xb4,0x00,0x00,0x00,0xbe,0x04,0x00,0x00,
-0xaa,0x04,0x00,0x00,0xac,0x00,0x05,0x00,0xb9,0x00,0x00,0x00,
-0xad,0x04,0x00,0x00,0xfb,0x04,0x00,0x00,0x4b,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xa9,0x04,0x00,0x00,0xaa,0x04,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xad,0x04,0x00,0x00,
-0xa8,0x04,0x00,0x00,0xa9,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa8,0x04,0x00,0x00,0xb0,0x00,0x05,0x00,0xb9,0x00,0x00,0x00,
-0xb0,0x04,0x00,0x00,0x71,0x00,0x00,0x00,0xfb,0x04,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xb2,0x04,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xb0,0x04,0x00,0x00,0xb1,0x04,0x00,0x00,
-0xb2,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0xb1,0x04,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb6,0x04,0x00,0x00,
-0x71,0x00,0x00,0x00,0xfb,0x04,0x00,0x00,0x41,0x00,0x05,0x00,
-0xae,0x00,0x00,0x00,0xb7,0x04,0x00,0x00,0xa7,0x00,0x00,0x00,
-0xb6,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,
-0xb8,0x04,0x00,0x00,0xb7,0x04,0x00,0x00,0x41,0x00,0x05,0x00,
-0xae,0x00,0x00,0x00,0xb9,0x04,0x00,0x00,0xa7,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,
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-0xb8,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,0xb9,0x04,0x00,0x00,
-0xbb,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,0xb2,0x04,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb2,0x04,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x51,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0xa5,0x04,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xaa,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xaa,0x04,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xbe,0x04,0x00,0x00,0xfb,0x04,0x00,0x00,0x9a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa7,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa9,0x04,0x00,0x00,0xaa,0x00,0x05,0x00,0xb9,0x00,0x00,0x00,
-0xc0,0x04,0x00,0x00,0x71,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xc2,0x04,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xc0,0x04,0x00,0x00,0xc1,0x04,0x00,0x00,
-0xc2,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0xc1,0x04,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc9,0x04,0x00,0x00,
-0xf9,0x04,0x00,0x00,0x55,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0xae,0x00,0x00,0x00,0xca,0x04,0x00,0x00,0xa7,0x00,0x00,0x00,
-0x61,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,
-0xcb,0x04,0x00,0x00,0xca,0x04,0x00,0x00,0x41,0x00,0x06,0x00,
-0xcc,0x04,0x00,0x00,0xcd,0x04,0x00,0x00,0xc6,0x04,0x00,0x00,
-0x61,0x00,0x00,0x00,0xc9,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xcd,0x04,0x00,0x00,0xcb,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xc2,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,0xc2,0x04,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q5_k_f16_f32_len = 13436;
-
-unsigned char mul_mat_vec_q5_k_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xdf,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
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-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
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-0x4e,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x42,0x02,0x00,0x00,0xa7,0x04,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x48,0x00,0x05,0x00,0x18,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x18,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6e,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0xca,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
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-0x48,0x00,0x05,0x00,0xce,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xce,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd0,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3f,0x02,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x40,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
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-0x42,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
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-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
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-0x20,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x7a,0x04,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xe8,0x03,0x00,0x00,0x78,0x04,0x00,0x00,0x75,0x04,0x00,0x00,
-0x85,0x00,0x05,0x00,0xa4,0x00,0x00,0x00,0x7e,0x04,0x00,0x00,
-0xde,0x00,0x00,0x00,0x62,0x04,0x00,0x00,0x7f,0x00,0x04,0x00,
-0xa4,0x00,0x00,0x00,0xde,0x04,0x00,0x00,0x7e,0x04,0x00,0x00,
-0x0c,0x00,0x08,0x00,0xa4,0x00,0x00,0x00,0x7f,0x04,0x00,0x00,
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-0x7a,0x04,0x00,0x00,0xde,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,
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-0x4b,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x8a,0x04,0x00,0x00,
-0x8b,0x04,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x8e,0x04,0x00,0x00,0x89,0x04,0x00,0x00,0x8a,0x04,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x89,0x04,0x00,0x00,0xb0,0x00,0x05,0x00,
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-0xdb,0x04,0x00,0x00,0xf7,0x00,0x03,0x00,0x93,0x04,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x91,0x04,0x00,0x00,
-0x92,0x04,0x00,0x00,0x93,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x92,0x04,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
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-0x86,0x04,0x00,0x00,0xf9,0x00,0x02,0x00,0x8b,0x04,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8b,0x04,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x9f,0x04,0x00,0x00,0xdb,0x04,0x00,0x00,
-0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x88,0x04,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x8a,0x04,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xb9,0x00,0x00,0x00,0xa1,0x04,0x00,0x00,0x71,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xa3,0x04,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xa1,0x04,0x00,0x00,
-0xa2,0x04,0x00,0x00,0xa3,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa2,0x04,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xaa,0x04,0x00,0x00,0xd9,0x04,0x00,0x00,0x55,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0xae,0x00,0x00,0x00,0xab,0x04,0x00,0x00,
-0xa7,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xa4,0x00,0x00,0x00,0xac,0x04,0x00,0x00,0xab,0x04,0x00,0x00,
-0x41,0x00,0x06,0x00,0x46,0x02,0x00,0x00,0xad,0x04,0x00,0x00,
-0xa7,0x04,0x00,0x00,0x61,0x00,0x00,0x00,0xaa,0x04,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xad,0x04,0x00,0x00,0xac,0x04,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa3,0x04,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa3,0x04,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q5_k_f32_f32_len = 12924;
-
-unsigned char mul_mat_vec_q6_k_f16_f32_data[] = {
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-0x72,0x00,0x04,0x00,0xbe,0x00,0x00,0x00,0xc9,0x03,0x00,0x00,
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-0x1b,0x00,0x00,0x00,0xcb,0x03,0x00,0x00,0xca,0x03,0x00,0x00,
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-0xca,0x00,0x00,0x00,0xd3,0x03,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x61,0x00,0x00,0x00,0xd2,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc0,0x00,0x00,0x00,0xd4,0x03,0x00,0x00,0xd3,0x03,0x00,0x00,
-0x73,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0xd5,0x03,0x00,0x00,
-0xd4,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xbe,0x00,0x00,0x00,
-0xd9,0x03,0x00,0x00,0x5e,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x9d,0x00,0x00,0x00,0xda,0x03,0x00,0x00,0xd9,0x03,0x00,0x00,
-0x85,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0xdb,0x03,0x00,0x00,
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-0xcd,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
-0xe2,0x03,0x00,0x00,0x92,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,
-0xbb,0x00,0x00,0x00,0xe3,0x03,0x00,0x00,0xe2,0x03,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xe4,0x03,0x00,0x00,0xe3,0x03,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0xe5,0x03,0x00,0x00,0xe4,0x03,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0xea,0x03,0x00,0x00,
-0x9a,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,0xbb,0x00,0x00,0x00,
-0xeb,0x03,0x00,0x00,0xea,0x03,0x00,0x00,0x3b,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xec,0x03,0x00,0x00,
-0xeb,0x03,0x00,0x00,0x7c,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0xed,0x03,0x00,0x00,0xec,0x03,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x1b,0x00,0x00,0x00,0xee,0x03,0x00,0x00,0xed,0x03,0x00,0x00,
-0xc9,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0xef,0x03,0x00,0x00,0xee,0x03,0x00,0x00,0x3b,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0xf0,0x03,0x00,0x00,
-0xe5,0x03,0x00,0x00,0xef,0x03,0x00,0x00,0x72,0x00,0x04,0x00,
-0xbe,0x00,0x00,0x00,0xf1,0x03,0x00,0x00,0xf0,0x03,0x00,0x00,
-0x72,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0xf2,0x03,0x00,0x00,
-0xf1,0x03,0x00,0x00,0x82,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0xf3,0x03,0x00,0x00,0xf2,0x03,0x00,0x00,0x14,0x01,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0xf4,0x03,0x00,0x00,
-0xf3,0x03,0x00,0x00,0x0c,0x00,0x08,0x00,0x9d,0x00,0x00,0x00,
-0xf6,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xdc,0x03,0x00,0x00,0xf4,0x03,0x00,0x00,0xce,0x03,0x00,0x00,
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-0xde,0x00,0x00,0x00,0x2c,0x04,0x00,0x00,0x41,0x00,0x06,0x00,
-0xca,0x00,0x00,0x00,0xfb,0x03,0x00,0x00,0xdb,0x00,0x00,0x00,
-0x61,0x00,0x00,0x00,0xfa,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,
-0xc0,0x00,0x00,0x00,0xfc,0x03,0x00,0x00,0xfb,0x03,0x00,0x00,
-0x73,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0xfd,0x03,0x00,0x00,
-0xfc,0x03,0x00,0x00,0x3d,0x00,0x04,0x00,0xbe,0x00,0x00,0x00,
-0x01,0x04,0x00,0x00,0x97,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,
-0x9d,0x00,0x00,0x00,0x02,0x04,0x00,0x00,0x01,0x04,0x00,0x00,
-0x85,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0x03,0x04,0x00,0x00,
-0xfd,0x03,0x00,0x00,0x02,0x04,0x00,0x00,0x85,0x00,0x05,0x00,
-0x9d,0x00,0x00,0x00,0x04,0x04,0x00,0x00,0x03,0x04,0x00,0x00,
-0xcd,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,
-0x0a,0x04,0x00,0x00,0xb9,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,
-0xbb,0x00,0x00,0x00,0x0b,0x04,0x00,0x00,0x0a,0x04,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x04,0x00,0x00,0x0b,0x04,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x0d,0x04,0x00,0x00,0x0c,0x04,0x00,0x00,
-0x3d,0x00,0x04,0x00,0xbb,0x00,0x00,0x00,0x12,0x04,0x00,0x00,
-0x9a,0x03,0x00,0x00,0xc2,0x00,0x05,0x00,0xbb,0x00,0x00,0x00,
-0x13,0x04,0x00,0x00,0x12,0x04,0x00,0x00,0x45,0x00,0x00,0x00,
-0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x14,0x04,0x00,0x00,
-0x13,0x04,0x00,0x00,0x7c,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x15,0x04,0x00,0x00,0x14,0x04,0x00,0x00,0xc7,0x00,0x05,0x00,
-0x1b,0x00,0x00,0x00,0x16,0x04,0x00,0x00,0x15,0x04,0x00,0x00,
-0xc9,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x17,0x04,0x00,0x00,0x16,0x04,0x00,0x00,0x3b,0x00,0x00,0x00,
-0xc5,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x18,0x04,0x00,0x00,
-0x0d,0x04,0x00,0x00,0x17,0x04,0x00,0x00,0x72,0x00,0x04,0x00,
-0xbe,0x00,0x00,0x00,0x19,0x04,0x00,0x00,0x18,0x04,0x00,0x00,
-0x72,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x1a,0x04,0x00,0x00,
-0x19,0x04,0x00,0x00,0x82,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x1b,0x04,0x00,0x00,0x1a,0x04,0x00,0x00,0x14,0x01,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,0x1c,0x04,0x00,0x00,
-0x1b,0x04,0x00,0x00,0x0c,0x00,0x08,0x00,0x9d,0x00,0x00,0x00,
-0x1e,0x04,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x04,0x04,0x00,0x00,0x1c,0x04,0x00,0x00,0xf6,0x03,0x00,0x00,
-0x81,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0x1f,0x04,0x00,0x00,
-0x77,0x03,0x00,0x00,0x1e,0x04,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x9d,0x00,0x00,0x00,0xc9,0x01,0x00,0x00,0xa8,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0xca,0x01,0x00,0x00,
-0xc9,0x01,0x00,0x00,0x1f,0x04,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xa8,0x00,0x00,0x00,0xca,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xcd,0x01,0x00,0x00,0x23,0x02,0x00,0x00,
-0x51,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xab,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xad,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x51,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0xce,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd0,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd0,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x24,0x02,0x00,0x00,0xa1,0x00,0x00,0x00,0xad,0x00,0x00,0x00,
-0xe7,0x01,0x00,0x00,0xd3,0x01,0x00,0x00,0xac,0x00,0x05,0x00,
-0xb2,0x00,0x00,0x00,0xd6,0x01,0x00,0x00,0x24,0x02,0x00,0x00,
-0x4b,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xd2,0x01,0x00,0x00,
-0xd3,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd6,0x01,0x00,0x00,0xd1,0x01,0x00,0x00,0xd2,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd1,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
-0xb2,0x00,0x00,0x00,0xd9,0x01,0x00,0x00,0x71,0x00,0x00,0x00,
-0x24,0x02,0x00,0x00,0xf7,0x00,0x03,0x00,0xdb,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd9,0x01,0x00,0x00,
-0xda,0x01,0x00,0x00,0xdb,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xda,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xdf,0x01,0x00,0x00,0x71,0x00,0x00,0x00,0x24,0x02,0x00,0x00,
-0x41,0x00,0x05,0x00,0xa7,0x00,0x00,0x00,0xe0,0x01,0x00,0x00,
-0xa0,0x00,0x00,0x00,0xdf,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x9d,0x00,0x00,0x00,0xe1,0x01,0x00,0x00,0xe0,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0xa7,0x00,0x00,0x00,0xe2,0x01,0x00,0x00,
-0xa0,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x9d,0x00,0x00,0x00,0xe3,0x01,0x00,0x00,0xe2,0x01,0x00,0x00,
-0x81,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0xe4,0x01,0x00,0x00,
-0xe3,0x01,0x00,0x00,0xe1,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xe2,0x01,0x00,0x00,0xe4,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xdb,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xdb,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x51,0x00,0x00,0x00,
-0xce,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xd3,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd3,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x24,0x02,0x00,0x00,
-0x05,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xd0,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd2,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xb2,0x00,0x00,0x00,0xe9,0x01,0x00,0x00,0x71,0x00,0x00,0x00,
-0x4b,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xeb,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xe9,0x01,0x00,0x00,
-0xea,0x01,0x00,0x00,0xeb,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xea,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xf2,0x01,0x00,0x00,0x22,0x02,0x00,0x00,0x55,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0xa7,0x00,0x00,0x00,0xf3,0x01,0x00,0x00,
-0xa0,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x9d,0x00,0x00,0x00,0xf4,0x01,0x00,0x00,0xf3,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xf5,0x01,0x00,0x00,0xf6,0x01,0x00,0x00,
-0xef,0x01,0x00,0x00,0x61,0x00,0x00,0x00,0xf2,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf6,0x01,0x00,0x00,0xf4,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xeb,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xeb,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q6_k_f16_f32_len = 12108;
-
-unsigned char mul_mat_vec_q6_k_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x1d,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,
-0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,
-0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0xa0,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0xdb,0x00,0x00,0x00,0xec,0x01,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xd3,0x01,0x00,0x00,
-0xce,0x01,0x00,0x00,0xcf,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xce,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0xb2,0x00,0x00,0x00,
-0xd6,0x01,0x00,0x00,0x71,0x00,0x00,0x00,0x20,0x02,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xd8,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xd6,0x01,0x00,0x00,0xd7,0x01,0x00,0x00,
-0xd8,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xd7,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdc,0x01,0x00,0x00,
-0x71,0x00,0x00,0x00,0x20,0x02,0x00,0x00,0x41,0x00,0x05,0x00,
-0xa7,0x00,0x00,0x00,0xdd,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,
-0xdc,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,
-0xde,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0xa7,0x00,0x00,0x00,0xdf,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,
-0xe0,0x01,0x00,0x00,0xdf,0x01,0x00,0x00,0x81,0x00,0x05,0x00,
-0x9d,0x00,0x00,0x00,0xe1,0x01,0x00,0x00,0xe0,0x01,0x00,0x00,
-0xde,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0xdf,0x01,0x00,0x00,
-0xe1,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xd8,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xd8,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x51,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0xcb,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd0,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xd0,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe4,0x01,0x00,0x00,0x20,0x02,0x00,0x00,0x05,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xcd,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xcf,0x01,0x00,0x00,0xaa,0x00,0x05,0x00,0xb2,0x00,0x00,0x00,
-0xe6,0x01,0x00,0x00,0x71,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xe8,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xe6,0x01,0x00,0x00,0xe7,0x01,0x00,0x00,
-0xe8,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xe7,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x01,0x00,0x00,
-0x1e,0x02,0x00,0x00,0x55,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0xa7,0x00,0x00,0x00,0xf0,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,
-0x61,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x9d,0x00,0x00,0x00,
-0xf1,0x01,0x00,0x00,0xf0,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xe3,0x00,0x00,0x00,0xf2,0x01,0x00,0x00,0xec,0x01,0x00,0x00,
-0x61,0x00,0x00,0x00,0xef,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xf2,0x01,0x00,0x00,0xf1,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xe8,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xe8,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q6_k_f32_f32_len = 11852;
-
-unsigned char mul_mat_vec_q8_0_f16_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x67,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0x11,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x20,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x20,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4c,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4c,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x81,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x97,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xd0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xd0,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd2,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd2,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x0f,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x0f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x0f,0x01,0x00,0x00,
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-0x47,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1a,0x01,0x00,0x00,
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-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
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-0x16,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1e,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
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-0x3d,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0xd8,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xda,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,0x51,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,0x62,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
-0xd2,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xdd,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x08,0x00,0x00,0x00,
-0xe8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xce,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xea,0x00,0x00,0x00,
-0x9e,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x9e,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,
-0x65,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xa0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa2,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf2,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf2,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0xf1,0x00,0x00,0x00,
-0xa2,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0xf5,0x00,0x00,0x00,
-0xac,0x00,0x05,0x00,0xa9,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x66,0x01,0x00,0x00,0x7e,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0xf4,0x00,0x00,0x00,0xf5,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,
-0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf3,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0xa9,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xfd,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xfb,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x8c,0x00,0x00,0x00,
-0x66,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,
-0x02,0x01,0x00,0x00,0x9a,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x03,0x01,0x00,0x00,
-0x02,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x05,0x01,0x00,0x00,0x9e,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x05,0x01,0x00,0x00,
-0x03,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x9e,0x00,0x00,0x00,
-0x06,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xfd,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x84,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf5,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x09,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf2,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf4,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0xa9,0x00,0x00,0x00,
-0x0b,0x01,0x00,0x00,0x8c,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x0d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x0b,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,
-0x0d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0c,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
-0x45,0x01,0x00,0x00,0x88,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x9d,0x00,0x00,0x00,0x15,0x01,0x00,0x00,0x9a,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x16,0x01,0x00,0x00,0x15,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x17,0x01,0x00,0x00,0x18,0x01,0x00,0x00,0x11,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x14,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x18,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x0d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x0d,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t mul_mat_vec_q8_0_f16_f32_len = 4064;
-
-unsigned char mul_mat_vec_q8_0_f32_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x65,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,
-0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,
-0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,
-0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,
-0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,
-0x00,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x46,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x8a,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0x10,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x1e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x20,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x20,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x46,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4c,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x4c,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x4c,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x4c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x7d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x81,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x97,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xcf,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xd0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xd0,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd2,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd2,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0d,0x01,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x0e,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x0e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x0e,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x10,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x10,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x17,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x18,0x01,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x08,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x18,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x19,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x1b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x1e,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x1e,0x00,0x00,0x00,
-0x1d,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
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-0x3d,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x4b,0x01,0x00,0x00,
-0x4a,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x4c,0x01,0x00,0x00,0x4b,0x01,0x00,0x00,0x41,0x00,0x08,0x00,
-0x2f,0x00,0x00,0x00,0x51,0x01,0x00,0x00,0x20,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x49,0x01,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xbc,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x19,0x00,0x00,0x00,
-0x52,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x72,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x53,0x01,0x00,0x00,0x52,0x01,0x00,0x00,
-0x6f,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
-0x53,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x59,0x01,0x00,0x00,0xbc,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x41,0x00,0x08,0x00,0x2f,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,
-0x20,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x49,0x01,0x00,0x00,
-0x2d,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x19,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,
-0x72,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,
-0x5b,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x5d,0x01,0x00,0x00,0x5c,0x01,0x00,0x00,0x50,0x00,0x05,0x00,
-0x09,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,0x54,0x01,0x00,0x00,
-0x5d,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,0x09,0x00,0x00,0x00,
-0x60,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,
-0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xce,0x00,0x00,0x00,
-0x60,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,
-0xc1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xd8,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0xd2,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0xd9,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x08,0x00,0x00,0x00,
-0xdd,0x00,0x00,0x00,0x60,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0xd7,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xd8,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xd2,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
-0xdd,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x08,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x08,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0xe7,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x9e,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xed,0x00,0x00,0x00,0x63,0x01,0x00,0x00,0x84,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xa0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xa2,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x84,0x00,0x00,0x00,
-0x84,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf1,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x64,0x01,0x00,0x00,
-0xf0,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x08,0x01,0x00,0x00,
-0xf4,0x00,0x00,0x00,0xac,0x00,0x05,0x00,0xa9,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x64,0x01,0x00,0x00,0x7e,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0xf3,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf7,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf2,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0xa9,0x00,0x00,0x00,
-0xfa,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0x64,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xfc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x64,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x9d,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x9a,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x08,0x00,0x00,0x00,
-0x02,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x9e,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x08,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
-0x04,0x01,0x00,0x00,0x02,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x9e,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xfc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xfc,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x84,0x00,0x00,0x00,
-0xee,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,0xc2,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x64,0x01,0x00,0x00,
-0x2d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf1,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf3,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,
-0xa9,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x8c,0x00,0x00,0x00,
-0x7e,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x0c,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x0a,0x01,0x00,0x00,
-0x0b,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x13,0x01,0x00,0x00,0x43,0x01,0x00,0x00,0x88,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x9d,0x00,0x00,0x00,0x14,0x01,0x00,0x00,
-0x9a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x08,0x00,0x00,0x00,0x15,0x01,0x00,0x00,0x14,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0xd8,0x00,0x00,0x00,0x16,0x01,0x00,0x00,
-0x10,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x13,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x16,0x01,0x00,0x00,0x15,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x0c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x0c,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t mul_mat_vec_q8_0_f32_f32_len = 4032;
-
-unsigned char norm_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x27,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x27,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x27,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x27,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x34,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x36,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x98,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x9a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x1c,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2c,0x00,0x05,0x00,
-0x15,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1d,0x00,0x00,0x00,
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-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t norm_f32_len = 2624;
-
-unsigned char relu_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x34,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1f,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x20,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x22,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x25,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x27,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x27,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x1f,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x20,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x21,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x24,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x25,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x29,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0x31,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x32,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
-0x0c,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x33,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x1c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x32,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x29,0x00,0x00,0x00,
-0x2a,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x0c,0x00,0x07,0x00,
-0x11,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x29,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x2e,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x32,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x32,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t relu_f32_len = 1212;
-
-unsigned char rms_norm_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x9e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x25,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x25,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x31,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x32,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x32,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x34,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x34,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x82,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x83,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x83,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x83,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x85,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x85,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x14,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x1c,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,
-0x25,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x26,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x2d,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x31,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x32,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x3b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x49,0x00,0x00,0x00,0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x00,0x01,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
-0x71,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x72,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x82,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x83,0x00,0x00,0x00,0x82,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x84,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x83,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x84,0x00,0x00,0x00,
-0x85,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x99,0x00,0x00,0x00,
-0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1f,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x9b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x2a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x2e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3b,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x14,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x43,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x21,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4d,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x28,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,
-0x4c,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x50,0x00,0x00,0x00,0xad,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x50,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x53,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0x9c,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x56,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x59,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x57,0x00,0x00,0x00,
-0x58,0x00,0x00,0x00,0x59,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x58,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5e,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
-0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x60,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,0x63,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x59,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x59,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,0x48,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x50,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x50,0x00,0x00,0x00,
-0xc3,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x9c,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4f,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x70,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x88,0x00,0x05,0x00,0x14,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x72,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x73,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x6e,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,
-0x14,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x79,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x98,0x00,0x00,0x00,
-0x7a,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
-0x81,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0xf6,0x00,0x04,0x00,0x7b,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x81,0x00,0x00,0x00,
-0x7a,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x7a,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x34,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x93,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x95,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x94,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x3b,0x00,0x00,0x00,0x96,0x00,0x00,0x00,0x85,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x8b,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x96,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x7b,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t rms_norm_f32_len = 2344;
-
-unsigned char rope_neox_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x50,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
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-0x73,0x00,0x04,0x00,0x86,0x00,0x00,0x00,0x04,0x01,0x00,0x00,
-0x03,0x01,0x00,0x00,0x41,0x00,0x06,0x00,0x93,0x00,0x00,0x00,
-0x05,0x01,0x00,0x00,0x8a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0xe9,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x05,0x01,0x00,0x00,
-0x04,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x08,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x08,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t rope_neox_f16_len = 3952;
-
-unsigned char rope_neox_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
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-0xe8,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x7f,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00,0xf0,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0x32,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x92,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0x32,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xe0,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0xfd,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x92,0x00,0x00,0x00,0xff,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xff,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x02,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x02,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t rope_neox_f32_len = 3852;
-
-unsigned char rope_norm_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x49,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x67,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0xb0,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0x10,0x00,0x06,0x00,
-0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x67,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x87,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x88,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x88,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x88,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8a,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x8a,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x8d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x8e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x8e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x8e,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x90,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x90,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xae,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xae,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xae,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xb0,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc7,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xc8,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xc8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xc8,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xca,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xca,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x00,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x16,0x00,0x03,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x6f,0x12,0x83,0x3a,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,
-0x1e,0x00,0x0c,0x00,0x29,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x3b,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x40,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0xcd,0xcc,0xcc,0x3d,0x17,0x00,0x04,0x00,0x65,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x66,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x65,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x66,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
-0x68,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x69,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x72,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x16,0x00,0x03,0x00,
-0x86,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x87,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x88,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x89,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x88,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x89,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x8d,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x8e,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x8f,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x8e,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x8f,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x93,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x86,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,
-0xa8,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0xad,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0xae,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0xaf,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xae,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xb2,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0xb6,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xc7,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xc8,0x00,0x00,0x00,
-0xc7,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0xc9,0x00,0x00,0x00,0xca,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,
-0xff,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x2c,0x00,0x06,0x00,
-0x65,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x68,0x00,0x00,0x00,
-0xff,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
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-0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x33,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x44,0x01,0x00,0x00,
-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,
-0x33,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x93,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0x90,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x86,0x00,0x00,0x00,0xe1,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
-0xe1,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,
-0xe5,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x68,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x93,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x86,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0xe8,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0x35,0x01,0x00,0x00,0x7f,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x48,0x01,0x00,0x00,0xf0,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,
-0x06,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x32,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x31,0x01,0x00,0x00,
-0x48,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
-0xf2,0x00,0x00,0x00,0xf1,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x93,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xf3,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0x31,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xe2,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0xfb,0x00,0x00,0x00,
-0x73,0x00,0x04,0x00,0x86,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x93,0x00,0x00,0x00,
-0xfe,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x41,0x00,0x00,0x00,
-0xe5,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0xfe,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x01,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x01,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t rope_norm_f16_len = 3952;
-
-unsigned char rope_norm_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x43,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
-0x8f,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x29,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x24,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x29,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x29,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1c,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x86,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x87,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x89,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x89,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x8d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x8d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x8d,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x47,0x00,0x04,0x00,0x8f,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xac,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xad,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xad,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xad,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xaf,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xaf,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0xc7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0xc7,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0xc7,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc9,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
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-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,
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-0x1f,0x00,0x00,0x00,0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,
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-0x07,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x07,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x2a,0x00,0x00,0x00,
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-0x15,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
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-0x2d,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2e,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
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-0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x21,0x01,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x52,0x00,0x00,0x00,
-0x22,0x01,0x00,0x00,0x1f,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x07,0x01,0x00,0x00,
-0x24,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x27,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x27,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,0x07,0x01,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
-0x0a,0x01,0x00,0x00,0xc5,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,
-0x0f,0x01,0x00,0x00,0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,
-0x29,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,
-0x3e,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,
-0x0c,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x3e,0x01,0x00,0x00,
-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,
-0x2d,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x92,0x00,0x00,0x00,0xde,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xde,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
-0xa4,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x92,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,0xe3,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x7f,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0xec,0x00,0x00,0x00,
-0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x92,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x89,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0xa4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0xe4,0x00,0x00,0x00,
-0x2b,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00,
-0xf7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xdf,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0xf6,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x92,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,
-0x89,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0xe2,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf8,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xfb,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xfb,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t rope_norm_f32_len = 3852;
-
-unsigned char scale_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x9c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
-0xe3,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x07,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x44,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x12,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x48,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xc7,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xd4,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xd5,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xd5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xd5,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd7,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xd7,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xe0,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xe1,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0xe1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xe1,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe3,0x00,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0xe3,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0xf3,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x16,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x55,0x00,0x00,0x00,
-0x08,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x5a,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x6f,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x76,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0xad,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0xb2,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0xc5,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xc6,0x00,0x00,0x00,
-0xc7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x14,0x00,0x02,0x00,
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-0xcc,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe9,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0xec,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xea,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0xe8,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xd7,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t scale_f32_len = 3320;
-
-unsigned char silu_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x3b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x12,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x22,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,
-0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,
-0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x11,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x12,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x1a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x21,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x22,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x29,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x00,0x02,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2c,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x37,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x39,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,
-0x0c,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x1c,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x39,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x28,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x7f,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x2f,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x88,0x00,0x05,0x00,
-0x11,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0x33,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00,
-0x35,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x35,0x00,0x00,0x00,
-0x34,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x39,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x39,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,
-0x38,0x00,0x01,0x00,
-};
-const uint64_t silu_f32_len = 1264;
-
-unsigned char soft_max_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x6f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,
-0x0c,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x74,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x75,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
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-0x14,0x01,0x00,0x00,0x15,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x14,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x1c,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x1c,0x01,0x00,0x00,0xf7,0x00,0x03,0x00,0x1f,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x1d,0x01,0x00,0x00,
-0x1e,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1e,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x24,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0x1c,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0xa5,0x00,0x00,0x00,0x25,0x01,0x00,0x00,
-0xa2,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
-0xa6,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x29,0x01,0x00,0x00,0x28,0x01,0x00,0x00,0x26,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xa6,0x00,0x00,0x00,0x29,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x1f,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x1f,0x01,0x00,0x00,0xe0,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x16,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x16,0x01,0x00,0x00,
-0xc3,0x00,0x05,0x00,0x1a,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,
-0x65,0x01,0x00,0x00,0x1b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x13,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x15,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,
-0xc6,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x31,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x31,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x15,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x34,0x01,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0x39,0x01,0x00,0x00,
-0x66,0x01,0x00,0x00,0x66,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x33,0x01,0x00,0x00,0x34,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x39,0x01,0x00,0x00,0x32,0x01,0x00,0x00,
-0x33,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x32,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,
-0x66,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0xae,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
-0x66,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x43,0x01,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x41,0x01,0x00,0x00,
-0x42,0x01,0x00,0x00,0x43,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x42,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x33,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x43,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x13,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4a,0x01,0x00,0x00,0x48,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,
-0x41,0x00,0x06,0x00,0x7e,0x00,0x00,0x00,0x4c,0x01,0x00,0x00,
-0x0c,0x01,0x00,0x00,0x64,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x4d,0x01,0x00,0x00,
-0x4c,0x01,0x00,0x00,0x88,0x00,0x05,0x00,0x16,0x00,0x00,0x00,
-0x4e,0x01,0x00,0x00,0x4d,0x01,0x00,0x00,0x2f,0x01,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x4c,0x01,0x00,0x00,0x4e,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x34,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x34,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x51,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x9d,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x31,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x33,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t soft_max_f32_len = 4836;
-
-unsigned char soft_max_f32_f16_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x73,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,
-0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,
-0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,
-0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0x10,0x01,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x17,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x74,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x75,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x75,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x77,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8d,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,
-0x8e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x8e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x8e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x90,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x90,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0d,0x01,0x00,0x00,
-0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x0e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x0e,0x01,0x00,0x00,
-0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x10,0x01,0x00,0x00,
-0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x10,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x56,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x16,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x1e,0x00,0x09,0x00,0x17,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x18,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x1c,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x80,0x3f,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x24,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x28,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,
-0x3a,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x05,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x1a,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x74,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x76,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x76,0x00,0x00,0x00,
-0x77,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x7e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x81,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x8c,0x00,0x00,0x00,
-0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x8d,0x00,0x00,0x00,
-0x8c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x8e,0x00,0x00,0x00,
-0x8d,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x8f,0x00,0x00,0x00,
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-0xb7,0x00,0x00,0x00,0x60,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0xb7,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0xba,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,
-0xb9,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xb9,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0xb7,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0xa8,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,0xc2,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,
-0xc3,0x00,0x00,0x00,0x0c,0x00,0x07,0x00,0x16,0x00,0x00,0x00,
-0xc5,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x28,0x00,0x00,0x00,
-0xbe,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xa9,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xba,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xba,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0xaa,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb1,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb1,0x00,0x00,0x00,0xc3,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x60,0x01,0x00,0x00,
-0x1b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xae,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xb0,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0xa8,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0xca,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x4e,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0xaa,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xa9,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xce,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xce,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x61,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,
-0x15,0x01,0x00,0x00,0xd1,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0xd6,0x00,0x00,0x00,0x61,0x01,0x00,0x00,
-0x66,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0xd0,0x00,0x00,0x00,
-0xd1,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xd6,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xcf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x61,0x01,0x00,0x00,
-0x0f,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
-0xde,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0xe0,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0xde,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
-0xe0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdf,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xd0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xe0,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,
-0xe6,0x00,0x00,0x00,0xda,0x00,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7e,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x77,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x24,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00,
-0xac,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0xf2,0x00,0x00,0x00,
-0x1e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0xf5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0xf2,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0x01,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0xfc,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0xda,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x97,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,
-0x90,0x00,0x00,0x00,0x64,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x8c,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,
-0xfd,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0xff,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x85,0x00,0x05,0x00,
-0x16,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x5c,0x01,0x00,0x00,
-0xff,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x01,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf5,0x00,0x00,0x00,
-0xf5,0x00,0x07,0x00,0x16,0x00,0x00,0x00,0x65,0x01,0x00,0x00,
-0x00,0x01,0x00,0x00,0xf4,0x00,0x00,0x00,0x27,0x00,0x00,0x00,
-0x01,0x01,0x00,0x00,0x0c,0x00,0x08,0x00,0x16,0x00,0x00,0x00,
-0x03,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,
-0xec,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x65,0x01,0x00,0x00,
-0x83,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x05,0x01,0x00,0x00,
-0x03,0x01,0x00,0x00,0xca,0x00,0x00,0x00,0x0c,0x00,0x06,0x00,
-0x16,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0x1b,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0xa9,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,
-0x0a,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xa9,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7e,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x10,0x01,0x00,0x00,
-0x64,0x00,0x00,0x00,0xe8,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x13,0x01,0x00,0x00,0x06,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xd1,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd1,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x15,0x01,0x00,0x00,
-0x61,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0xce,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xd0,0x00,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0xaa,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x17,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x17,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x1a,0x00,0x00,0x00,0x69,0x01,0x00,0x00,0xad,0x00,0x00,0x00,
-0xd0,0x00,0x00,0x00,0x30,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,
-0xad,0x00,0x05,0x00,0x28,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,
-0x69,0x01,0x00,0x00,0x64,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x19,0x01,0x00,0x00,0x1a,0x01,0x00,0x00,0x01,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x1d,0x01,0x00,0x00,0x18,0x01,0x00,0x00,
-0x19,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x18,0x01,0x00,0x00,
-0x7c,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0x69,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
-0x21,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0x20,0x01,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x23,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x21,0x01,0x00,0x00,0x22,0x01,0x00,0x00,
-0x23,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x20,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0xa8,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0xa5,0x00,0x00,0x00,
-0x28,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,
-0x2a,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0xa9,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,
-0x2c,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0xa9,0x00,0x00,0x00,0x2d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x23,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x23,0x01,0x00,0x00,
-0xe0,0x00,0x04,0x00,0x4e,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0xaa,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1a,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1a,0x01,0x00,0x00,0xc3,0x00,0x05,0x00,
-0x1a,0x00,0x00,0x00,0x30,0x01,0x00,0x00,0x69,0x01,0x00,0x00,
-0x1b,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x17,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x19,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0xc9,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x35,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x35,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,
-0x6a,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x19,0x01,0x00,0x00,
-0x55,0x01,0x00,0x00,0x38,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,
-0x28,0x00,0x00,0x00,0x3d,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,
-0x66,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x37,0x01,0x00,0x00,
-0x38,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x3d,0x01,0x00,0x00,0x36,0x01,0x00,0x00,0x37,0x01,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x36,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,
-0x0f,0x00,0x00,0x00,0xae,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
-0x45,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x66,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x47,0x01,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0x46,0x01,0x00,0x00,
-0x47,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x46,0x01,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x37,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x47,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x4c,0x01,0x00,0x00,0x13,0x00,0x00,0x00,0x66,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,
-0x4c,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0x7e,0x00,0x00,0x00,0x50,0x01,0x00,0x00,0x10,0x01,0x00,0x00,
-0x64,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x51,0x01,0x00,0x00,0x50,0x01,0x00,0x00,
-0x88,0x00,0x05,0x00,0x16,0x00,0x00,0x00,0x52,0x01,0x00,0x00,
-0x51,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x50,0x01,0x00,0x00,0x52,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x38,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x38,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,
-0x6a,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x35,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x37,0x01,0x00,0x00,
-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-};
-const uint64_t soft_max_f32_f16_len = 4904;
-
-unsigned char split_k_reduce_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,
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-0x24,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x2c,0x00,0x00,0x00,0x24,0x00,0x00,0x00,0x25,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x24,0x00,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x36,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x37,0x00,0x00,0x00,0x38,0x00,0x00,0x00,
-0x30,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x36,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x1e,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x81,0x00,0x05,0x00,0x1e,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x39,0x00,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,
-0x4e,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,
-0x23,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x25,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x37,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x41,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x44,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x48,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x48,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t split_k_reduce_len = 1416;
-
-unsigned char sqr_f32_data[] = {
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-0x9c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
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-0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
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-0x23,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
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-0x09,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x2c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x12,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x38,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,
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-0x47,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x49,0x01,0x00,0x00,0x37,0x01,0x00,0x00,0x48,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,
-0x45,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x41,0x00,0x06,0x00,
-0xde,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0xd9,0x00,0x00,0x00,
-0xcc,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x11,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0xe5,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0xe6,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x54,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x56,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0x56,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00,
-0x55,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0x76,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x58,0x01,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x5c,0x01,0x00,0x00,0xcb,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x63,0x01,0x00,0x00,0x60,0x01,0x00,0x00,
-0x57,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x66,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,
-0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,
-0xcb,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,0x57,0x01,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x6f,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x73,0x01,0x00,0x00,
-0x6f,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x73,0x01,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x7b,0x01,0x00,0x00,0x69,0x01,0x00,0x00,0x76,0x01,0x00,0x00,
-0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,
-0x7b,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x87,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,
-0x5a,0x01,0x00,0x00,0x82,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x88,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,0x87,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0x8a,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,
-0x5c,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,0x41,0x00,0x05,0x00,
-0x17,0x00,0x00,0x00,0x8e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,
-0xb2,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x84,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,
-0x8f,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x91,0x01,0x00,0x00,0x8c,0x01,0x00,0x00,0x90,0x01,0x00,0x00,
-0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0x93,0x01,0x00,0x00,
-0x14,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,
-0x7e,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x96,0x01,0x00,0x00,0x91,0x01,0x00,0x00,
-0x95,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,
-0x98,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,
-0x98,0x01,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,
-0x9a,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x99,0x01,0x00,0x00,
-0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9b,0x01,0x00,0x00,
-0x96,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,
-0x9b,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,
-0xef,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,0xe0,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0xde,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,
-0xe4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0xec,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0xf0,0x00,0x00,0x00,0xef,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0xf4,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0xf4,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t sqr_f32_len = 3252;
-
-unsigned char sum_rows_f32_data[] = {
-0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00,
-0x76,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00,
-0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,
-0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,
-0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,
-0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,
-0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x15,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,
-0x25,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,
-0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
-0x48,0x00,0x05,0x00,0x25,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00,
-0x25,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x30,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x31,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x31,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x31,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x33,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x69,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x48,0x00,0x04,0x00,0x6a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x6a,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x03,0x00,0x6a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,
-0x71,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x47,0x00,0x04,0x00,0x73,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,
-0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x00,0x00,0x00,0x00,0x17,0x00,0x04,0x00,0x09,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0a,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x14,0x00,0x00,0x00,
-0x20,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,
-0x16,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x16,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x17,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x1b,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x1e,0x00,0x06,0x00,0x25,0x00,0x00,0x00,
-0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x14,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00,
-0x09,0x00,0x00,0x00,0x25,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,
-0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0x09,0x00,0x00,0x00,
-0x15,0x00,0x04,0x00,0x28,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,
-0x14,0x00,0x02,0x00,0x2d,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,
-0x30,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,
-0x31,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x20,0x00,0x04,0x00,
-0x32,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x31,0x00,0x00,0x00,
-0x3b,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0x33,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x3a,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x28,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x15,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x28,0x00,0x00,0x00,
-0x48,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,
-0x28,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x87,0x00,0x00,0x00,
-0x47,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,
-0x28,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x1d,0x00,0x03,0x00,0x69,0x00,0x00,0x00,0x14,0x00,0x00,0x00,
-0x1e,0x00,0x03,0x00,0x6a,0x00,0x00,0x00,0x69,0x00,0x00,0x00,
-0x20,0x00,0x04,0x00,0x6b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0x6a,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x6b,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
-0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,
-0x73,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x72,0x00,0x00,0x00,
-0x72,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,
-0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x0d,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x11,0x00,0x00,0x00,
-0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x41,0x00,0x05,0x00,
-0x1b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
-0x1a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,
-0x06,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x05,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0x27,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,
-0x21,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x2e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,
-0x21,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00,
-0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x0f,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x37,0x00,0x00,0x00,
-0x74,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x3a,0x00,0x00,0x00,
-0x3b,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x29,0x00,0x00,0x00,
-0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x3c,0x00,0x00,0x00,0x3b,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,
-0x81,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,
-0x3e,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,
-0x1c,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x74,0x00,0x00,0x00,
-0x15,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x21,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x43,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4a,0x00,0x00,0x00,0xf5,0x00,0x07,0x00,0x28,0x00,0x00,0x00,
-0x75,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x21,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xad,0x00,0x05,0x00,
-0x2d,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0x29,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x4c,0x00,0x00,0x00,
-0x4d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x50,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x4b,0x00,0x00,0x00,0x7c,0x00,0x04,0x00,
-0x06,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x75,0x00,0x00,0x00,
-0xb0,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,0x54,0x00,0x00,0x00,
-0x13,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,
-0x56,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,
-0x54,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x56,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x55,0x00,0x00,0x00,0x80,0x00,0x05,0x00,
-0x06,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,
-0x53,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,
-0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,
-0x5c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x14,0x00,0x00,0x00,
-0x5f,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x81,0x00,0x05,0x00,
-0x14,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,
-0x5d,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x1c,0x00,0x00,0x00,
-0x60,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x56,0x00,0x00,0x00,
-0xf8,0x00,0x02,0x00,0x56,0x00,0x00,0x00,0xe0,0x00,0x04,0x00,
-0x43,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x44,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4d,0x00,0x00,0x00,0xc3,0x00,0x05,0x00,0x28,0x00,0x00,0x00,
-0x64,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x62,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x4c,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,
-0x66,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,
-0xf7,0x00,0x03,0x00,0x68,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
-0xfa,0x00,0x04,0x00,0x66,0x00,0x00,0x00,0x67,0x00,0x00,0x00,
-0x68,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x67,0x00,0x00,0x00,
-0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x18,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,
-0x14,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,
-0x41,0x00,0x06,0x00,0x3a,0x00,0x00,0x00,0x70,0x00,0x00,0x00,
-0x6c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,
-0x3e,0x00,0x03,0x00,0x70,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,
-0xf9,0x00,0x02,0x00,0x68,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,
-0x68,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00,
-
-};
-const uint64_t sum_rows_f32_len = 2112;
diff --git a/ggml/src/vulkan-shaders/CMakeLists.txt b/ggml/src/vulkan-shaders/CMakeLists.txt
new file mode 100644
index 00000000000000..41551e0094d4a5
--- /dev/null
+++ b/ggml/src/vulkan-shaders/CMakeLists.txt
@@ -0,0 +1,5 @@
+
+set(TARGET vulkan-shaders-gen)
+add_executable(${TARGET} vulkan-shaders-gen.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
new file mode 100644
index 00000000000000..fa231c0ec16886
--- /dev/null
+++ b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -0,0 +1,524 @@
+
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <string>
+#include <stdexcept>
+#include <array>
+#include <vector>
+#include <map>
+#include <thread>
+#include <mutex>
+#include <future>
+#include <queue>
+#include <condition_variable>
+#include <cstdio>
+#include <cstring>
+#include <cstdlib>
+#include <sys/stat.h>
+#include <sys/types.h>
+
+#ifdef _WIN32
+    #include <windows.h>
+    #include <direct.h> // For _mkdir on Windows
+#else
+    #include <unistd.h>
+    #include <sys/wait.h>
+    #include <fcntl.h>
+#endif
+
+#define ASYNCIO_CONCURRENCY 64
+
+std::mutex lock;
+std::vector<std::pair<std::string, std::string>> shader_fnames;
+
+std::string GLSLC = "glslc";
+std::string input_dir = "vulkan-shaders";
+std::string output_dir = "/tmp";
+std::string target_hpp = "ggml-vulkan-shaders.hpp";
+std::string target_cpp = "ggml-vulkan-shaders.cpp";
+bool no_clean = false;
+
+const std::vector<std::string> type_names = {
+    "f32",
+    "f16",
+    "q4_0",
+    "q4_1",
+    "q5_0",
+    "q5_1",
+    "q8_0",
+    "q2_k",
+    "q3_k",
+    "q4_k",
+    "q5_k",
+    "q6_k"
+};
+
+void execute_command(const std::string& command, std::string& stdout_str, std::string& stderr_str) {
+#ifdef _WIN32
+    HANDLE stdout_read, stdout_write;
+    HANDLE stderr_read, stderr_write;
+    SECURITY_ATTRIBUTES sa = { sizeof(SECURITY_ATTRIBUTES), NULL, TRUE };
+
+    if (!CreatePipe(&stdout_read, &stdout_write, &sa, 0) ||
+        !SetHandleInformation(stdout_read, HANDLE_FLAG_INHERIT, 0)) {
+        throw std::runtime_error("Failed to create stdout pipe");
+    }
+
+    if (!CreatePipe(&stderr_read, &stderr_write, &sa, 0) ||
+        !SetHandleInformation(stderr_read, HANDLE_FLAG_INHERIT, 0)) {
+        throw std::runtime_error("Failed to create stderr pipe");
+    }
+
+    PROCESS_INFORMATION pi;
+    STARTUPINFOA si = { sizeof(STARTUPINFOA) };
+    si.dwFlags = STARTF_USESTDHANDLES;
+    si.hStdOutput = stdout_write;
+    si.hStdError = stderr_write;
+
+    std::vector<char> cmd(command.begin(), command.end());
+    cmd.push_back('\0');
+
+    if (!CreateProcessA(NULL, cmd.data(), NULL, NULL, TRUE, 0, NULL, NULL, &si, &pi)) {
+        throw std::runtime_error("Failed to create process");
+    }
+
+    CloseHandle(stdout_write);
+    CloseHandle(stderr_write);
+
+    std::array<char, 128> buffer;
+    DWORD bytes_read;
+
+    while (ReadFile(stdout_read, buffer.data(), buffer.size(), &bytes_read, NULL) && bytes_read > 0) {
+        stdout_str.append(buffer.data(), bytes_read);
+    }
+
+    while (ReadFile(stderr_read, buffer.data(), buffer.size(), &bytes_read, NULL) && bytes_read > 0) {
+        stderr_str.append(buffer.data(), bytes_read);
+    }
+
+    CloseHandle(stdout_read);
+    CloseHandle(stderr_read);
+    WaitForSingleObject(pi.hProcess, INFINITE);
+    CloseHandle(pi.hProcess);
+    CloseHandle(pi.hThread);
+#else
+int stdout_pipe[2];
+    int stderr_pipe[2];
+
+    if (pipe(stdout_pipe) != 0 || pipe(stderr_pipe) != 0) {
+        throw std::runtime_error("Failed to create pipes");
+    }
+
+    pid_t pid = fork();
+    if (pid < 0) {
+        throw std::runtime_error("Failed to fork process");
+    }
+
+    if (pid == 0) {
+        close(stdout_pipe[0]);
+        close(stderr_pipe[0]);
+        dup2(stdout_pipe[1], STDOUT_FILENO);
+        dup2(stderr_pipe[1], STDERR_FILENO);
+        close(stdout_pipe[1]);
+        close(stderr_pipe[1]);
+        execl("/bin/sh", "sh", "-c", command.c_str(), (char*) nullptr);
+        _exit(EXIT_FAILURE);
+    } else {
+        close(stdout_pipe[1]);
+        close(stderr_pipe[1]);
+
+        std::array<char, 128> buffer;
+        ssize_t bytes_read;
+
+        while ((bytes_read = read(stdout_pipe[0], buffer.data(), buffer.size())) > 0) {
+            stdout_str.append(buffer.data(), bytes_read);
+        }
+
+        while ((bytes_read = read(stderr_pipe[0], buffer.data(), buffer.size())) > 0) {
+            stderr_str.append(buffer.data(), bytes_read);
+        }
+
+        close(stdout_pipe[0]);
+        close(stderr_pipe[0]);
+        waitpid(pid, nullptr, 0);
+    }
+#endif
+}
+
+bool directory_exists(const std::string& path) {
+    struct stat info;
+    if (stat(path.c_str(), &info) != 0) {
+        return false; // Path doesn't exist or can't be accessed
+    }
+    return (info.st_mode & S_IFDIR) != 0; // Check if it is a directory
+}
+
+bool create_directory(const std::string& path) {
+#ifdef _WIN32
+    return _mkdir(path.c_str()) == 0 || errno == EEXIST; // EEXIST means the directory already exists
+#else
+    return mkdir(path.c_str(), 0755) == 0 || errno == EEXIST; // 0755 is the directory permissions
+#endif
+}
+
+std::string to_uppercase(const std::string& input) {
+    std::string result = input;
+    for (char& c : result) {
+        c = std::toupper(c);
+    }
+    return result;
+}
+
+bool string_ends_with(const std::string& str, const std::string& suffix) {
+    if (suffix.size() > str.size()) {
+        return false;
+    }
+    return std::equal(suffix.rbegin(), suffix.rend(), str.rbegin());
+}
+
+#ifdef _WIN32
+    static const char path_separator = '\\';
+#else
+    static const char path_separator = '/';
+#endif
+
+std::string join_paths(const std::string& path1, const std::string& path2) {
+    return path1 + path_separator + path2;
+}
+
+std::string basename(const std::string &path) {
+    return path.substr(path.find_last_of("/\\") + 1);
+}
+
+void string_to_spv(const std::string& _name, const std::string& in_fname, const std::map<std::string, std::string>& defines, bool fp16 = true) {
+    std::string name = _name + (fp16 ? "" : "_fp32");
+    std::string out_fname = join_paths(output_dir, name + ".spv");
+    std::string in_path = join_paths(input_dir, in_fname);
+
+    std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", in_path, "-o", out_fname};
+    for (const auto& define : defines) {
+        cmd.push_back("-D" + define.first + "=" + define.second);
+    }
+
+    std::string command;
+    for (const auto& part : cmd) {
+        command += part + " ";
+    }
+
+    std::string stdout_str, stderr_str;
+    try {
+        // std::cout << "Executing command: ";
+        // for (const auto& part : cmd) {
+        //     std::cout << part << " ";
+        // }
+        // std::cout << std::endl;
+
+        execute_command(command, stdout_str, stderr_str);
+        if (!stderr_str.empty()) {
+            std::cerr << "cannot compile " << name << "\n\n" << command << "\n\n" << stderr_str << std::endl;
+            return;
+        }
+
+        std::lock_guard<std::mutex> guard(lock);
+        shader_fnames.push_back(std::make_pair(name, out_fname));
+    } catch (const std::exception& e) {
+        std::cerr << "Error executing command for " << name << ": " << e.what() << std::endl;
+    }
+}
+
+std::map<std::string, std::string> merge_maps(const std::map<std::string, std::string>& a, const std::map<std::string, std::string>& b) {
+    std::map<std::string, std::string> result = a;
+    result.insert(b.begin(), b.end());
+    return result;
+}
+
+void matmul_shaders(std::vector<std::future<void>>& tasks, bool fp16, bool matmul_id) {
+    std::string load_vec = fp16 ? "8" : "4";
+    std::string aligned_b_type_f32 = fp16 ? "mat2x4" : "vec4";
+    std::string aligned_b_type_f16 = fp16 ? "f16mat2x4" : "f16vec4";
+
+    std::map<std::string, std::string> base_dict = {{"FLOAT_TYPE", fp16 ? "float16_t" : "float"}};
+    std::string shader_name = "matmul";
+
+    if (matmul_id) {
+        base_dict["MUL_MAT_ID"] = "1";
+        shader_name = "matmul_id";
+    }
+
+    if (fp16) {
+        base_dict["FLOAT16"] = "1";
+    }
+
+    // Shaders with f16 B_TYPE
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv(shader_name + "_f32_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16);
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv(shader_name + "_f32_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16);
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv(shader_name + "_f16", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}), fp16);
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv(shader_name + "_f16_aligned", "mul_mm.comp", merge_maps(base_dict, {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"D_TYPE", "float"}}), fp16);
+    }));
+
+    for (const auto& tname : type_names) {
+        std::string data_a_key = "DATA_A_" + to_uppercase(tname);
+        std::string load_vec_a = (tname == "f32" || tname == "f16") ? load_vec : "2";
+        tasks.push_back(std::async(std::launch::async, [=] {
+            string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16);
+        }));
+        tasks.push_back(std::async(std::launch::async, [=] {
+            string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "2"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16);
+        }));
+    }
+}
+
+void process_shaders(std::vector<std::future<void>>& tasks) {
+    std::cout << "ggml_vulkan: Generating and compiling shaders to SPIR-V" << std::endl;
+    std::map<std::string, std::string> base_dict = {{"FLOAT_TYPE", "float"}};
+
+    for (const auto& fp16 : {false, true}) {
+        matmul_shaders(tasks, fp16, false);
+        matmul_shaders(tasks, fp16, true);
+    }
+
+    for (const auto& tname : type_names) {
+        // mul mat vec
+        std::string data_a_key = "DATA_A_" + to_uppercase(tname);
+        std::string shader = (string_ends_with(tname, "_k")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp";
+
+        tasks.push_back(std::async(std::launch::async, [=] {
+            string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+        }));
+        tasks.push_back(std::async(std::launch::async, [=] {
+            string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
+        }));
+
+        tasks.push_back(std::async(std::launch::async, [=] {
+            string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+        }));
+
+        // Dequant shaders
+        if (tname != "f16") {
+            tasks.push_back(std::async(std::launch::async, [=] {
+                string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}}));
+            }));
+        }
+
+        if (!string_ends_with(tname, "_k")) {
+            shader = (tname == "f32" || tname == "f16") ? "get_rows.comp" : "get_rows_quant.comp";
+
+            if (tname == "f16") {
+                tasks.push_back(std::async(std::launch::async, [=] {
+                    string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}});
+                }));
+            } else {
+                tasks.push_back(std::async(std::launch::async, [=] {
+                    string_to_spv("get_rows_" + tname, shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}});
+                }));
+            }
+            tasks.push_back(std::async(std::launch::async, [=] {
+                string_to_spv("get_rows_" + tname + "_f32", shader, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}});
+            }));
+        }
+    }
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
+    // Norms
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("add_f32", "add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("mul_f32", "mul.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("div_f32", "div.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("silu_f32", "silu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("relu_f32", "relu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("soft_max_f32", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    }));
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [] {
+        string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}});
+    }));
+
+    tasks.push_back(std::async(std::launch::async, [=] {
+        string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
+    }));
+}
+
+void write_output_files() {
+    FILE* hdr = fopen(target_hpp.c_str(), "w");
+    FILE* src = fopen(target_cpp.c_str(), "w");
+
+    fprintf(hdr, "#include <cstdint>\n\n");
+    fprintf(src, "#include \"%s\"\n\n", basename(target_hpp).c_str());
+
+    for (const auto& pair : shader_fnames) {
+        const std::string& name = pair.first;
+        const std::string& path = pair.second;
+        FILE* spv = fopen(path.c_str(), "rb");
+        if (!spv) {
+            std::cerr << "Error opening SPIR-V file: " << path << "\n";
+            continue;
+        }
+
+        fseek(spv, 0, SEEK_END);
+        size_t size = ftell(spv);
+        fseek(spv, 0, SEEK_SET);
+
+        std::vector<unsigned char> data(size);
+        size_t read_size = fread(data.data(), 1, size, spv);
+        fclose(spv);
+        if (read_size != size) {
+            std::cerr << "Error reading SPIR-V file: " << path << "\n";
+            continue;
+        }
+
+        fprintf(hdr, "extern unsigned char %s_data[%zu];\n", name.c_str(), size);
+        fprintf(hdr, "const uint64_t %s_len = %zu;\n\n", name.c_str(), size);
+
+        fprintf(src, "unsigned char %s_data[%zu] = {\n", name.c_str(), size);
+        for (size_t i = 0; i < size; ++i) {
+            fprintf(src, "0x%02x,", data[i]);
+            if ((i + 1) % 12 == 0) fprintf(src, "\n");
+        }
+        fprintf(src, "\n};\n\n");
+
+        if (!no_clean) {
+            std::remove(path.c_str());
+        }
+    }
+
+    fclose(hdr);
+    fclose(src);
+}
+
+int main(int argc, char** argv) {
+    std::map<std::string, std::string> args;
+    for (int i = 1; i < argc; i += 2) {
+        if (i + 1 < argc) {
+            args[argv[i]] = argv[i + 1];
+        }
+    }
+
+    if (args.find("--glslc") != args.end()) {
+        GLSLC = args["--glslc"]; // Path to glslc
+    }
+    if (args.find("--input-dir") != args.end()) {
+        input_dir = args["--input-dir"]; // Directory containing shader sources
+    }
+    if (args.find("--output-dir") != args.end()) {
+        output_dir = args["--output-dir"]; // Directory for containing SPIR-V output
+    }
+    if (args.find("--target-hpp") != args.end()) {
+        target_hpp = args["--target-hpp"]; // Path to generated header file
+    }
+    if (args.find("--target-cpp") != args.end()) {
+        target_cpp = args["--target-cpp"]; // Path to generated cpp file
+    }
+    if (args.find("--no-clean") != args.end()) {
+        no_clean = true; // Keep temporary SPIR-V files in output-dir after build
+    }
+
+    if (!directory_exists(input_dir)) {
+        std::cerr << "\"" << input_dir << "\" must be a valid directory containing shader sources" << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    if (!directory_exists(output_dir)) {
+        if (!create_directory(output_dir)) {
+            std::cerr << "Error creating output directory: " << output_dir << "\n";
+            return EXIT_FAILURE;
+        }
+    }
+
+    std::vector<std::future<void>> tasks;
+    process_shaders(tasks);
+
+    for (auto& task : tasks) {
+        task.get();
+    }
+
+    write_output_files();
+
+    return EXIT_SUCCESS;
+}

From fa79495bb4897953a75607addd9d2cdd2ec63222 Mon Sep 17 00:00:00 2001
From: compilade <git@compilade.net>
Date: Sat, 13 Jul 2024 23:35:10 -0400
Subject: [PATCH 046/125] llama : fix pre-tokenization of non-special added
 tokens (#8228)

* llama : fix mpt and olmo pre-tokenizer

* llama : pre-tokenize non-special user-defined tokens first

* llama : fix detection of control-like user-defined tokens

* convert_hf : identify which user-defined tokens are control tokens

Only used in _set_vocab_gpt2() for now.

* convert_hf : identify more added control tokens for SPM tokenziers

This makes Gemma and Gemma-2 tokenize pretty much EVERYTHING correctly,
including HTML tags and consecutive spaces,
but it unfortunately requires model re-conversion.

There seems to be a weird behavior of the HF tokenizer for Gemma,
which prefers to use the 16-space token over more lengthy space tokens,
while using the SentencePiece tokenizer does not do this.
(the implementation in llama.cpp has the same behavior as SentencePiece)

* llama : fix wrong pre-tokenization of byte tokens

* llama : fix Viking pre-tokenizer regex

The order was previously wrong, which caused errors in some tests.

* llama : fix command-r detokenization

* convert_hf : reduce usages of the UNKNOWN token type

* llama : add UNKNOWN tokens in the special tokens cache

* convert_hf : reduce usages of UNKNOWN for InternLM2

This makes the changes from #8321 more consistent
with the other changes made here.

* test-tokenizer-random : reduce potential confilcts with #8379

* test-tokenizer-random : add a failing edge case for falcon
---
 convert_hf_to_gguf.py          | 112 +++++++++++++++++++++------------
 src/llama.cpp                  |  29 +++++----
 tests/test-tokenizer-0.cpp     |   4 +-
 tests/test-tokenizer-random.py |   7 ++-
 4 files changed, 91 insertions(+), 61 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index cf930be17a6e07..af82cd6cd05c99 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -373,6 +373,29 @@ def from_model_architecture(cls, arch: str) -> type[Model]:
         except KeyError:
             raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
 
+    def does_token_look_special(self, token: str | bytes) -> bool:
+        if isinstance(token, (bytes, bytearray)):
+            token_text = token.decode(encoding="utf-8")
+        elif isinstance(token, memoryview):
+            token_text = token.tobytes().decode(encoding="utf-8")
+        else:
+            token_text = token
+
+        # Some models mark some added tokens which ought to be control tokens as not special.
+        # (e.g. command-r, command-r-plus, deepseek-coder, gemma{,-2})
+        seems_special = token_text in (
+            "<pad>",  # deepseek-coder
+            "<mask>", "<2mass>", "[@BOS@]",  # gemma{,-2}
+        )
+
+        seems_special = seems_special or (token_text.startswith("<|") and token_text.endswith("|>"))
+        seems_special = seems_special or (token_text.startswith("<｜") and token_text.endswith("｜>"))  # deepseek-coder
+
+        # TODO: should these be marked as UNUSED instead? (maybe not)
+        seems_special = seems_special or (token_text.startswith("<unused") and token_text.endswith(">"))  # gemma{,-2}
+
+        return seems_special
+
     # used for GPT-2 BPE and WordPiece vocabs
     def get_vocab_base(self) -> tuple[list[str], list[int], str]:
         tokens: list[str] = []
@@ -391,16 +414,18 @@ def get_vocab_base(self) -> tuple[list[str], list[int], str]:
         for i in range(vocab_size):
             if i not in reverse_vocab:
                 tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.USER_DEFINED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                if tokenizer.added_tokens_decoder[i].special:
-                    toktypes.append(gguf.TokenType.CONTROL)
-                else:
-                    toktypes.append(gguf.TokenType.USER_DEFINED)
+                toktypes.append(gguf.TokenType.UNUSED)
             else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
+                token: str = reverse_vocab[i]
+                if token in added_vocab:
+                    if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
+                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+                tokens.append(token)
 
         return tokens, toktypes, tokpre
 
@@ -559,7 +584,7 @@ def _set_vocab_qwen(self):
         for i in range(vocab_size):
             if i not in reverse_vocab:
                 tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.USER_DEFINED)
+                toktypes.append(gguf.TokenType.UNUSED)
             elif reverse_vocab[i] in added_vocab:
                 tokens.append(reverse_vocab[i])
                 toktypes.append(gguf.TokenType.CONTROL)
@@ -609,7 +634,7 @@ def _create_vocab_sentencepiece(self):
 
         tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
         scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
 
         for token_id in range(tokenizer.vocab_size()):
             piece = tokenizer.IdToPiece(token_id)
@@ -644,6 +669,25 @@ def _create_vocab_sentencepiece(self):
                     scores[token_id] = -1000.0
                     toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
 
+        tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
+        if tokenizer_config_file.is_file():
+            with open(tokenizer_config_file, "r", encoding="utf-8") as f:
+                tokenizer_config_json = json.load(f)
+                added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {})
+                for token_id, token_data in added_tokens_decoder.items():
+                    token_id = int(token_id)
+                    token: str = token_data["content"]
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
+                        assert tokens[token_id] == token.encode("utf-8")
+                    if token_data.get("special") or self.does_token_look_special(token):
+                        toktypes[token_id] = SentencePieceTokenTypes.CONTROL
+                    else:
+                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
+                        toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+                    scores[token_id] = -1000.0
+                    tokens[token_id] = token.encode("utf-8")
+
         if vocab_size > len(tokens):
             pad_count = vocab_size - len(tokens)
             logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
@@ -1266,7 +1310,7 @@ def set_vocab(self):
         if (self.dir_model / "tokenizer.json").is_file():
             self._set_vocab_gpt2()
         else:
-            # StableLM 2 1.6B uses a vocab in a similar format to Qwen's vocab
+            # StableLM 2 1.6B used to have a vocab in a similar format to Qwen's vocab
             self._set_vocab_qwen()
 
     def set_gguf_parameters(self):
@@ -1578,7 +1622,6 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
 
         self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
-        self.gguf_writer.add_file_type(self.ftype)
 
         self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
         self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
@@ -1872,7 +1915,7 @@ def set_vocab(self):
 
         tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
         scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
 
         for token_id in range(tokenizer.vocab_size()):
 
@@ -1917,7 +1960,7 @@ def set_vocab(self):
                 for token_id, foken_data in added_tokens_decoder.items():
                     token_id = int(token_id)
                     token = foken_data["content"].encode("utf-8")
-                    if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
                         assert tokens[token_id] == token
                     tokens[token_id] = token
                     scores[token_id] = -1000.0
@@ -1933,7 +1976,7 @@ def set_vocab(self):
                 for foken_data in added_tokens:
                     token_id = int(foken_data["id"])
                     token = foken_data["content"].encode("utf-8")
-                    if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
                         assert tokens[token_id] == token
                     tokens[token_id] = token
                     scores[token_id] = -1000.0
@@ -2145,7 +2188,7 @@ def set_vocab(self):
                 toktype = SentencePieceTokenTypes.BYTE
             # take care of ununsed raw token
             if piece.startswith('[UNUSED'):
-                toktype = SentencePieceTokenTypes.UNKNOWN
+                toktype = SentencePieceTokenTypes.UNUSED
 
             tokens.append(text)
             scores.append(score)
@@ -2175,7 +2218,7 @@ def set_vocab(self):
                     if token == chat_eos_token:
                         chat_eos_token_id = token_id
                     token = token.encode("utf-8")
-                    if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
                         assert(tokens[token_id] == token)
                     tokens[token_id] = token
                     scores[token_id] = -1000.0
@@ -2194,7 +2237,7 @@ def set_vocab(self):
                     if token == chat_eos_token:
                         chat_eos_token_id = token_id
                     token = token.encode("utf-8")
-                    if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN:
+                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
                         assert(tokens[token_id] == token)
                     tokens[token_id] = token
                     scores[token_id] = -1000.0
@@ -2434,19 +2477,7 @@ class Gemma2Model(Model):
     model_arch = gguf.MODEL_ARCH.GEMMA2
 
     def set_vocab(self):
-        tokens, scores, toktypes = self._create_vocab_sentencepiece()
-        # hack: This is required so that we can properly use start/end-of-turn for chat template
-        for i in range(108):
-            # including <unusedX>, <start_of_turn>, <end_of_turn>
-            toktypes[i] = SentencePieceTokenTypes.CONTROL
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
+        self._set_vocab_sentencepiece()
 
         self.gguf_writer.add_add_space_prefix(False)
 
@@ -2770,7 +2801,7 @@ def set_vocab(self):
 
         tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
         scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
 
         for token_id in range(tokenizer.vocab_size()):
 
@@ -3025,7 +3056,7 @@ def set_vocab(self):
 
         tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
         scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
 
         for token_id in range(tokenizer.vocab_size()):
             piece = tokenizer.IdToPiece(token_id)
@@ -3243,15 +3274,14 @@ def set_vocab_chatglm3(self):
             if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():
                 score = tokenizer.tokenizer.sp_model.get_score(token_id)
 
-            if len(piece) == 0:
-                text = f"[PAD{token_id}]".encode("utf-8")
-
             if token_id >= tokenizer.tokenizer.sp_model.vocab_size():
                 if piece in special_tokens:
-                    # show special tokens in prompt
-                    toktype = SentencePieceTokenTypes.USER_DEFINED
+                    toktype = SentencePieceTokenTypes.CONTROL
+                elif len(piece) == 0:
+                    text = f"[PAD{token_id}]".encode("utf-8")
+                    toktype = SentencePieceTokenTypes.UNUSED
                 else:
-                    toktype = SentencePieceTokenTypes.UNKNOWN
+                    toktype = SentencePieceTokenTypes.USER_DEFINED
                 tokens.append(text)
                 scores.append(score)
                 toktypes.append(toktype)
@@ -3340,7 +3370,7 @@ def set_vocab(self):
         for i in range(vocab_size):
             if i not in reverse_vocab:
                 tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.USER_DEFINED)
+                toktypes.append(gguf.TokenType.UNUSED)
             elif reverse_vocab[i] in added_vocab:
                 tokens.append(reverse_vocab[i])
                 if tokenizer.added_tokens_decoder[i].special:
diff --git a/src/llama.cpp b/src/llama.cpp
index 59b76a6d80cdf8..77d34dca280fc5 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -5419,6 +5419,7 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "command-r") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
+                vocab.tokenizer_clean_spaces = false;
             } else if (
                 tokenizer_pre == "qwen2") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
@@ -5652,7 +5653,7 @@ static void llm_load_vocab(
     // build special tokens cache
     {
         for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
-            if (!(vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL)) {
+            if (vocab.id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
                 vocab.cache_special_tokens.push_back(id);
             }
         }
@@ -15411,17 +15412,6 @@ struct llm_tokenizer_bpe {
                     "[0-9][0-9][0-9]",
                 };
                 break;
-            case LLAMA_VOCAB_PRE_TYPE_MPT:
-                // TODO: MPT pre-tokenization regexes are unknown
-                //       the following are close, but not exact. run the following:
-                //       ./bin/test-tokenizer-0 ../models/ggml-vocab-mpt.gguf
-                GGML_ASSERT("MPT pre-tokenization regexes are unknown - fixes needed");
-                regex_exprs = {
-                    "\\s?\\p{L}+",
-                    "\\s?\\p{P}+",
-                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                };
-                break;
             case LLAMA_VOCAB_PRE_TYPE_STARCODER:
             case LLAMA_VOCAB_PRE_TYPE_REFACT:
             case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
@@ -15431,6 +15421,7 @@ struct llm_tokenizer_bpe {
                 };
                 break;
             case LLAMA_VOCAB_PRE_TYPE_GPT2:
+            case LLAMA_VOCAB_PRE_TYPE_MPT:
             case LLAMA_VOCAB_PRE_TYPE_OLMO:
             case LLAMA_VOCAB_PRE_TYPE_JAIS:
                 regex_exprs = {
@@ -15457,8 +15448,8 @@ struct llm_tokenizer_bpe {
                 break;
             case LLAMA_VOCAB_PRE_TYPE_VIKING:
                 regex_exprs = {
-                    "\\p{N}",
                     " ?[^(\\s|.,!?…。，、।۔،)]+",
+                    "\\p{N}",
                 };
                 break;
             default:
@@ -16178,12 +16169,20 @@ struct fragment_buffer_variant {
 
 // #define PRETOKENIZERDEBUG
 
-static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
+static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) {
     // for each special token
     for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
         const auto & data = vocab.id_to_token[special_id];
         const auto & special_token = data.text;
 
+        if (!parse_special && (data.attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_UNKNOWN))) {
+            // Ignore control and unknown tokens when parse_special == false
+            continue;
+            // User-defined tokens are still pre-tokenized before everything else
+            // ref: https://github.com/huggingface/tokenizers/blob/fdd26ba9a3f0c133427aab0423888cbde91362d7/tokenizers/src/tokenizer/mod.rs#L726
+            // This is mostly relevant for neox-style tokenizers (mpt, olmo, stablelm, etc.)
+        }
+
         // for each text fragment
         std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
         while (it != buffer.end()) {
@@ -16296,7 +16295,7 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
 
     if (!raw_text.empty()) {
         fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
-        if (parse_special) tokenizer_st_partition(vocab, fragment_buffer);
+        tokenizer_st_partition(vocab, fragment_buffer, parse_special);
     }
 
     switch (vocab.type) {
diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp
index 1f04b6f34ad7e4..d3d21331bfd3d1 100644
--- a/tests/test-tokenizer-0.cpp
+++ b/tests/test-tokenizer-0.cpp
@@ -195,7 +195,7 @@ int main(int argc, char **argv) {
     const bool add_special = false;
 
     for (const auto & test_kv : k_tests) {
-        const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, true);
+        const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, add_special, false);
 
         printf("\n");
         printf("src: '%s'\n", test_kv.first.c_str());
@@ -253,7 +253,7 @@ int main(int argc, char **argv) {
         {
             const auto t_start = ggml_time_us();
 
-            res = llama_tokenize(ctx, text, add_special, true);
+            res = llama_tokenize(ctx, text, add_special, false);
 
             const auto t_end = ggml_time_us();
 
diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py
index c50a8ca32f6573..9ebe6c89185a32 100644
--- a/tests/test-tokenizer-random.py
+++ b/tests/test-tokenizer-random.py
@@ -20,7 +20,7 @@
 from typing_extensions import Buffer
 
 import cffi
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, PreTrainedTokenizer
 
 
 logger = logging.getLogger("test-tokenizer-random")
@@ -129,7 +129,7 @@ def decode(self, ids: list[int]) -> str:
 class TokenizerGroundtruth (Tokenizer):
 
     def __init__(self, dir_tokenizer: str):
-        self.model = AutoTokenizer.from_pretrained(dir_tokenizer)
+        self.model: PreTrainedTokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
         # guess BOS and EOS
         ids = self.encode("a")
         assert 1 <= len(ids) <= 3
@@ -143,7 +143,7 @@ def __init__(self, dir_tokenizer: str):
         self.vocab = list(sorted(self.vocab))
         # tokens and lists
         self.special_tokens = list(self.model.all_special_tokens)
-        self.added_tokens   = list(self.model.added_tokens_encoder)
+        self.added_tokens   = self.model.batch_decode(self.model.added_tokens_encoder.values(), skip_special_tokens=False)
         self.bos_token = self.model.bos_token
         self.eos_token = self.model.eos_token
 
@@ -232,6 +232,7 @@ def generator_custom_text_edge_cases() -> Iterator[str]:
         'a\na',            # bert fail
         '"`',              # falcon
         ' \u2e4e',         # falcon
+        '\n\x0b  ',        # falcon
         'a\xa0\xa0\x00b',  # jina-v2-es
         'one <mask>',      # jina-v2-es  <mask> lstrip=true
         'a </s> b',        # rstrip phi-3

From e236528e7628a0e59751eee9addf21fc3c33d376 Mon Sep 17 00:00:00 2001
From: Brian <mofosyne@gmail.com>
Date: Sun, 14 Jul 2024 16:47:14 +1000
Subject: [PATCH 047/125] gguf_hash.py: Add sha256 (#8470)

* gguf_hash.py: Add sha256

* gguf_hash.py: rename string UUIDv5 --> uuid

* Apply suggestions from code review

Co-authored-by: compilade <git@compilade.net>

---------

Co-authored-by: compilade <git@compilade.net>
---
 gguf-py/scripts/gguf_hash.py | 27 +++++++++++++++++++--------
 1 file changed, 19 insertions(+), 8 deletions(-)

diff --git a/gguf-py/scripts/gguf_hash.py b/gguf-py/scripts/gguf_hash.py
index 770b79a93471c8..ee34d09bfe7ef6 100755
--- a/gguf-py/scripts/gguf_hash.py
+++ b/gguf-py/scripts/gguf_hash.py
@@ -27,8 +27,9 @@
 
 # For more information about what field.parts and field.data represent,
 # please see the comments in the modify_gguf.py example.
-def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar) -> None:
+def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar: bool, no_layer: bool) -> None:
     sha1 = hashlib.sha1()
+    sha256 = hashlib.sha256()
     uuidv5_sha1 = hashlib.sha1()
     uuidv5_sha1.update(UUID_NAMESPACE_LLAMA_CPP.bytes)
 
@@ -50,7 +51,7 @@ def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar) -> None:
     bar = tqdm(desc="Hashing", total=total_weights, unit="weights", unit_scale=True, disable=disable_progress_bar)
 
     # Hashing Process
-    for n, tensor in enumerate(reader.tensors, 1):
+    for tensor in reader.tensors:
 
         # We don't need these
         if tensor.name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
@@ -62,29 +63,39 @@ def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar) -> None:
             sum_weights_in_tensor *= dim
         bar.update(sum_weights_in_tensor)
 
-        sha1_layer = hashlib.sha1()
-        sha1_layer.update(tensor.data.data)
+        if not no_layer:
+
+            sha1_layer = hashlib.sha1()
+            sha1_layer.update(tensor.data.data)
+            print("sha1      {0}  {1}:{2}".format(sha1_layer.hexdigest(), filename, tensor.name)) # noqa: NP100
+
+            sha256_layer = hashlib.sha256()
+            sha256_layer.update(tensor.data.data)
+            print("sha256    {0}  {1}:{2}".format(sha256_layer.hexdigest(), filename, tensor.name)) # noqa: NP100
+
         sha1.update(tensor.data.data)
+        sha256.update(tensor.data.data)
         uuidv5_sha1.update(tensor.data.data)
-        print("sha1    {0}  {1}:{2}".format(sha1_layer.hexdigest(), filename, tensor.name)) # noqa: NP100
 
     # Flush Hash Progress Bar
     bar.close()
 
     # Display Hash Output
-    print("sha1    {0}  {1}".format(sha1.hexdigest(), filename)) # noqa: NP100
-    print("UUIDv5  {0}  {1}".format(uuid.UUID(bytes=uuidv5_sha1.digest()[:16], version=5), filename)) # noqa: NP100
+    print("sha1      {0}  {1}".format(sha1.hexdigest(), filename)) # noqa: NP100
+    print("sha256    {0}  {1}".format(sha256.hexdigest(), filename)) # noqa: NP100
+    print("uuid      {0}  {1}".format(uuid.UUID(bytes=uuidv5_sha1.digest()[:16], version=5), filename)) # noqa: NP100
 
 
 def main() -> None:
     parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
     parser.add_argument("model",         type=str,            help="GGUF format model filename")
+    parser.add_argument("--no-layer",    action="store_true", help="exclude per layer hash")
     parser.add_argument("--verbose",     action="store_true", help="increase output verbosity")
     parser.add_argument("--progressbar", action="store_true", help="enable progressbar")
     args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
     logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
     reader = GGUFReader(args.model, 'r')
-    gguf_hash(reader, args.model, not args.progressbar)
+    gguf_hash(reader, args.model, not args.progressbar, args.no_layer)
 
 
 if __name__ == '__main__':

From 73cf442e7bc9baca7b3e213b261551812f1676c9 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 14 Jul 2024 14:05:09 +0300
Subject: [PATCH 048/125] llama : fix Gemma-2 Query scaling factors (#8473)

* 9B - query_pre_attn_scalar = 256 not 224

See https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e

Gemma 9b should use 256 and not 224 (self.config.hidden_size // self.config.num_attention_heads)

* llama : fix Gemma-2 Query scaling factor

ggml-ci

---------

Co-authored-by: Daniel Han <danielhanchen@gmail.com>
---
 convert_hf_to_gguf.py | 5 -----
 src/llama.cpp         | 7 ++++++-
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index af82cd6cd05c99..42dace219f20fc 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2504,11 +2504,6 @@ def set_gguf_parameters(self):
         )
         self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
 
-        # sanity check
-        attn_scalar = self.hparams["query_pre_attn_scalar"]
-        if attn_scalar != hparams["hidden_size"] / hparams["num_attention_heads"]:
-            raise ValueError("query_pre_attn_scalar must be equal to n_embd / n_head")
-
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
 
diff --git a/src/llama.cpp b/src/llama.cpp
index 77d34dca280fc5..400a4232beeb09 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -11680,7 +11680,12 @@ struct llm_build_context {
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il);
 
-                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head)));
+                // ref: https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
+                switch (model.type) {
+                    case e_model::MODEL_9B:  Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));   break;
+                    case e_model::MODEL_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
+                    default: GGML_ASSERT(false);
+                };
                 cb(Qcur, "Qcur_scaled", il);
 
                 Kcur = ggml_rope_ext(

From aaab2419eaa17ab3aa38f4ba49c7eea406999e99 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 14 Jul 2024 18:54:02 +0300
Subject: [PATCH 049/125] flake.lock: Update (#8475)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Flake lock file updates:

• Updated input 'nixpkgs':
    'github:NixOS/nixpkgs/9f4128e00b0ae8ec65918efeba59db998750ead6?narHash=sha256-rwz8NJZV%2B387rnWpTYcXaRNvzUSnnF9aHONoJIYmiUQ%3D' (2024-07-03)
  → 'github:NixOS/nixpkgs/7e7c39ea35c5cdd002cd4588b03a3fb9ece6fad9?narHash=sha256-EYekUHJE2gxeo2pM/zM9Wlqw1Uw2XTJXOSAO79ksc4Y%3D' (2024-07-12)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
---
 flake.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/flake.lock b/flake.lock
index 5001fbf912c5ce..3e5125dde3b14d 100644
--- a/flake.lock
+++ b/flake.lock
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1720031269,
-        "narHash": "sha256-rwz8NJZV+387rnWpTYcXaRNvzUSnnF9aHONoJIYmiUQ=",
+        "lastModified": 1720768451,
+        "narHash": "sha256-EYekUHJE2gxeo2pM/zM9Wlqw1Uw2XTJXOSAO79ksc4Y=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "9f4128e00b0ae8ec65918efeba59db998750ead6",
+        "rev": "7e7c39ea35c5cdd002cd4588b03a3fb9ece6fad9",
         "type": "github"
       },
       "original": {

From 090fca7a073efe9ad3dd2b9ac12491a4f20ac957 Mon Sep 17 00:00:00 2001
From: compilade <git@compilade.net>
Date: Sun, 14 Jul 2024 19:51:21 -0400
Subject: [PATCH 050/125] pydantic : replace uses of __annotations__ with
 get_type_hints (#8474)

* pydantic : replace uses of __annotations__ with get_type_hints

* pydantic : fix Python 3.9 and 3.10 support
---
 examples/pydantic_models_to_grammar.py        | 77 +++++++++++--------
 .../pydantic_models_to_grammar_examples.py    |  2 +
 requirements/requirements-pydantic.txt        |  1 +
 3 files changed, 46 insertions(+), 34 deletions(-)

diff --git a/examples/pydantic_models_to_grammar.py b/examples/pydantic_models_to_grammar.py
index d8145710ce250e..93e5dcb6c3855a 100644
--- a/examples/pydantic_models_to_grammar.py
+++ b/examples/pydantic_models_to_grammar.py
@@ -6,7 +6,7 @@
 from copy import copy
 from enum import Enum
 from inspect import getdoc, isclass
-from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin
+from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin, get_type_hints
 
 from docstring_parser import parse
 from pydantic import BaseModel, create_model
@@ -53,35 +53,38 @@ class PydanticDataType(Enum):
 
 
 def map_pydantic_type_to_gbnf(pydantic_type: type[Any]) -> str:
-    if isclass(pydantic_type) and issubclass(pydantic_type, str):
+    origin_type = get_origin(pydantic_type)
+    origin_type = pydantic_type if origin_type is None else origin_type
+
+    if isclass(origin_type) and issubclass(origin_type, str):
         return PydanticDataType.STRING.value
-    elif isclass(pydantic_type) and issubclass(pydantic_type, bool):
+    elif isclass(origin_type) and issubclass(origin_type, bool):
         return PydanticDataType.BOOLEAN.value
-    elif isclass(pydantic_type) and issubclass(pydantic_type, int):
+    elif isclass(origin_type) and issubclass(origin_type, int):
         return PydanticDataType.INTEGER.value
-    elif isclass(pydantic_type) and issubclass(pydantic_type, float):
+    elif isclass(origin_type) and issubclass(origin_type, float):
         return PydanticDataType.FLOAT.value
-    elif isclass(pydantic_type) and issubclass(pydantic_type, Enum):
+    elif isclass(origin_type) and issubclass(origin_type, Enum):
         return PydanticDataType.ENUM.value
 
-    elif isclass(pydantic_type) and issubclass(pydantic_type, BaseModel):
-        return format_model_and_field_name(pydantic_type.__name__)
-    elif get_origin(pydantic_type) is list:
+    elif isclass(origin_type) and issubclass(origin_type, BaseModel):
+        return format_model_and_field_name(origin_type.__name__)
+    elif origin_type is list:
         element_type = get_args(pydantic_type)[0]
         return f"{map_pydantic_type_to_gbnf(element_type)}-list"
-    elif get_origin(pydantic_type) is set:
+    elif origin_type is set:
         element_type = get_args(pydantic_type)[0]
         return f"{map_pydantic_type_to_gbnf(element_type)}-set"
-    elif get_origin(pydantic_type) is Union:
+    elif origin_type is Union:
         union_types = get_args(pydantic_type)
         union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types]
         return f"union-{'-or-'.join(union_rules)}"
-    elif get_origin(pydantic_type) is Optional:
+    elif origin_type is Optional:
         element_type = get_args(pydantic_type)[0]
         return f"optional-{map_pydantic_type_to_gbnf(element_type)}"
-    elif isclass(pydantic_type):
-        return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(pydantic_type.__name__)}"
-    elif get_origin(pydantic_type) is dict:
+    elif isclass(origin_type):
+        return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(origin_type.__name__)}"
+    elif origin_type is dict:
         key_type, value_type = get_args(pydantic_type)
         return f"custom-dict-key-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(key_type))}-value-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(value_type))}"
     else:
@@ -118,7 +121,7 @@ def get_members_structure(cls, rule_name):
         # Modify this comprehension
         members = [
             f'  "\\"{name}\\"" ":"  {map_pydantic_type_to_gbnf(param_type)}'
-            for name, param_type in cls.__annotations__.items()
+            for name, param_type in get_type_hints(cls).items()
             if name != "self"
         ]
 
@@ -297,17 +300,20 @@ def generate_gbnf_rule_for_type(
     field_name = format_model_and_field_name(field_name)
     gbnf_type = map_pydantic_type_to_gbnf(field_type)
 
-    if isclass(field_type) and issubclass(field_type, BaseModel):
+    origin_type = get_origin(field_type)
+    origin_type = field_type if origin_type is None else origin_type
+
+    if isclass(origin_type) and issubclass(origin_type, BaseModel):
         nested_model_name = format_model_and_field_name(field_type.__name__)
         nested_model_rules, _ = generate_gbnf_grammar(field_type, processed_models, created_rules)
         rules.extend(nested_model_rules)
         gbnf_type, rules = nested_model_name, rules
-    elif isclass(field_type) and issubclass(field_type, Enum):
+    elif isclass(origin_type) and issubclass(origin_type, Enum):
         enum_values = [f'"\\"{e.value}\\""' for e in field_type]  # Adding escaped quotes
         enum_rule = f"{model_name}-{field_name} ::= {' | '.join(enum_values)}"
         rules.append(enum_rule)
         gbnf_type, rules = model_name + "-" + field_name, rules
-    elif get_origin(field_type) == list:  # Array
+    elif origin_type is list:  # Array
         element_type = get_args(field_type)[0]
         element_rule_name, additional_rules = generate_gbnf_rule_for_type(
             model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
@@ -317,7 +323,7 @@ def generate_gbnf_rule_for_type(
         rules.append(array_rule)
         gbnf_type, rules = model_name + "-" + field_name, rules
 
-    elif get_origin(field_type) == set or field_type == set:  # Array
+    elif origin_type is set:  # Array
         element_type = get_args(field_type)[0]
         element_rule_name, additional_rules = generate_gbnf_rule_for_type(
             model_name, f"{field_name}-element", element_type, is_optional, processed_models, created_rules
@@ -371,7 +377,7 @@ def generate_gbnf_rule_for_type(
             gbnf_type = f"{model_name}-{field_name}-optional"
         else:
             gbnf_type = f"{model_name}-{field_name}-union"
-    elif isclass(field_type) and issubclass(field_type, str):
+    elif isclass(origin_type) and issubclass(origin_type, str):
         if field_info and hasattr(field_info, "json_schema_extra") and field_info.json_schema_extra is not None:
             triple_quoted_string = field_info.json_schema_extra.get("triple_quoted_string", False)
             markdown_string = field_info.json_schema_extra.get("markdown_code_block", False)
@@ -387,8 +393,8 @@ def generate_gbnf_rule_for_type(
             gbnf_type = PydanticDataType.STRING.value
 
     elif (
-        isclass(field_type)
-        and issubclass(field_type, float)
+        isclass(origin_type)
+        and issubclass(origin_type, float)
         and field_info
         and hasattr(field_info, "json_schema_extra")
         and field_info.json_schema_extra is not None
@@ -413,8 +419,8 @@ def generate_gbnf_rule_for_type(
         )
 
     elif (
-        isclass(field_type)
-        and issubclass(field_type, int)
+        isclass(origin_type)
+        and issubclass(origin_type, int)
         and field_info
         and hasattr(field_info, "json_schema_extra")
         and field_info.json_schema_extra is not None
@@ -462,7 +468,7 @@ def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[Bas
     if not issubclass(model, BaseModel):
         # For non-Pydantic classes, generate model_fields from __annotations__ or __init__
         if hasattr(model, "__annotations__") and model.__annotations__:
-            model_fields = {name: (typ, ...) for name, typ in model.__annotations__.items()}  # pyright: ignore[reportGeneralTypeIssues]
+            model_fields = {name: (typ, ...) for name, typ in get_type_hints(model).items()}
         else:
             init_signature = inspect.signature(model.__init__)
             parameters = init_signature.parameters
@@ -470,7 +476,7 @@ def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[Bas
                             name != "self"}
     else:
         # For Pydantic models, use model_fields and check for ellipsis (required fields)
-        model_fields = model.__annotations__
+        model_fields = get_type_hints(model)
 
     model_rule_parts = []
     nested_rules = []
@@ -706,7 +712,7 @@ def generate_markdown_documentation(
         else:
             documentation += f"  Fields:\n"  # noqa: F541
         if isclass(model) and issubclass(model, BaseModel):
-            for name, field_type in model.__annotations__.items():
+            for name, field_type in get_type_hints(model).items():
                 # if name == "markdown_code_block":
                 #    continue
                 if get_origin(field_type) == list:
@@ -754,14 +760,17 @@ def generate_field_markdown(
     field_info = model.model_fields.get(field_name)
     field_description = field_info.description if field_info and field_info.description else ""
 
-    if get_origin(field_type) == list:
+    origin_type = get_origin(field_type)
+    origin_type = field_type if origin_type is None else origin_type
+
+    if origin_type == list:
         element_type = get_args(field_type)[0]
         field_text = f"{indent}{field_name} ({format_model_and_field_name(field_type.__name__)} of {format_model_and_field_name(element_type.__name__)})"
         if field_description != "":
             field_text += ":\n"
         else:
             field_text += "\n"
-    elif get_origin(field_type) == Union:
+    elif origin_type == Union:
         element_types = get_args(field_type)
         types = []
         for element_type in element_types:
@@ -792,9 +801,9 @@ def generate_field_markdown(
             example_text = f"'{field_example}'" if isinstance(field_example, str) else field_example
             field_text += f"{indent}  Example: {example_text}\n"
 
-    if isclass(field_type) and issubclass(field_type, BaseModel):
+    if isclass(origin_type) and issubclass(origin_type, BaseModel):
         field_text += f"{indent}  Details:\n"
-        for name, type_ in field_type.__annotations__.items():
+        for name, type_ in get_type_hints(field_type).items():
             field_text += generate_field_markdown(name, type_, field_type, depth + 2)
 
     return field_text
@@ -855,7 +864,7 @@ def generate_text_documentation(
 
         if isclass(model) and issubclass(model, BaseModel):
             documentation_fields = ""
-            for name, field_type in model.__annotations__.items():
+            for name, field_type in get_type_hints(model).items():
                 # if name == "markdown_code_block":
                 #    continue
                 if get_origin(field_type) == list:
@@ -948,7 +957,7 @@ def generate_field_text(
 
     if isclass(field_type) and issubclass(field_type, BaseModel):
         field_text += f"{indent}  Details:\n"
-        for name, type_ in field_type.__annotations__.items():
+        for name, type_ in get_type_hints(field_type).items():
             field_text += generate_field_text(name, type_, field_type, depth + 2)
 
     return field_text
diff --git a/examples/pydantic_models_to_grammar_examples.py b/examples/pydantic_models_to_grammar_examples.py
index 8e7f46cf99a43b..504ed98df64f29 100644
--- a/examples/pydantic_models_to_grammar_examples.py
+++ b/examples/pydantic_models_to_grammar_examples.py
@@ -20,6 +20,8 @@ def create_completion(prompt, grammar):
     response = requests.post("http://127.0.0.1:8080/completion", headers=headers, json=data)
     data = response.json()
 
+    assert data.get("error") is None, data
+
     print(data["content"])
     return data["content"]
 
diff --git a/requirements/requirements-pydantic.txt b/requirements/requirements-pydantic.txt
index 2f9455b147d68e..bdd423e07ea36b 100644
--- a/requirements/requirements-pydantic.txt
+++ b/requirements/requirements-pydantic.txt
@@ -1,2 +1,3 @@
 docstring_parser~=0.15
 pydantic~=2.6.3
+requests

From bda62d7999caa8c222b6c354ac1e7c7442508539 Mon Sep 17 00:00:00 2001
From: 0cc4m <picard12@live.de>
Date: Mon, 15 Jul 2024 09:38:52 +0200
Subject: [PATCH 051/125] Vulkan MMQ Fix (#8479)

* Fix incoherence by adding missing LOAD_VEC_A parameter

* Fix Vulkan op result checker build error
---
 ggml/src/ggml-vulkan.cpp                       | 16 ++++++++--------
 ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp |  4 ++--
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp
index 101781ede4b4fd..8efe32329693ee 100644
--- a/ggml/src/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan.cpp
@@ -6561,7 +6561,7 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso
         ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
 
         vk_buffer buffer_gpu = extra->buffer_gpu.lock();
-        ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
+        ggml_vk_buffer_read(buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
     }
 
     std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl;
@@ -6645,7 +6645,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
                 for (int i3 = 0; i3 < src0->ne[3]; i3++) {
                     for (int i2 = 0; i2 < src0->ne[2]; i2++) {
                         const int idx = i3*src0->ne[2] + i2;
-                        ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]);
+                        ggml_vk_buffer_read(buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]);
                     }
                 }
 
@@ -6658,7 +6658,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
                 if (offset + src0_size >= buffer_gpu->size) {
                     src0_size = buffer_gpu->size - offset;
                 }
-                ggml_vk_buffer_read(ctx, buffer_gpu, offset, src0_clone->data, src0_size);
+                ggml_vk_buffer_read(buffer_gpu, offset, src0_clone->data, src0_size);
                 memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS);
             }
         } else {
@@ -6687,7 +6687,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
                 for (int i3 = 0; i3 < src1->ne[3]; i3++) {
                     for (int i2 = 0; i2 < src1->ne[2]; i2++) {
                         const int idx = i3*src1->ne[2] + i2;
-                        ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]);
+                        ggml_vk_buffer_read(buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]);
                     }
                 }
 
@@ -6700,7 +6700,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
                 if (offset + src1_size >= buffer_gpu->size) {
                     src1_size = buffer_gpu->size - offset;
                 }
-                ggml_vk_buffer_read(ctx, buffer_gpu, offset, src1_clone->data, src1_size);
+                ggml_vk_buffer_read(buffer_gpu, offset, src1_clone->data, src1_size);
                 memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS);
             }
         } else {
@@ -6745,7 +6745,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
                 for (int i3 = 0; i3 < src2->ne[3]; i3++) {
                     for (int i2 = 0; i2 < src2->ne[2]; i2++) {
                         const int idx = i3*src2->ne[2] + i2;
-                        ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]);
+                        ggml_vk_buffer_read(buffer_gpu, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]);
                     }
                 }
 
@@ -6758,7 +6758,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_tensor *
                 if (offset + src2_size >= buffer_gpu->size) {
                     src2_size = buffer_gpu->size - offset;
                 }
-                ggml_vk_buffer_read(ctx, buffer_gpu, offset, src2_clone->data, src2_size);
+                ggml_vk_buffer_read(buffer_gpu, offset, src2_clone->data, src2_size);
                 memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS);
             }
         } else {
@@ -6922,7 +6922,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_tensor *
             tensor_size = buffer_gpu->size - (extra->offset + tensor->view_offs);
         }
 
-        ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
+        ggml_vk_buffer_read(buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
     }
 
     float first_error_result = -1.0f;
diff --git a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
index fa231c0ec16886..3038d647f10f35 100644
--- a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -270,10 +270,10 @@ void matmul_shaders(std::vector<std::future<void>>& tasks, bool fp16, bool matmu
         std::string data_a_key = "DATA_A_" + to_uppercase(tname);
         std::string load_vec_a = (tname == "f32" || tname == "f16") ? load_vec : "2";
         tasks.push_back(std::async(std::launch::async, [=] {
-            string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16);
+            string_to_spv(shader_name + "_" + tname + "_f32", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}), fp16);
         }));
         tasks.push_back(std::async(std::launch::async, [=] {
-            string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "2"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16);
+            string_to_spv(shader_name + "_" + tname + "_f32_aligned", "mul_mm.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"D_TYPE", "float"}}), fp16);
         }));
     }
 }

From 3dfda05956befb350745c5c2f7134d06adfe8724 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 15 Jul 2024 14:10:39 +0300
Subject: [PATCH 052/125] llama : de-duplicate deepseek2 norm

---
 src/llama.cpp | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index 400a4232beeb09..ddf0262d443b06 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -12873,12 +12873,12 @@ struct llm_build_context {
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
             cb(ffn_inp, "ffn_inp", il);
 
-            if ((uint32_t) il < hparams.n_layer_dense_lead) {
-                cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, cb, il);
-                cb(cur, "ffn_norm", il);
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
 
+            if ((uint32_t) il < hparams.n_layer_dense_lead) {
                 cur = llm_build_ffn(ctx0, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
@@ -12888,11 +12888,6 @@ struct llm_build_context {
                 cb(cur, "ffn_out", il);
             } else {
                 // MoE branch
-                cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, cb, il);
-                cb(cur, "ffn_norm", il);
-
                 ggml_tensor * moe_out =
                         llm_build_moe_ffn(ctx0, cur,
                             model.layers[il].ffn_gate_inp,

From 16bdfa42acb09175e88cf97e9d9e4e48f616d120 Mon Sep 17 00:00:00 2001
From: "Meng, Hengyu" <hengyu.meng@intel.com>
Date: Mon, 15 Jul 2024 19:32:15 +0800
Subject: [PATCH 053/125] [SYCL] add concat through dim 1/2 (#8483)

* add concat through dim 1/2
---
 ggml/src/ggml-sycl.cpp         |  67 +----------
 ggml/src/ggml-sycl/backend.hpp |   1 +
 ggml/src/ggml-sycl/concat.cpp  | 195 +++++++++++++++++++++++++++++++++
 ggml/src/ggml-sycl/concat.hpp  |  21 ++++
 4 files changed, 218 insertions(+), 66 deletions(-)
 create mode 100644 ggml/src/ggml-sycl/concat.cpp
 create mode 100644 ggml/src/ggml-sycl/concat.hpp

diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl.cpp
index 5a890237f24535..36518ff93b0340 100644
--- a/ggml/src/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl.cpp
@@ -291,29 +291,6 @@ static void sqr_f32(const float * x, float * dst, const int k,
     dst[i] = x[i] * x[i];
 }
 
-static void concat_f32(const float  *x,const float  *y, float *dst, const int ne0, const int ne02,
-                       const sycl::nd_item<3> &item_ct1) {
-    int nidx = item_ct1.get_local_id(2) +
-               item_ct1.get_group(2) * item_ct1.get_local_range(2);
-    if (nidx >= ne0) {
-        return;
-    }
-    // operation
-    int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
-                     item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
-    if (item_ct1.get_group(0) < ne02) { // src0
-        int offset_src =
-            nidx + item_ct1.get_group(1) * ne0 +
-            item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
-            dst[offset_dst] = x[offset_src];
-    } else {
-        int offset_src =
-            nidx + item_ct1.get_group(1) * ne0 +
-            (item_ct1.get_group(0) - ne02) * ne0 * item_ct1.get_group_range(1);
-            dst[offset_dst] = y[offset_src];
-    }
-}
-
 static void upscale_f32(const float  *x, float *dst, const int nb00, const int nb01,
                         const int nb02, const int nb03, const int ne10, const int ne11,
                         const int ne12, const int ne13, const float sf0, const float sf1,
@@ -1347,20 +1324,6 @@ static void sqr_f32_sycl(const float *x, float *dst, const int k,
         });
 }
 
-static void concat_f32_sycl(const float *x, const float *y, float *dst,
-                            const int ne0, int ne1, int ne2, int ne02,
-                            queue_ptr stream) {
-    int num_blocks = (ne0 + SYCL_CONCAT_BLOCK_SIZE - 1) / SYCL_CONCAT_BLOCK_SIZE;
-    sycl::range<3> gridDim(ne2, ne1, num_blocks);
-    stream->parallel_for(
-        sycl::nd_range<3>(gridDim *
-                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
-                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
-            concat_f32(x, y, dst, ne0, ne02, item_ct1);
-        });
-}
-
 static void upscale_f32_sycl(const float *x, float *dst, const int nb00, const int nb01,
                              const int nb02, const int nb03, const int ne10, const int ne11,
                              const int ne12, const int ne13, const float sf0, const float sf1,
@@ -2429,28 +2392,6 @@ inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor
     (void) src1_dd;
 }
 
-inline void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-                                const ggml_tensor *src1, ggml_tensor *dst,
-                                const float *src0_dd, const float *src1_dd,
-                                float *dst_dd,
-                                const queue_ptr &main_stream) {
-#pragma message("TODO: generalize concat kernel for dim != 2")
-#pragma message("      https://github.com/ggerganov/llama.cpp/pull/7563")
-    int dim = dst->op_params[0];
-    GGML_ASSERT(dim == 2);
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-    for (int i3 = 0; i3 < dst->ne[3]; i3++) {
-        concat_f32_sycl(src0_dd + i3 * (src0->nb[3] / 4), src1_dd + i3 * (src1->nb[3] / 4), dst_dd + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], main_stream);
-    }
-
-    (void) src1;
-    (void) dst;
-}
-
 inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
                                  const ggml_tensor *src1, ggml_tensor *dst,
                                  const float *src0_dd, const float *src1_dd,
@@ -3359,12 +3300,6 @@ static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, const ggml_ten
     GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
 
-static void ggml_sycl_concat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
-    ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_concat);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
-}
-
 static void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_SYCL_DEBUG("call %s\n", __func__);
     ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_upscale);
@@ -4101,7 +4036,7 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens
             func = ggml_sycl_group_norm;
             break;
         case GGML_OP_CONCAT:
-            func = ggml_sycl_concat;
+            func = ggml_sycl_op_concat;
             break;
         case GGML_OP_UPSCALE:
             func = ggml_sycl_upscale;
diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp
index 2a789edfc909d5..067181de3836ce 100644
--- a/ggml/src/ggml-sycl/backend.hpp
+++ b/ggml/src/ggml-sycl/backend.hpp
@@ -13,6 +13,7 @@
 #ifndef GGML_SYCL_BACKEND_HPP
 #define GGML_SYCL_BACKEND_HPP
 
+#include "concat.hpp"
 #include "common.hpp"
 #include "convert.hpp"
 #include "dequantize.hpp"
diff --git a/ggml/src/ggml-sycl/concat.cpp b/ggml/src/ggml-sycl/concat.cpp
new file mode 100644
index 00000000000000..632eedb9d42b81
--- /dev/null
+++ b/ggml/src/ggml-sycl/concat.cpp
@@ -0,0 +1,195 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#include "concat.hpp"
+#include "common.hpp"
+
+static void concat_f32_dim0(const float *x, const float *y, float *dst,
+                            const int ne0, const int ne00,
+                            const sycl::nd_item<3> &item_ct1) {
+  int nidx = item_ct1.get_local_id(2) +
+             item_ct1.get_group(2) * item_ct1.get_local_range(2);
+  if (nidx >= ne0) {
+    return;
+  }
+  // operation
+  int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
+                   item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+  if (nidx < ne00) { // src0
+    int offset_src = nidx + item_ct1.get_group(1) * ne00 +
+                     item_ct1.get_group(0) * ne00 * item_ct1.get_group_range(1);
+    dst[offset_dst] = x[offset_src];
+  } else {
+    int offset_src =
+        nidx - ne00 + item_ct1.get_group(1) * (ne0 - ne00) +
+        item_ct1.get_group(0) * (ne0 - ne00) * item_ct1.get_group_range(1);
+    dst[offset_dst] = y[offset_src];
+  }
+}
+
+static void concat_f32_dim1(const float *x, const float *y, float *dst,
+                            const int ne0, const int ne01,
+                            const sycl::nd_item<3> &item_ct1) {
+  int nidx = item_ct1.get_local_id(2) +
+             item_ct1.get_group(2) * item_ct1.get_local_range(2);
+  if (nidx >= ne0) {
+    return;
+  }
+  // operation
+  int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
+                   item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+  if (item_ct1.get_group(1) < ne01) { // src0
+    int offset_src =
+        nidx + item_ct1.get_group(1) * ne0 + item_ct1.get_group(0) * ne0 * ne01;
+    dst[offset_dst] = x[offset_src];
+  } else {
+    int offset_src =
+        nidx + (item_ct1.get_group(1) - ne01) * ne0 +
+        item_ct1.get_group(0) * ne0 * (item_ct1.get_group_range(1) - ne01);
+    dst[offset_dst] = y[offset_src];
+  }
+}
+
+static void concat_f32_dim2(const float *x, const float *y, float *dst,
+                            const int ne0, const int ne02,
+                            const sycl::nd_item<3> &item_ct1) {
+  int nidx = item_ct1.get_local_id(2) +
+             item_ct1.get_group(2) * item_ct1.get_local_range(2);
+  if (nidx >= ne0) {
+    return;
+  }
+  // operation
+  int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
+                   item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+  if (item_ct1.get_group(0) < ne02) { // src0
+    int offset_src = nidx + item_ct1.get_group(1) * ne0 +
+                     item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
+    dst[offset_dst] = x[offset_src];
+  } else {
+    int offset_src =
+        nidx + item_ct1.get_group(1) * ne0 +
+        (item_ct1.get_group(0) - ne02) * ne0 * item_ct1.get_group_range(1);
+    dst[offset_dst] = y[offset_src];
+  }
+}
+
+static void concat_f32_sycl(const float *x, const float *y, float *dst,
+                            int ne00, int ne01, int ne02, int ne0, int ne1,
+                            int ne2, int dim, queue_ptr stream) {
+  int num_blocks = (ne0 + SYCL_CONCAT_BLOCK_SIZE - 1) / SYCL_CONCAT_BLOCK_SIZE;
+  sycl::range<3> gridDim(ne2, ne1, num_blocks);
+  switch (dim) {
+  case 0:
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+          concat_f32_dim0(x, y, dst, ne0, ne00, item_ct1);
+        });
+    break;
+  case 1:
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+          concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1);
+        });
+    break;
+  default:
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+          concat_f32_dim2(x, y, dst, ne0, ne02, item_ct1);
+        });
+    break;
+  }
+}
+
+// non-contiguous kernel (slow)
+static void concat_f32_sycl_non_cont(
+    queue_ptr stream, const char *src0, const char *src1, char *dst,
+    int64_t ne00, int64_t ne01, int64_t ne02, int64_t ne03, uint64_t nb00,
+    uint64_t nb01, uint64_t nb02, uint64_t nb03, int64_t /*ne10*/,
+    int64_t /*ne11*/, int64_t /*ne12*/, int64_t /*ne13*/, uint64_t nb10,
+    uint64_t nb11, uint64_t nb12, uint64_t nb13, int64_t ne0, int64_t ne1,
+    int64_t ne2, int64_t ne3, uint64_t nb0, uint64_t nb1, uint64_t nb2,
+    uint64_t nb3, int32_t dim) {
+  sycl::range<3> gridDim(ne3, ne2, ne1);
+  stream->parallel_for(
+      sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)),
+      [=](sycl::nd_item<3> item_ct1) {
+        int64_t i3 = item_ct1.get_group(0);
+        int64_t i2 = item_ct1.get_group(1);
+        int64_t i1 = item_ct1.get_group(2);
+
+        int64_t o[4] = {0, 0, 0, 0};
+        o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03));
+
+        const float *x;
+
+        for (int i0 = item_ct1.get_local_id(2); i0 < ne0;
+             i0 += item_ct1.get_local_range(2)) {
+          if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+            x = (const float *)(src0 + (i3)*nb03 + (i2)*nb02 + (i1)*nb01 +
+                                (i0)*nb00);
+          } else {
+            x = (const float *)(src1 + (i3 - o[3]) * nb13 + (i2 - o[2]) * nb12 +
+                                (i1 - o[1]) * nb11 + (i0 - o[0]) * nb10);
+          }
+
+          float *y = (float *)(dst + i3 * nb3 + i2 * nb2 + i1 * nb1 + i0 * nb0);
+
+          *y = *x;
+        }
+      });
+}
+
+void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst) {
+  queue_ptr stream = ctx.stream();
+
+  const int32_t dim = ((int32_t *)dst->op_params)[0];
+
+  if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
+    const float *src0_d = (const float *)src0->data;
+    const float *src1_d = (const float *)src1->data;
+
+    float *dst_d = (float *)dst->data;
+
+    if (dim != 3) {
+      for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+        concat_f32_sycl(
+            src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4),
+            dst_d + i3 * (dst->nb[3] / 4), src0->ne[0], src0->ne[1],
+            src0->ne[2], dst->ne[0], dst->ne[1], dst->ne[2], dim, stream);
+      }
+    } else {
+      const size_t size0 = ggml_nbytes(src0);
+      const size_t size1 = ggml_nbytes(src1);
+
+      SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0).wait()));
+      SYCL_CHECK(CHECK_TRY_ERROR(
+          stream->memcpy(dst_d + size0 / 4, src1_d, size1).wait()));
+    }
+  } else
+    concat_f32_sycl_non_cont(
+        stream, (const char *)src0->data, (const char *)src1->data,
+        (char *)dst->data, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
+        src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], src1->ne[0],
+        src1->ne[1], src1->ne[2], src1->ne[3], src1->nb[0], src1->nb[1],
+        src1->nb[2], src1->nb[3], dst->ne[0], dst->ne[1], dst->ne[2],
+        dst->ne[3], dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
+}
diff --git a/ggml/src/ggml-sycl/concat.hpp b/ggml/src/ggml-sycl/concat.hpp
new file mode 100644
index 00000000000000..5a04feaab6b0a3
--- /dev/null
+++ b/ggml/src/ggml-sycl/concat.hpp
@@ -0,0 +1,21 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_CONCAT_HPP
+#define GGML_SYCL_CONCAT_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
+                                const ggml_tensor *src1, ggml_tensor *dst);
+
+#endif // GGML_SYCL_CONCAT_HPP

From fc690b018e459012cd82c37f1343e9bb658987d1 Mon Sep 17 00:00:00 2001
From: NikolaiLyssogor <59844691+NikolaiLyssogor@users.noreply.github.com>
Date: Mon, 15 Jul 2024 04:46:39 -0700
Subject: [PATCH 054/125] docs: fix links in development docs [no ci] (#8481)

Fixes a few links to within the repo that were broken in the reorganization of the
documentation in #8325.
---
 docs/development/HOWTO-add-model.md              | 16 ++++++++--------
 .../token_generation_performance_tips.md         |  2 +-
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md
index 2712b66c175339..04c5ccbbe60c3d 100644
--- a/docs/development/HOWTO-add-model.md
+++ b/docs/development/HOWTO-add-model.md
@@ -9,15 +9,15 @@ Adding a model requires few steps:
 After following these steps, you can open PR.
 
 Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
-- [main](../examples/main)
-- [imatrix](../examples/imatrix)
-- [quantize](../examples/quantize)
-- [server](../examples/server)
+- [main](/examples/main/)
+- [imatrix](/examples/imatrix/)
+- [quantize](/examples/quantize/)
+- [server](/examples/server/)
 
 ### 1. Convert the model to GGUF
 
 This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
-Depending on the model architecture, you can use either [convert_hf_to_gguf.py](../convert_hf_to_gguf.py) or [examples/convert_legacy_llama.py](../examples/convert_legacy_llama.py) (for `llama/llama2` models in `.pth` format).
+Depending on the model architecture, you can use either [convert_hf_to_gguf.py](/convert_hf_to_gguf.py) or [examples/convert_legacy_llama.py](/examples/convert_legacy_llama.py) (for `llama/llama2` models in `.pth` format).
 
 The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
 
@@ -31,7 +31,7 @@ class MyModel(Model):
     model_arch = gguf.MODEL_ARCH.GROK
 ```
 
-2. Define the layout of the GGUF tensors in [constants.py](../gguf-py/gguf/constants.py)
+2. Define the layout of the GGUF tensors in [constants.py](/gguf-py/gguf/constants.py)
 
 Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`.
 
@@ -54,7 +54,7 @@ Example for `falcon` model:
 
 As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist.
 
-Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](../gguf-py/gguf/tensor_mapping.py) file.
+Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](/gguf-py/gguf/tensor_mapping.py) file.
 
 If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it.
 
@@ -100,7 +100,7 @@ Have a look at existing implementation like `build_llama`, `build_dbrx` or `buil
 
 When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR.
 
-Note: to debug the inference graph: you can use [llama-eval-callback](../examples/eval-callback).
+Note: to debug the inference graph: you can use [llama-eval-callback](/examples/eval-callback/).
 
 ## GGUF specification
 
diff --git a/docs/development/token_generation_performance_tips.md b/docs/development/token_generation_performance_tips.md
index c0840cad57fb3b..41b7232c976b3a 100644
--- a/docs/development/token_generation_performance_tips.md
+++ b/docs/development/token_generation_performance_tips.md
@@ -1,7 +1,7 @@
 # Token generation performance troubleshooting
 
 ## Verifying that the model is running on the GPU with CUDA
-Make sure you compiled llama with the correct env variables according to [this guide](../README.md#CUDA), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
+Make sure you compiled llama with the correct env variables according to [this guide](/docs/build.md#cuda), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
 ```shell
 ./llama-cli -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some "
 ```

From 9104bc20edf47f74dc49379b7d61d0c6e85a4882 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 15 Jul 2024 14:54:58 +0300
Subject: [PATCH 055/125] common : add --no-cont-batching arg (#6358)

---
 common/common.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/common/common.cpp b/common/common.cpp
index 1e5fc30dd458aa..9035c359267a7e 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -797,6 +797,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.cont_batching = true;
         return true;
     }
+    if (arg == "-nocb" || arg == "--no-cont-batching") {
+        params.cont_batching = false;
+        return true;
+    }
     if (arg == "-fa" || arg == "--flash-attn") {
         params.flash_attn = true;
         return true;
@@ -1538,6 +1542,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
     options.push_back({ "*",           "-np,   --parallel N",           "number of parallel sequences to decode (default: %d)", params.n_parallel });
     options.push_back({ "*",           "-ns,   --sequences N",          "number of sequences to decode (default: %d)", params.n_sequences });
     options.push_back({ "*",           "-cb,   --cont-batching",        "enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled" });
+    options.push_back({ "*",           "-nocb, --no-cont-batching",     "disable continuous batching" });
 
     options.push_back({ "multi-modality" });
     options.push_back({ "*",           "       --mmproj FILE",          "path to a multimodal projector file for LLaVA. see examples/llava/README.md" });

From f17f39ff9cb71fa7b0eda0c649bb0ce9b2d8a6b8 Mon Sep 17 00:00:00 2001
From: M-A <maruel@gmail.com>
Date: Mon, 15 Jul 2024 08:04:56 -0400
Subject: [PATCH 056/125] server: update README.md with llama-server --help
 output [no ci] (#8472)

The README.md had a stale information. In particular, the --ctx-size
"defaults to 512" confused me and I had to check the code to confirm
this was false. This the server is evolving rapidly, it's probably
better to keep the source of truth at a single place (in the source) and
generate the README.md based on that.

Did:

    make llama-server
    ./llama-server --help > t.txt
    vimdiff t.txt examples/server/README.md

I copied the content inside a backquote block. I would have preferred
proper text but it would require a fair amount of surgery to make the
current output compatible with markdown. A follow up could be to
automate this process with a script.

No functional change.
---
 examples/server/README.md | 338 +++++++++++++++++++++++++++++++-------
 1 file changed, 275 insertions(+), 63 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index cb45ee06dd0a08..d7487ec1b5b7e8 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -15,69 +15,281 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
 
 The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216).
 
-**Command line options:**
-
-- `-v`, `--verbose`: Enable verbose server output. When using the `/completion` endpoint, this includes the tokenized prompt, the full request and the full response.
-- `-t N`, `--threads N`: Set the number of threads to use by CPU layers during generation. Not used by model layers that are offloaded to GPU. This option has no effect when using the maximum number of GPU layers. Default: `std::thread::hardware_concurrency()` (number of CPU cores).
-- `-tb N, --threads-batch N`: Set the number of threads to use by CPU layers during batch and prompt processing (>= 32 tokens). This option has no effect if a GPU is available. Default: `--threads`.
-- `--threads-http N`: Number of threads in the http server pool to process requests. Default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`
-- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
-- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file. Default: unused
-- `-hfr REPO, --hf-repo REPO`: Hugging Face model repository. Default: unused
-- `-hff FILE, --hf-file FILE`: Hugging Face model file. Default: unused
-- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
-- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is `512`, but LLaMA models were built with a context of `2048`, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of `4096`.
-- `-ngl N`, `--n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
-- `-mg i, --main-gpu i`: When using multiple GPUs, this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default, GPU `0` is used.
-- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs, this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default, the data is split in proportion to VRAM, but this may not be optimal for performance.
-- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`
-- `-ub N`, `--ubatch-size N`: Physical maximum batch size. Default: `512`
-- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
-- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
-- `--numa STRATEGY`: Attempt one of the below optimization strategies that may help on some NUMA systems
-- `--numa distribute`: Spread execution evenly over all nodes
-- `--numa isolate`: Only spawn threads on CPUs on the node that execution started on
-- `--numa numactl`: Use the CPU map provided by numactl. If run without this previously, it is recommended to drop the system page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/1437
-- `--numa`: Attempt optimizations that may help on some NUMA systems.
-- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
-- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
-- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`
-- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`
-- `--port`: Set the port to listen. Default: `8080`
-- `--path`: Path from which to serve static files. Default: disabled
-- `--api-key`: Set an api key for request authorization. By default, the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
-- `--api-key-file`: Path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`s.
-- `--embeddings`: Enable embedding vector output and the OAI compatible endpoint /v1/embeddings. Physical batch size (`--ubatch-size`) must be carefully defined. Default: disabled
-- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1`. Values > 1 will allow for higher throughput with multiple parallel requests but the results will **not** be deterministic due to differences in rounding error.
-- `-cb`, `--cont-batching`: Enable continuous batching (a.k.a dynamic batching).  Default: disabled
-- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load a system prompt (initial prompt of all slots). This is useful for chat applications. [See more](#change-system-prompt-on-runtime)
-- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.
-- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend. Used together with group attention width `--grp-attn-w`. Default: `1`, which is disabled.
-- `--grp-attn-w`: Set the group attention width to extend context size through self-extend.  Used together with group attention factor `--grp-attn-n`. Default: `512`
-- `-n N, --n-predict N`: Set the maximum tokens to predict. Default: `-1`
-- `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included.
-- `--metrics`: enable prometheus `/metrics` compatible endpoint. Default: disabled
-- `--slot-save-path PATH`: Specifies the path where the state of slots (the prompt cache) can be stored. If not provided, the slot management endpoints will be disabled.
-- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name.  Default: template taken from model's metadata. We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
-- `--log-disable`: Output logs to stdout only, not to `llama.log`. Default: enabled
-- `--log-format FORMAT`: Define the log output to FORMAT: json or text Default: `json`
-- `--rope-scaling` : RoPE scaling method. Defaults to linear unless otherwise specified by the model. Options are `none`, `linear`, `yarn`
-- `--rope-freq-base N` : RoPE frequency base (default: loaded from model)
-- `--rope-freq-scale N`: RoPE frequency scaling factor, expands context by a factor of 1/N (e.g. 0.25)
-- `--yarn-ext-factor N` : YaRN: extrapolation mix factor (Default: 1.0, 0.0 = full interpolation)
-- `--yarn-attn-factor N` : YaRN: scale sqrt(t) or attention magnitude (default: 1.0)
-- `--yarn-beta-slow N`: YaRN: High correction dim or alpha (default: 1.0)
-- `--yarn-beta-fast N`: YaRN: low correction dim or beta (default: 32.0)
-- `--pooling` : Pooling type for embeddings, use model default if unspecified. Options are `none`, `mean`, `cls`
-- `-dt N`, `--defrag-thold N`: KV cache defragmentation threshold (default: -1.0, < 0 = disabled)
-- `-fa`, `--flash-attn` : enable flash attention (default: disabled).
-- `-ctk TYPE`, `--cache-type-k TYPE` : KV cache data type for K (default: `f16`, options `f32`, `f16`, `q8_0`, `q4_0`, `q4_1`, `iq4_nl`, `q5_0`, or `q5_1`)
-- `-ctv TYPE`, `--cache-type-v TYPE` : KV cache type for V (default `f16`, see `-ctk` for options)
-- `--spm-infill` : Use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this.
-
-**If compiled with `LLAMA_SERVER_SSL=ON`**
-- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
-- `--ssl-cert-file FNAME`: path to file a PEM-encoded SSL certificate
+## Usage
+
+```
+usage: ./llama-server [options]
+
+general:
+
+  -h,    --help, --usage          print usage and exit
+         --version                show version and build info
+  -v,    --verbose                print verbose information
+         --verbosity N            set specific verbosity level (default: 0)
+         --verbose-prompt         print a verbose prompt before generation (default: false)
+         --no-display-prompt      don't print prompt at generation (default: false)
+  -co,   --color                  colorise output to distinguish prompt and user input from generations (default: false)
+  -s,    --seed SEED              RNG seed (default: -1, use random seed for < 0)
+  -t,    --threads N              number of threads to use during generation (default: 8)
+  -tb,   --threads-batch N        number of threads to use during batch and prompt processing (default: same as --threads)
+  -td,   --threads-draft N        number of threads to use during generation (default: same as --threads)
+  -tbd,  --threads-batch-draft N  number of threads to use during batch and prompt processing (default: same as --threads-draft)
+         --draft N                number of tokens to draft for speculative decoding (default: 5)
+  -ps,   --p-split N              speculative decoding split probability (default: 0.1)
+  -lcs,  --lookup-cache-static FNAME
+                                  path to static lookup cache to use for lookup decoding (not updated by generation)
+  -lcd,  --lookup-cache-dynamic FNAME
+                                  path to dynamic lookup cache to use for lookup decoding (updated by generation)
+  -c,    --ctx-size N             size of the prompt context (default: 0, 0 = loaded from model)
+  -n,    --predict N              number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)
+  -b,    --batch-size N           logical maximum batch size (default: 2048)
+  -ub,   --ubatch-size N          physical maximum batch size (default: 512)
+         --keep N                 number of tokens to keep from the initial prompt (default: 0, -1 = all)
+         --chunks N               max number of chunks to process (default: -1, -1 = all)
+  -fa,   --flash-attn             enable Flash Attention (default: disabled)
+  -p,    --prompt PROMPT          prompt to start generation with
+                                  in conversation mode, this will be used as system prompt
+                                  (default: '')
+  -f,    --file FNAME             a file containing the prompt (default: none)
+         --in-file FNAME          an input file (repeat to specify multiple files)
+  -bf,   --binary-file FNAME      binary file containing the prompt (default: none)
+  -e,    --escape                 process escapes sequences (\n, \r, \t, \', \", \\) (default: true)
+         --no-escape              do not process escape sequences
+  -ptc,  --print-token-count N    print token count every N tokens (default: -1)
+         --prompt-cache FNAME     file to cache prompt state for faster startup (default: none)
+         --prompt-cache-all       if specified, saves user input and generations to cache as well
+                                  not supported with --interactive or other interactive options
+         --prompt-cache-ro        if specified, uses the prompt cache but does not update it
+  -r,    --reverse-prompt PROMPT  halt generation at PROMPT, return control in interactive mode
+                                  can be specified more than once for multiple prompts
+  -sp,   --special                special tokens output enabled (default: false)
+  -cnv,  --conversation           run in conversation mode, does not print special tokens and suffix/prefix
+                                  if suffix/prefix are not specified, default chat template will be used
+                                  (default: false)
+  -i,    --interactive            run in interactive mode (default: false)
+  -if,   --interactive-first      run in interactive mode and wait for input right away (default: false)
+  -mli,  --multiline-input        allows you to write or paste multiple lines without ending each in '\'
+         --in-prefix-bos          prefix BOS to user inputs, preceding the `--in-prefix` string
+         --in-prefix STRING       string to prefix user inputs with (default: empty)
+         --in-suffix STRING       string to suffix after user inputs with (default: empty)
+         --spm-infill             use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled)
+
+sampling:
+
+         --samplers SAMPLERS      samplers that will be used for generation in the order, separated by ';'
+                                  (default: top_k;tfs_z;typical_p;top_p;min_p;temperature)
+         --sampling-seq SEQUENCE  simplified sequence for samplers that will be used (default: kfypmt)
+         --ignore-eos             ignore end of stream token and continue generating (implies --logit-bias EOS-inf)
+         --penalize-nl            penalize newline tokens (default: false)
+         --temp N                 temperature (default: 0.8)
+         --top-k N                top-k sampling (default: 40, 0 = disabled)
+         --top-p N                top-p sampling (default: 0.9, 1.0 = disabled)
+         --min-p N                min-p sampling (default: 0.1, 0.0 = disabled)
+         --tfs N                  tail free sampling, parameter z (default: 1.0, 1.0 = disabled)
+         --typical N              locally typical sampling, parameter p (default: 1.0, 1.0 = disabled)
+         --repeat-last-n N        last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size)
+         --repeat-penalty N       penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled)
+         --presence-penalty N     repeat alpha presence penalty (default: 0.0, 0.0 = disabled)
+         --frequency-penalty N    repeat alpha frequency penalty (default: 0.0, 0.0 = disabled)
+         --dynatemp-range N       dynamic temperature range (default: 0.0, 0.0 = disabled)
+         --dynatemp-exp N         dynamic temperature exponent (default: 1.0)
+         --mirostat N             use Mirostat sampling.
+                                  Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.
+                                  (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)
+         --mirostat-lr N          Mirostat learning rate, parameter eta (default: 0.1)
+         --mirostat-ent N         Mirostat target entropy, parameter tau (default: 5.0)
+         -l TOKEN_ID(+/-)BIAS     modifies the likelihood of token appearing in the completion,
+                                  i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
+                                  or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'
+         --cfg-negative-prompt PROMPT
+                                  negative prompt to use for guidance (default: '')
+         --cfg-negative-prompt-file FNAME
+                                  negative prompt file to use for guidance
+         --cfg-scale N            strength of guidance (default: 1.0, 1.0 = disable)
+         --chat-template JINJA_TEMPLATE
+                                  set custom jinja chat template (default: template taken from model's metadata)
+                                  if suffix/prefix are specified, template will be disabled
+                                  only commonly used templates are accepted:
+                                  https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
+
+grammar:
+
+         --grammar GRAMMAR        BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '')
+         --grammar-file FNAME     file to read grammar from
+  -j,    --json-schema SCHEMA     JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
+                                  For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead
+
+embedding:
+
+         --pooling {none,mean,cls,last}
+                                  pooling type for embeddings, use model default if unspecified
+         --attention {causal,non-causal}
+                                  attention type for embeddings, use model default if unspecified
+
+context hacking:
+
+         --rope-scaling {none,linear,yarn}
+                                  RoPE frequency scaling method, defaults to linear unless specified by the model
+         --rope-scale N           RoPE context scaling factor, expands context by a factor of N
+         --rope-freq-base N       RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
+         --rope-freq-scale N      RoPE frequency scaling factor, expands context by a factor of 1/N
+         --yarn-orig-ctx N        YaRN: original context size of model (default: 0 = model training context size)
+         --yarn-ext-factor N      YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
+         --yarn-attn-factor N     YaRN: scale sqrt(t) or attention magnitude (default: 1.0)
+         --yarn-beta-slow N       YaRN: high correction dim or alpha (default: 1.0)
+         --yarn-beta-fast N       YaRN: low correction dim or beta (default: 32.0)
+  -gan,  --grp-attn-n N           group-attention factor (default: 1)
+  -gaw,  --grp-attn-w N           group-attention width (default: 512.0)
+  -dkvc, --dump-kv-cache          verbose print of the KV cache
+  -nkvo, --no-kv-offload          disable KV offload
+  -ctk,  --cache-type-k TYPE      KV cache data type for K (default: f16)
+  -ctv,  --cache-type-v TYPE      KV cache data type for V (default: f16)
+
+perplexity:
+
+         --all-logits             return logits for all tokens in the batch (default: false)
+         --hellaswag              compute HellaSwag score over random tasks from datafile supplied with -f
+         --hellaswag-tasks N      number of tasks to use when computing the HellaSwag score (default: 400)
+         --winogrande             compute Winogrande score over random tasks from datafile supplied with -f
+         --winogrande-tasks N     number of tasks to use when computing the Winogrande score (default: 0)
+         --multiple-choice        compute multiple choice score over random tasks from datafile supplied with -f
+         --multiple-choice-tasks N
+                                  number of tasks to use when computing the multiple choice score (default: 0)
+         --kl-divergence          computes KL-divergence to logits provided via --kl-divergence-base
+         --ppl-stride N           stride for perplexity calculation (default: 0)
+         --ppl-output-type {0,1}  output type for perplexity calculation (default: 0)
+
+parallel:
+
+  -dt,   --defrag-thold N         KV cache defragmentation threshold (default: -1.0, < 0 - disabled)
+  -np,   --parallel N             number of parallel sequences to decode (default: 1)
+  -ns,   --sequences N            number of sequences to decode (default: 1)
+  -cb,   --cont-batching          enable continuous batching (a.k.a dynamic batching) (default: enabled)
+
+multi-modality:
+
+         --mmproj FILE            path to a multimodal projector file for LLaVA. see examples/llava/README.md
+         --image FILE             path to an image file. use with multimodal models. Specify multiple times for batching
+
+backend:
+
+         --rpc SERVERS            comma separated list of RPC servers
+         --mlock                  force system to keep model in RAM rather than swapping or compressing
+         --no-mmap                do not memory-map model (slower load but may reduce pageouts if not using mlock)
+         --numa TYPE              attempt optimizations that help on some NUMA systems
+                                    - distribute: spread execution evenly over all nodes
+                                    - isolate: only spawn threads on CPUs on the node that execution started on
+                                    - numactl: use the CPU map provided by numactl
+                                  if run without this previously, it is recommended to drop the system page cache before using this
+                                  see https://github.com/ggerganov/llama.cpp/issues/1437
+
+model:
+
+         --check-tensors          check model tensor data for invalid values (default: false)
+         --override-kv KEY=TYPE:VALUE
+                                  advanced option to override model metadata by key. may be specified multiple times.
+                                  types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false
+         --lora FNAME             apply LoRA adapter (implies --no-mmap)
+         --lora-scaled FNAME S    apply LoRA adapter with user defined scaling S (implies --no-mmap)
+         --lora-base FNAME        optional model to use as a base for the layers modified by the LoRA adapter
+         --control-vector FNAME   add a control vector
+                                  note: this argument can be repeated to add multiple control vectors
+         --control-vector-scaled FNAME SCALE
+                                  add a control vector with user defined scaling SCALE
+                                  note: this argument can be repeated to add multiple scaled control vectors
+         --control-vector-layer-range START END
+                                  layer range to apply the control vector(s) to, start and end inclusive
+  -m,    --model FNAME            model path (default: models/$filename with filename from --hf-file
+                                  or --model-url if set, otherwise models/7B/ggml-model-f16.gguf)
+  -md,   --model-draft FNAME      draft model for speculative decoding (default: unused)
+  -mu,   --model-url MODEL_URL    model download url (default: unused)
+  -hfr,  --hf-repo REPO           Hugging Face model repository (default: unused)
+  -hff,  --hf-file FILE           Hugging Face model file (default: unused)
+  -hft,  --hf-token TOKEN         Hugging Face access token (default: value from HF_TOKEN environment variable)
+
+retrieval:
+
+         --context-file FNAME     file to load context from (repeat to specify multiple files)
+         --chunk-size N           minimum length of embedded text chunks (default: 64)
+         --chunk-separator STRING 
+                                  separator between chunks (default: '
+                                  ')
+
+passkey:
+
+         --junk N                 number of times to repeat the junk text (default: 250)
+         --pos N                  position of the passkey in the junk text (default: -1)
+
+imatrix:
+
+  -o,    --output FNAME           output file (default: 'imatrix.dat')
+         --output-frequency N     output the imatrix every N iterations (default: 10)
+         --save-frequency N       save an imatrix copy every N iterations (default: 0)
+         --process-output         collect data for the output tensor (default: false)
+         --no-ppl                 do not compute perplexity (default: true)
+         --chunk N                start processing the input from chunk N (default: 0)
+
+bench:
+
+  -pps                            is the prompt shared across parallel sequences (default: false)
+  -npp n0,n1,...                  number of prompt tokens
+  -ntg n0,n1,...                  number of text generation tokens
+  -npl n0,n1,...                  number of parallel prompts
+
+embedding:
+
+         --embd-normalize         normalisation for embendings (default: 2) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
+         --embd-output-format     empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
+         --embd-separator         separator of embendings (default \n) for example "<#sep#>"
+
+server:
+
+         --host HOST              ip address to listen (default: 127.0.0.1)
+         --port PORT              port to listen (default: 8080)
+         --path PATH              path to serve static files from (default: )
+         --embedding(s)           enable embedding endpoint (default: disabled)
+         --api-key KEY            API key to use for authentication (default: none)
+         --api-key-file FNAME     path to file containing API keys (default: none)
+         --ssl-key-file FNAME     path to file a PEM-encoded SSL private key
+         --ssl-cert-file FNAME    path to file a PEM-encoded SSL certificate
+         --timeout N              server read/write timeout in seconds (default: 600)
+         --threads-http N         number of threads used to process HTTP requests (default: -1)
+         --system-prompt-file FNAME
+                                  set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications
+         --log-format {text,json} 
+                                  log output format: json or text (default: json)
+         --metrics                enable prometheus compatible metrics endpoint (default: disabled)
+         --no-slots               disables slots monitoring endpoint (default: enabled)
+         --slot-save-path PATH    path to save slot kv cache (default: disabled)
+         --chat-template JINJA_TEMPLATE
+                                  set custom jinja chat template (default: template taken from model's metadata)
+                                  only commonly used templates are accepted:
+                                  https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
+  -sps,  --slot-prompt-similarity SIMILARITY
+                                  how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)
+
+
+logging:
+
+         --simple-io              use basic IO for better compatibility in subprocesses and limited consoles
+  -ld,   --logdir LOGDIR          path under which to save YAML logs (no logging if unset)
+         --log-test               Run simple logging test
+         --log-disable            Disable trace logs
+         --log-enable             Enable trace logs
+         --log-file FNAME         Specify a log filename (without extension)
+         --log-new                Create a separate new log file on start. Each log file will have unique name: "<name>.<ID>.log"
+         --log-append             Don't truncate the old log file.
+
+cvector:
+
+  -o,    --output FNAME           output file (default: 'control_vector.gguf')
+         --positive-file FNAME    positive prompts file, one prompt per line (default: 'examples/cvector-generator/positive.txt')
+         --negative-file FNAME    negative prompts file, one prompt per line (default: 'examples/cvector-generator/negative.txt')
+         --pca-batch N            batch size used for PCA. Larger batch runs faster, but uses more memory (default: 100)
+         --pca-iter N             number of iterations used for PCA (default: 1000)
+         --method {pca,mean}      dimensionality reduction method to be used (default: pca)
+```
+
 
 ## Build
 

From 8fac431b0692e88cdc55250f29f8d4386be82c5d Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 15 Jul 2024 14:48:17 +0200
Subject: [PATCH 057/125] ggml : suppress unknown pragma 'GCC' on windows
 (#8460)

This commit adds a macro guard to pragma GCC to avoid the following
warning on windows:

```console
C:\llama.cpp\ggml\src\ggml-aarch64.c(17,9): warning C4068:
unknown pragma 'GCC' [C:\lama.cpp\build\ggml\src\ggml.vcxproj]
```
---
 ggml/src/ggml-aarch64.c | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ggml/src/ggml-aarch64.c b/ggml/src/ggml-aarch64.c
index 40838cf4ffdcf6..26535b1c432ba6 100644
--- a/ggml/src/ggml-aarch64.c
+++ b/ggml/src/ggml-aarch64.c
@@ -14,7 +14,9 @@
 
 #include "ggml-aarch64.h"
 
+#if defined(__GNUC__)
 #pragma GCC diagnostic ignored "-Woverlength-strings"
+#endif
 
 #define UNUSED GGML_UNUSED
 

From 4db8f60fe79a391e82b0464013ada123baced96a Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Mon, 15 Jul 2024 19:23:10 +0200
Subject: [PATCH 058/125] fix ci (#8494)

---
 examples/server/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index d7487ec1b5b7e8..e477d1501f976f 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -211,7 +211,7 @@ retrieval:
 
          --context-file FNAME     file to load context from (repeat to specify multiple files)
          --chunk-size N           minimum length of embedded text chunks (default: 64)
-         --chunk-separator STRING 
+         --chunk-separator STRING
                                   separator between chunks (default: '
                                   ')
 
@@ -256,7 +256,7 @@ server:
          --threads-http N         number of threads used to process HTTP requests (default: -1)
          --system-prompt-file FNAME
                                   set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications
-         --log-format {text,json} 
+         --log-format {text,json}
                                   log output format: json or text (default: json)
          --metrics                enable prometheus compatible metrics endpoint (default: disabled)
          --no-slots               disables slots monitoring endpoint (default: enabled)

From 97bdd26eee11fe109dec00de75690ceef61c03f2 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Mon, 15 Jul 2024 20:50:47 +0200
Subject: [PATCH 059/125] Refactor lora adapter support (#8332)

* lora: load to devide buft

* add patch tensor function

* correct tensor patch

* llama_lora_adapter_apply

* correct ggml_backend_tensor_copy

* add llm_build_mm

* fix auto merge

* update based on review comments

* add convert script

* no more transpose A

* add f16 convert

* add metadata check

* add sanity check

* fix ftype

* add requirements

* fix requirements

* fix outfile

* conversion: only allow selected models

* fix types

* cuda : do not use dmmv if the tensor does not have enough cols

* llama : lora fixes

* do not disable mmap with lora

Co-authored-by: slaren <slarengh@gmail.com>

* llm_build_lora_mm_id

* convert_lora : MoE LoRA conversion support

* convert_lora : prefer safetensors, similarly to convert_hf

* convert_hf : simplify modify_tensors for InternLM2

* convert_lora : lazy conversion

* llama : load and use alpha from LoRA adapters

* llama : use llm_build_lora_mm in most model graphs

* auto scale

* Revert "auto scale"

This reverts commit 42415a4874e0f963e4aca6796ea5dfb97cd17464.

* remove redundant params

* Apply suggestions from code review

Co-authored-by: slaren <slarengh@gmail.com>

* change kv metadata

* move add_type to __init__

* convert_hf : move add_type to main()

* convert_lora : use the GGUFWriter from Model instead of overwriting it

---------

Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Francis Couture-Harpin <git@compilade.net>
---
 common/common.cpp                             |  13 +-
 convert_hf_to_gguf.py                         |  34 +-
 convert_lora_to_gguf.py                       | 374 +++++++
 ggml/src/ggml-cuda.cu                         |   3 +-
 ggml/src/ggml.c                               |   4 +-
 gguf-py/gguf/constants.py                     |  10 +
 gguf-py/gguf/gguf_writer.py                   |   3 +
 gguf-py/gguf/quants.py                        |   2 +-
 include/llama.h                               |  37 +-
 requirements.txt                              |   1 +
 .../requirements-convert_lora_to_gguf.txt     |   2 +
 src/llama.cpp                                 | 972 +++++++++---------
 12 files changed, 944 insertions(+), 511 deletions(-)
 create mode 100755 convert_lora_to_gguf.py
 create mode 100644 requirements/requirements-convert_lora_to_gguf.txt

diff --git a/common/common.cpp b/common/common.cpp
index 9035c359267a7e..dbb724fbbbcffb 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -685,7 +685,6 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
     if (arg == "--lora") {
         CHECK_ARG
         params.lora_adapter.emplace_back(argv[i], 1.0f);
-        params.use_mmap = false;
         return true;
     }
     if (arg == "--lora-scaled") {
@@ -693,7 +692,6 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         const char* lora_adapter = argv[i];
         CHECK_ARG
         params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
-        params.use_mmap = false;
         return true;
     }
     if (arg == "--lora-base") {
@@ -2089,19 +2087,14 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
     for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
         const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]);
         float lora_scale = std::get<1>(params.lora_adapter[i]);
-        int err = llama_model_apply_lora_from_file(model,
-                                             lora_adapter.c_str(),
-                                             lora_scale,
-                                             ((i > 0) || params.lora_base.empty())
-                                                ? NULL
-                                                : params.lora_base.c_str(),
-                                             params.n_threads);
-        if (err != 0) {
+        auto adapter = llama_lora_adapter_init(model, lora_adapter.c_str());
+        if (adapter == nullptr) {
             fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
             llama_free(lctx);
             llama_free_model(model);
             return std::make_tuple(nullptr, nullptr);
         }
+        llama_lora_adapter_set(lctx, adapter, lora_scale);
     }
 
     if (params.ignore_eos) {
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 42dace219f20fc..a755b0a60bf0a7 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2264,13 +2264,6 @@ def set_vocab(self):
 
         special_vocab.add_to_gguf(self.gguf_writer)
 
-    def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int):
-        if n_head_kv is not None and n_head != n_head_kv:
-            n_head = n_head_kv
-        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                .swapaxes(1, 2)
-                .reshape(weights.shape))
-
     def set_gguf_parameters(self):
         self.gguf_writer.add_name("InternLM2")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
@@ -2290,26 +2283,22 @@ def set_gguf_parameters(self):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         num_heads = self.hparams["num_attention_heads"]
         num_kv_heads = self.hparams["num_key_value_heads"]
-        hidden_size = self.hparams["hidden_size"]
+        n_embd = self.hparams["hidden_size"]
         q_per_kv = num_heads // num_kv_heads
-        head_dim = hidden_size // num_heads
+        head_dim = n_embd // num_heads
         num_groups = num_heads // q_per_kv
 
-        qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv"
-
-        if re.match(qkv_pattern, name):
-            bid = re.findall(qkv_pattern, name)[0]
+        if bid is not None and f"model.layers.{bid}.attention.wqkv" in name:
             qkv = data_torch
-            # qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim)
-            qkv = qkv.T.reshape((-1, num_groups, q_per_kv + 2, head_dim))
-            q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :]
+
+            qkv = qkv.reshape((num_groups, q_per_kv + 2, head_dim, n_embd))
+            q, k, v = qkv[:, : q_per_kv], qkv[:, -2], qkv[:, -1]
+
             # The model weights of q and k equire additional reshape.
-            # q = self._hf_permute_qk(rearrange(q, " o g n i ->  o (g n i)").T, num_heads, num_heads)
-            q = self._hf_permute_qk(q.reshape((q.shape[0], -1)).T, num_heads, num_heads)
-            # k = self._hf_permute_qk(rearrange(k, " o g n i ->  o (g n i)").T, num_heads, num_kv_heads)
-            k = self._hf_permute_qk(k.reshape((k.shape[0], -1)).T, num_heads, num_kv_heads)
-            # v = rearrange(v, " o g n i ->  o (g n i)").T
-            v = v.reshape((v.shape[0], -1)).T
+            q = LlamaModel.permute(q.reshape((-1, q.shape[-1])), num_heads, num_heads)
+            k = LlamaModel.permute(k.reshape((-1, k.shape[-1])), num_heads, num_kv_heads)
+            v = v.reshape((-1, v.shape[-1]))
+
             return [
                 (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), q),
                 (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), k),
@@ -3585,6 +3574,7 @@ def main() -> None:
                                      small_first_shard=args.no_tensor_first_split)
 
         logger.info("Set model parameters")
+        model_instance.gguf_writer.add_type(gguf.GGUFType.MODEL)
         model_instance.set_gguf_parameters()
 
         logger.info("Set model tokenizer")
diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py
new file mode 100755
index 00000000000000..4bb939d45d6bd4
--- /dev/null
+++ b/convert_lora_to_gguf.py
@@ -0,0 +1,374 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import logging
+import argparse
+import os
+import sys
+import json
+from math import prod
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Sequence, SupportsIndex, cast
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+import gguf
+
+# reuse model definitions from convert_hf_to_gguf.py
+from convert_hf_to_gguf import LazyTorchTensor, Model
+
+logger = logging.getLogger("lora-to-gguf")
+
+
+@dataclass
+class PartialLoraTensor:
+    A: Tensor | None = None
+    B: Tensor | None = None
+
+
+# magic to support tensor shape modifications and splitting
+class LoraTorchTensor:
+    _lora_A: Tensor  # (n_rank, row_size)
+    _lora_B: Tensor  # (col_size, n_rank)
+    _rank: int
+
+    def __init__(self, A: Tensor, B: Tensor):
+        assert len(A.shape) == len(B.shape)
+        assert A.shape[-2] == B.shape[-1]
+        if A.dtype != B.dtype:
+            A = A.to(torch.float32)
+            B = B.to(torch.float32)
+        self._lora_A = A
+        self._lora_B = B
+        self._rank = B.shape[-1]
+
+    def get_lora_A_B(self) -> tuple[Tensor, Tensor]:
+        return (self._lora_A, self._lora_B)
+
+    def __getitem__(
+        self,
+        indices: (
+            SupportsIndex
+            | slice
+            | tuple[SupportsIndex | slice | Tensor, ...]  # TODO: add ellipsis in the type signature
+        ),
+    ) -> LoraTorchTensor:
+        shape = self.shape
+        if isinstance(indices, SupportsIndex):
+            if len(shape) > 2:
+                return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices])
+            else:
+                raise NotImplementedError  # can't return a vector
+        elif isinstance(indices, slice):
+            if len(shape) > 2:
+                return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices])
+            else:
+                return LoraTorchTensor(self._lora_A, self._lora_B[indices])
+        elif isinstance(indices, tuple):
+            assert len(indices) > 0
+            if indices[-1] is Ellipsis:
+                return self[indices[:-1]]
+            # expand ellipsis
+            indices = tuple(
+                u
+                for v in (
+                    (
+                        (slice(None, None) for _ in range(len(indices) - 1))
+                        if i is Ellipsis
+                        else (i,)
+                    )
+                    for i in indices
+                )
+                for u in v
+            )
+
+            if len(indices) < len(shape):
+                indices = (*indices, *(slice(None, None) for _ in range(len(indices), len(shape))))
+
+            # TODO: make sure this is correct
+            indices_A = (
+                *(
+                    (
+                        j.__index__() % self._lora_A.shape[i]
+                        if isinstance(j, SupportsIndex)
+                        else slice(None, None)
+                    )
+                    for i, j in enumerate(indices[:-2])
+                ),
+                slice(None, None),
+                indices[-1],
+            )
+            indices_B = indices[:-1]
+            return LoraTorchTensor(self._lora_A[indices_A], self._lora_B[indices_B])
+        else:
+            raise NotImplementedError  # unknown indice type
+
+    @property
+    def dtype(self) -> torch.dtype:
+        assert self._lora_A.dtype == self._lora_B.dtype
+        return self._lora_A.dtype
+
+    @property
+    def shape(self) -> tuple[int, ...]:
+        assert len(self._lora_A.shape) == len(self._lora_B.shape)
+        return (*self._lora_B.shape[:-1], self._lora_A.shape[-1])
+
+    def size(self, dim=None):
+        assert dim is None
+        return self.shape
+
+    def reshape(self, *shape: int | tuple[int, ...]) -> LoraTorchTensor:
+        if isinstance(shape[0], tuple):
+            new_shape: tuple[int, ...] = shape[0]
+        else:
+            new_shape = cast(tuple[int, ...], shape)
+        orig_shape = self.shape
+        if len(new_shape) < 2:
+            raise NotImplementedError  # can't become a vector
+
+        # expand -1 in the shape
+        if any(dim == -1 for dim in new_shape):
+            n_elems = prod(orig_shape)
+            n_new_elems = prod(dim if dim != -1 else 1 for dim in new_shape)
+            assert n_elems % n_new_elems == 0
+            new_shape = (*(dim if dim != -1 else n_elems // n_new_elems for dim in new_shape),)
+
+        if new_shape[-1] != orig_shape[-1]:
+            raise NotImplementedError  # can't reshape the row size trivially
+
+        shape_A = (*(1 for _ in new_shape[:-2]), self._rank, orig_shape[-1])
+        shape_B = (*new_shape[:-1], self._rank)
+        return LoraTorchTensor(
+            self._lora_A.reshape(shape_A),
+            self._lora_B.reshape(shape_B),
+        )
+
+    def reshape_as(self, other: Tensor) -> LoraTorchTensor:
+        return self.reshape(*other.shape)
+
+    def view(self, *size: int) -> LoraTorchTensor:
+        return self.reshape(*size)
+
+    def permute(self, *dims: int) -> LoraTorchTensor:
+        shape = self.shape
+        dims = tuple(dim - len(shape) if dim >= 0 else dim for dim in dims)
+        if dims[-1] == -1:
+            # TODO: support higher dimensional A shapes bigger than 1
+            assert all(dim == 1 for dim in self._lora_A.shape[:-2])
+            return LoraTorchTensor(self._lora_A, self._lora_B.permute(*dims))
+        if len(shape) == 2 and dims[-1] == -2 and dims[-2] == -1:
+            return LoraTorchTensor(self._lora_B.permute(*dims), self._lora_A.permute(*dims))
+        else:
+            # TODO: compose the above two
+            raise NotImplementedError
+
+    def transpose(self, dim0: int, dim1: int) -> LoraTorchTensor:
+        shape = self.shape
+        dims = [i for i in range(len(shape))]
+        dims[dim0], dims[dim1] = dims[dim1], dims[dim0]
+        return self.permute(*dims)
+
+    def swapaxes(self, axis0: int, axis1: int) -> LoraTorchTensor:
+        return self.transpose(axis0, axis1)
+
+    def to(self, *args, **kwargs):
+        return LoraTorchTensor(self._lora_A.to(*args, **kwargs), self._lora_B.to(*args, **kwargs))
+
+    @classmethod
+    def __torch_function__(cls, func: Callable, types, args=(), kwargs=None):
+        del types  # unused
+
+        if kwargs is None:
+            kwargs = {}
+
+        if func is torch.permute:
+            return type(args[0]).permute(*args, **kwargs)
+        elif func is torch.reshape:
+            return type(args[0]).reshape(*args, **kwargs)
+        elif func is torch.stack:
+            assert isinstance(args[0], Sequence)
+            dim = kwargs.get("dim", 0)
+            assert dim == 0
+            return LoraTorchTensor(
+                torch.stack([a._lora_A for a in args[0]], dim),
+                torch.stack([b._lora_B for b in args[0]], dim),
+            )
+        elif func is torch.cat:
+            assert isinstance(args[0], Sequence)
+            dim = kwargs.get("dim", 0)
+            assert dim == 0
+            if len(args[0][0].shape) > 2:
+                return LoraTorchTensor(
+                    torch.cat([a._lora_A for a in args[0]], dim),
+                    torch.cat([b._lora_B for b in args[0]], dim),
+                )
+            elif all(torch.equal(args[0][0]._lora_A, t._lora_A) for t in args[0][1:]):
+                return LoraTorchTensor(
+                    args[0][0]._lora_A,
+                    torch.cat([b._lora_B for b in args[0]], dim),
+                )
+            else:
+                raise NotImplementedError
+        else:
+            raise NotImplementedError
+
+
+def get_base_tensor_name(lora_tensor_name: str) -> str:
+    base_name = lora_tensor_name.replace("base_model.model.", "")
+    base_name = base_name.replace(".lora_A.weight", ".weight")
+    base_name = base_name.replace(".lora_B.weight", ".weight")
+    return base_name
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Convert a huggingface PEFT LoRA adapter to a GGML compatible file")
+    parser.add_argument(
+        "--outfile", type=Path,
+        help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
+    )
+    parser.add_argument(
+        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
+        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
+    )
+    parser.add_argument(
+        "--bigendian", action="store_true",
+        help="model is executed on big endian machine",
+    )
+    parser.add_argument(
+        "--no-lazy", action="store_true",
+        help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)",
+    )
+    parser.add_argument(
+        "--verbose", action="store_true",
+        help="increase output verbosity",
+    )
+    parser.add_argument(
+        "--base", type=Path, required=True,
+        help="directory containing base model file",
+    )
+    parser.add_argument(
+        "lora_path", type=Path,
+        help="directory containing LoRA adapter file",
+    )
+
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
+    ftype_map: dict[str, gguf.LlamaFileType] = {
+        "f32": gguf.LlamaFileType.ALL_F32,
+        "f16": gguf.LlamaFileType.MOSTLY_F16,
+        "bf16": gguf.LlamaFileType.MOSTLY_BF16,
+        "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
+        "auto": gguf.LlamaFileType.GUESSED,
+    }
+
+    ftype = ftype_map[args.outtype]
+
+    dir_base_model: Path = args.base
+    dir_lora: Path = args.lora_path
+    lora_config = dir_lora / "adapter_config.json"
+    input_model = dir_lora / "adapter_model.safetensors"
+
+    if args.outfile is not None:
+        fname_out = args.outfile
+    else:
+        # output in the same directory as the model by default
+        fname_out = dir_lora / 'ggml-lora-{ftype}.gguf'
+
+    if os.path.exists(input_model):
+        # lazy import load_file only if lora is in safetensors format.
+        from safetensors.torch import load_file
+
+        lora_model = load_file(input_model, device="cpu")
+    else:
+        input_model = os.path.join(dir_lora, "adapter_model.bin")
+        lora_model = torch.load(input_model, map_location="cpu", weights_only=True)
+
+    # load base model
+    logger.info(f"Loading base model: {dir_base_model.name}")
+    hparams = Model.load_hparams(dir_base_model)
+    with torch.inference_mode():
+        try:
+            model_class = Model.from_model_architecture(hparams["architectures"][0])
+        except NotImplementedError:
+            logger.error(f"Model {hparams['architectures'][0]} is not supported")
+            sys.exit(1)
+
+        class LoraModel(model_class):
+            model_arch = model_class.model_arch
+
+            def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
+                tensor_map: dict[str, PartialLoraTensor] = {}
+
+                for name, tensor in lora_model.items():
+                    if self.lazy:
+                        tensor = LazyTorchTensor.from_eager(tensor)
+                    base_name = get_base_tensor_name(name)
+                    is_lora_a = ".lora_A.weight" in name
+                    is_lora_b = ".lora_B.weight" in name
+                    if not is_lora_a and not is_lora_b:
+                        if ".base_layer.weight" in name:
+                            continue
+                        logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
+                        sys.exit(1)
+
+                    if base_name in tensor_map:
+                        if is_lora_a:
+                            tensor_map[base_name].A = tensor
+                        else:
+                            tensor_map[base_name].B = tensor
+                    else:
+                        if is_lora_a:
+                            tensor_map[base_name] = PartialLoraTensor(A=tensor)
+                        else:
+                            tensor_map[base_name] = PartialLoraTensor(B=tensor)
+
+                for name, tensor in tensor_map.items():
+                    assert tensor.A is not None
+                    assert tensor.B is not None
+                    yield (name, cast(torch.Tensor, LoraTorchTensor(tensor.A, tensor.B)))
+
+            def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+                dest = super().modify_tensors(data_torch, name, bid)
+                for dest_name, dest_data in dest:
+                    assert isinstance(dest_data, LoraTorchTensor)
+                    lora_a, lora_b = dest_data.get_lora_A_B()
+
+                    yield (dest_name + ".lora_a", lora_a)
+                    yield (dest_name + ".lora_b", lora_b)
+
+        model_instance = LoraModel(
+            dir_base_model,
+            ftype,
+            fname_out,
+            is_big_endian=args.bigendian,
+            use_temp_file=False,
+            eager=args.no_lazy,
+            model_name=None,
+        )
+
+        with open(lora_config, "r") as f:
+            lparams: dict[str, Any] = json.load(f)
+
+        alpha = lparams["lora_alpha"]
+
+        model_instance.gguf_writer.add_string(gguf.Keys.General.TYPE, gguf.GGUFType.ADAPTER)
+        model_instance.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
+        model_instance.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, float(alpha))
+        model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
+        logger.info("Exporting model...")
+        model_instance.write()
+        logger.info(f"Model successfully exported to {model_instance.fname_out}")
diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu
index ed784ea1c20246..39e345b668bc11 100644
--- a/ggml/src/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -1876,7 +1876,8 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
 
     bool use_dequantize_mul_mat_vec = (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16)
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
-        && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src1->ne[1] == 1;
+        && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src0->ne[0] >= GGML_CUDA_DMMV_X*2
+        && src1->ne[1] == 1;
     bool          use_mul_mat_vec_q =  ggml_is_quantized(src0->type)
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
         && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 9a541478718474..60b3c5e7a12d37 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -19478,7 +19478,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
 
     fprintf(fp, "digraph G {\n");
     fprintf(fp, "  newrank = true;\n");
-    fprintf(fp, "  rankdir = LR;\n");
+    fprintf(fp, "  rankdir = TB;\n");
 
     for (int i = 0; i < gb->n_nodes; i++) {
         struct ggml_tensor * node = gb->nodes[i];
@@ -19540,7 +19540,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
         }
 
         fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]);
-        if (ggml_nelements(node) < 5) {
+        if (ggml_nelements(node) < 5 && node->data != NULL) {
             fprintf(fp, " | (");
             for (int j = 0; j < ggml_nelements(node); j++) {
                 if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) {
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index a95a44237e3482..5eb3df706e6e29 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -19,6 +19,7 @@
 
 class Keys:
     class General:
+        TYPE                 = "general.type"
         ARCHITECTURE         = "general.architecture"
         QUANTIZATION_VERSION = "general.quantization_version"
         ALIGNMENT            = "general.alignment"
@@ -120,11 +121,20 @@ class Tokenizer:
         MIDDLE_ID            = "tokenizer.ggml.middle_token_id"
         EOT_ID               = "tokenizer.ggml.eot_token_id"
 
+    class Adapter:
+        TYPE       = "adapter.type"
+        LORA_ALPHA = "adapter.lora.alpha"
+
 #
 # recommended mapping of model tensor names for storage in gguf
 #
 
 
+class GGUFType:
+    MODEL   = "model"
+    ADAPTER = "adapter"
+
+
 class MODEL_ARCH(IntEnum):
     LLAMA        = auto()
     FALCON       = auto()
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index cf955416290323..b0197961d46a83 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -424,6 +424,9 @@ def close(self) -> None:
                 fout.close()
             self.fout = None
 
+    def add_type(self, type_name: str) -> None:
+        self.add_string(Keys.General.TYPE, type_name)
+
     def add_architecture(self) -> None:
         self.add_string(Keys.General.ARCHITECTURE, self.arch)
 
diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py
index b22eec1661ce70..16e0a9aaa8a8be 100644
--- a/gguf-py/gguf/quants.py
+++ b/gguf-py/gguf/quants.py
@@ -43,7 +43,7 @@ def __apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.
         osize *= dim
     out = np.empty(shape=osize, dtype=otype)
     # compute over groups of 16 rows (arbitrary, but seems good for performance)
-    n_groups = rows.shape[0] // 16
+    n_groups = (rows.shape[0] // 16) or 1
     np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out)
     return out.reshape(oshape)
 
diff --git a/include/llama.h b/include/llama.h
index 3970c3aebcd623..c57d21f0c70b95 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -411,6 +411,9 @@ extern "C" {
         const char * content;
     } llama_chat_message;
 
+    // lora adapter
+    struct llama_lora_adapter;
+
     // Helpers for getting default parameters
     LLAMA_API struct llama_model_params llama_model_default_params(void);
     LLAMA_API struct llama_context_params llama_context_default_params(void);
@@ -510,18 +513,28 @@ extern "C" {
             const char * fname_out,
             const llama_model_quantize_params * params);
 
-    // Apply a LoRA adapter to a loaded model
-    // path_base_model is the path to a higher quality model to use as a base for
-    // the layers modified by the adapter. Can be NULL to use the current loaded model.
-    // The model needs to be reloaded before applying a new adapter, otherwise the adapter
-    // will be applied on top of the previous one
-    // Returns 0 on success
-    LLAMA_API int32_t llama_model_apply_lora_from_file(
-            const struct llama_model * model,
-                          const char * path_lora,
-                               float   scale,
-                          const char * path_base_model,
-                             int32_t   n_threads);
+    // Load a LoRA adapter from file
+    // The loaded adapter will be associated to the given model, and will be free when the model is deleted
+    LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init(
+            struct llama_model * model,
+            const char * path_lora);
+
+    // Add a loaded LoRA adapter to given context
+    // This will not modify model's weight
+    LLAMA_API int32_t llama_lora_adapter_set(
+            struct llama_context * ctx,
+            struct llama_lora_adapter * adapter,
+            float scale);
+
+    // Remove a LoRA adapter from given context
+    // Return -1 if the adapter is not present in the context
+    LLAMA_API int32_t llama_lora_adapter_remove(
+            struct llama_context * ctx,
+            struct llama_lora_adapter * adapter);
+
+    // Manually free a LoRA adapter
+    // Note: loaded adapters will be free when the associated model is deleted
+    LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter);
 
     // Apply a loaded control vector to a llama_context, or if data is NULL, clear
     // the currently loaded vector.
diff --git a/requirements.txt b/requirements.txt
index 52456c2e6fd24d..9e190ae27de389 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,3 +9,4 @@
 -r ./requirements/requirements-convert_hf_to_gguf.txt
 -r ./requirements/requirements-convert_hf_to_gguf_update.txt
 -r ./requirements/requirements-convert_llama_ggml_to_gguf.txt
+-r ./requirements/requirements-convert_lora_to_gguf.txt
diff --git a/requirements/requirements-convert_lora_to_gguf.txt b/requirements/requirements-convert_lora_to_gguf.txt
new file mode 100644
index 00000000000000..5758076c41dc11
--- /dev/null
+++ b/requirements/requirements-convert_lora_to_gguf.txt
@@ -0,0 +1,2 @@
+-r ./requirements-convert_hf_to_gguf.txt
+--extra-index-url https://download.pytorch.org/whl/cpu
diff --git a/src/llama.cpp b/src/llama.cpp
index ddf0262d443b06..07bb427135d8c5 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -287,6 +287,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
 };
 
 enum llm_kv {
+    LLM_KV_GENERAL_TYPE,
     LLM_KV_GENERAL_ARCHITECTURE,
     LLM_KV_GENERAL_QUANTIZATION_VERSION,
     LLM_KV_GENERAL_ALIGNMENT,
@@ -377,9 +378,13 @@ enum llm_kv {
     LLM_KV_TOKENIZER_SUFFIX_ID,
     LLM_KV_TOKENIZER_MIDDLE_ID,
     LLM_KV_TOKENIZER_EOT_ID,
+
+    LLM_KV_ADAPTER_TYPE,
+    LLM_KV_ADAPTER_LORA_ALPHA,
 };
 
 static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
+    { LLM_KV_GENERAL_TYPE,                  "general.type"                          },
     { LLM_KV_GENERAL_ARCHITECTURE,          "general.architecture"                  },
     { LLM_KV_GENERAL_QUANTIZATION_VERSION,  "general.quantization_version"          },
     { LLM_KV_GENERAL_ALIGNMENT,             "general.alignment"                     },
@@ -470,6 +475,9 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_TOKENIZER_SUFFIX_ID,            "tokenizer.ggml.suffix_token_id"          },
     { LLM_KV_TOKENIZER_MIDDLE_ID,            "tokenizer.ggml.middle_token_id"          },
     { LLM_KV_TOKENIZER_EOT_ID,               "tokenizer.ggml.eot_token_id"             },
+
+    { LLM_KV_ADAPTER_TYPE,                  "adapter.type"       },
+    { LLM_KV_ADAPTER_LORA_ALPHA,            "adapter.lora.alpha" },
 };
 
 struct LLM_KV {
@@ -2703,6 +2711,9 @@ struct llama_model {
     int64_t t_load_us = 0;
     int64_t t_start_us = 0;
 
+    // keep track of loaded lora adapters
+    std::set<struct llama_lora_adapter *> lora_adapters;
+
     ~llama_model() {
         for (struct ggml_context * ctx : ctxs) {
             ggml_free(ctx);
@@ -2715,6 +2726,9 @@ struct llama_model {
 #endif
             ggml_backend_buffer_free(buf);
         }
+        while (!lora_adapters.empty()) {
+            llama_lora_adapter_free(*lora_adapters.begin());
+        }
     }
 };
 
@@ -2819,6 +2833,52 @@ struct llama_context {
 
     // control vectors
     struct llama_control_vector cvec;
+
+    // lora adapters and scales
+    std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;
+};
+
+struct llama_lora_weight {
+    struct ggml_tensor * a = nullptr;
+    struct ggml_tensor * b = nullptr;
+    llama_lora_weight() = default;
+    llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b): a(a), b(b) {}
+};
+
+struct llama_lora_adapter {
+    struct llama_model * base_model;
+    // map tensor name to lora_a_b
+    std::unordered_map<std::string, struct llama_lora_weight> ab_map;
+    std::vector<struct ggml_context *> ctxs;
+    std::vector<ggml_backend_buffer_t> bufs;
+
+    float alpha;
+
+    llama_lora_adapter(struct llama_model * base_model): base_model(base_model) {
+        base_model->lora_adapters.insert(this);
+    }
+
+    llama_lora_weight * get_weight(struct ggml_tensor * w) {
+        std::string name(w->name);
+        auto pos = ab_map.find(name);
+        if (ab_map.find(name) != ab_map.end()) {
+            return &pos->second;
+        }
+        return nullptr;
+    }
+
+    ~llama_lora_adapter() {
+        for (struct ggml_context * ctx : ctxs) {
+            ggml_free(ctx);
+        }
+        for (ggml_backend_buffer_t buf : bufs) {
+            ggml_backend_buffer_free(buf);
+        }
+        auto pos = base_model->lora_adapters.find(this);
+        if (pos != base_model->lora_adapters.end()) {
+            base_model->lora_adapters.erase(pos);
+        }
+    }
 };
 
 static size_t llama_get_device_count(const llama_model & model) {
@@ -7809,6 +7869,58 @@ static void llm_build_kv_store(
     ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur, v_cache_view));
 }
 
+// do mat_mul, while optionally apply lora
+static struct ggml_tensor * llm_build_lora_mm(
+        struct llama_context & lctx,
+         struct ggml_context * ctx0,
+          struct ggml_tensor * w,
+          struct ggml_tensor * cur) {
+    struct ggml_tensor * res = ggml_mul_mat(ctx0, w, cur);
+    for (auto & it : lctx.lora_adapters) {
+        struct llama_lora_weight * lora = it.first->get_weight(w);
+        if (lora == nullptr) {
+            continue;
+        }
+        const float alpha = it.first->alpha;
+        const float rank  = (float) lora->b->ne[0];
+        const float scale = alpha ? it.second * alpha / rank : it.second;
+        struct ggml_tensor * ab_cur = ggml_mul_mat(
+            ctx0, lora->b,
+            ggml_mul_mat(ctx0, lora->a, cur)
+        );
+        ab_cur = ggml_scale(ctx0, ab_cur, scale);
+        res = ggml_add(ctx0, res, ab_cur);
+    }
+    return res;
+}
+
+// do mat_mul_id, while optionally apply lora
+static struct ggml_tensor * llm_build_lora_mm_id(
+        struct llama_context & lctx,
+         struct ggml_context * ctx0,
+          struct ggml_tensor * w,   // struct ggml_tensor * as
+          struct ggml_tensor * cur, // struct ggml_tensor * b
+          struct ggml_tensor * ids) {
+    struct ggml_tensor * res = ggml_mul_mat_id(ctx0, w, cur, ids);
+    for (auto & it : lctx.lora_adapters) {
+        struct llama_lora_weight * lora = it.first->get_weight(w);
+        if (lora == nullptr) {
+            continue;
+        }
+        const float alpha = it.first->alpha;
+        const float rank  = (float) lora->b->ne[0];
+        const float scale = alpha ? it.second * alpha / rank : it.second;
+        struct ggml_tensor * ab_cur = ggml_mul_mat_id(
+            ctx0, lora->b,
+            ggml_mul_mat_id(ctx0, lora->a, cur, ids),
+            ids
+        );
+        ab_cur = ggml_scale(ctx0, ab_cur, scale);
+        res = ggml_add(ctx0, res, ab_cur);
+    }
+    return res;
+}
+
 static struct ggml_tensor * llm_build_norm(
         struct ggml_context * ctx,
          struct ggml_tensor * cur,
@@ -7843,6 +7955,7 @@ static struct ggml_tensor * llm_build_norm(
 
 static struct ggml_tensor * llm_build_ffn(
         struct ggml_context * ctx,
+       struct llama_context & lctx,
          struct ggml_tensor * cur,
          struct ggml_tensor * up,
          struct ggml_tensor * up_b,
@@ -7858,7 +7971,7 @@ static struct ggml_tensor * llm_build_ffn(
           llm_ffn_gate_type   type_gate,
          const llm_build_cb & cb,
                         int   il) {
-    struct ggml_tensor * tmp = up ? ggml_mul_mat(ctx, up, cur) : cur;
+    struct ggml_tensor * tmp = up ? llm_build_lora_mm(lctx, ctx, up, cur) : cur;
     cb(tmp, "ffn_up", il);
 
     if (up_b) {
@@ -7875,12 +7988,12 @@ static struct ggml_tensor * llm_build_ffn(
         switch (type_gate) {
             case LLM_FFN_SEQ:
                 {
-                    cur = ggml_mul_mat(ctx, gate, tmp);
+                    cur = llm_build_lora_mm(lctx, ctx, gate, tmp);
                     cb(cur, "ffn_gate", il);
                 } break;
             case LLM_FFN_PAR:
                 {
-                    cur = ggml_mul_mat(ctx, gate, cur);
+                    cur = llm_build_lora_mm(lctx, ctx, gate, cur);
                     cb(cur, "ffn_gate", il);
                 } break;
         }
@@ -7948,7 +8061,7 @@ static struct ggml_tensor * llm_build_ffn(
     }
 
     if (down) {
-        cur = ggml_mul_mat(ctx, down, cur);
+        cur = llm_build_lora_mm(lctx, ctx, down, cur);
     }
 
     if (down_b) {
@@ -7969,6 +8082,7 @@ static struct ggml_tensor * llm_build_ffn(
 
 static struct ggml_tensor * llm_build_moe_ffn(
         struct ggml_context * ctx,
+       struct llama_context & lctx,
          struct ggml_tensor * cur,
          struct ggml_tensor * gate_inp,
          struct ggml_tensor * up_exps,
@@ -7985,7 +8099,7 @@ static struct ggml_tensor * llm_build_moe_ffn(
     int64_t n_embd = cur->ne[0];
     int64_t n_tokens = cur->ne[1];
 
-    ggml_tensor * logits = ggml_mul_mat(ctx, gate_inp, cur); // [n_expert, n_tokens]
+    ggml_tensor * logits = llm_build_lora_mm(lctx, ctx, gate_inp, cur); // [n_expert, n_tokens]
     cb(logits, "ffn_moe_logits", il);
 
     ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
@@ -8017,10 +8131,10 @@ static struct ggml_tensor * llm_build_moe_ffn(
     }
 
     cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
-    ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+    ggml_tensor * up = llm_build_lora_mm_id(lctx, ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
     cb(up, "ffn_moe_up", il);
 
-    ggml_tensor * gate = ggml_mul_mat_id(ctx, gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+    ggml_tensor * gate = llm_build_lora_mm_id(lctx, ctx, gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
     cb(gate, "ffn_moe_gate", il);
 
     switch (type_op) {
@@ -8041,7 +8155,7 @@ static struct ggml_tensor * llm_build_moe_ffn(
     ggml_tensor * par = ggml_mul(ctx, up, gate); // [n_ff, n_expert_used, n_tokens]
     cb(par, "ffn_moe_gate_par", il);
 
-    ggml_tensor * experts = ggml_mul_mat_id(ctx, down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
+    ggml_tensor * experts = llm_build_lora_mm_id(lctx, ctx, down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
     cb(experts, "ffn_moe_down", il);
 
     experts = ggml_mul(ctx, experts, weights);
@@ -8069,9 +8183,7 @@ static struct ggml_tensor * llm_build_moe_ffn(
 
 static struct ggml_tensor * llm_build_kqv(
         struct ggml_context * ctx,
-          const llama_model & model,
-        const llama_hparams & hparams,
-        const llama_cparams & cparams,
+       struct llama_context & lctx,
        const llama_kv_cache & kv,
          struct ggml_cgraph * graph,
          struct ggml_tensor * wo,
@@ -8083,6 +8195,10 @@ static struct ggml_tensor * llm_build_kqv(
                     float     kq_scale,
          const llm_build_cb & cb,
                     int       il) {
+    const llama_model   & model   = lctx.model;
+    const llama_hparams & hparams = lctx.model.hparams;
+    const llama_cparams & cparams = lctx.cparams;
+
     const int64_t n_ctx         = cparams.n_ctx;
     const int64_t n_head        = hparams.n_head(il);
     const int64_t n_head_kv     = hparams.n_head_kv(il);
@@ -8181,7 +8297,7 @@ static struct ggml_tensor * llm_build_kqv(
     ggml_build_forward_expand(graph, cur);
 
     if (wo) {
-        cur = ggml_mul_mat(ctx, wo, cur);
+        cur = llm_build_lora_mm(lctx, ctx, wo, cur);
     }
 
     if (wo_b) {
@@ -8197,9 +8313,7 @@ static struct ggml_tensor * llm_build_kqv(
 
 static struct ggml_tensor * llm_build_kv(
         struct ggml_context * ctx,
-          const llama_model & model,
-        const llama_hparams & hparams,
-        const llama_cparams & cparams,
+       struct llama_context & lctx,
        const llama_kv_cache & kv,
          struct ggml_cgraph * graph,
          struct ggml_tensor * wo,
@@ -8214,6 +8328,8 @@ static struct ggml_tensor * llm_build_kv(
                     float     kq_scale,
          const llm_build_cb & cb,
                     int       il) {
+    const llama_hparams & hparams = lctx.model.hparams;
+    const llama_cparams & cparams = lctx.cparams;
 
     // these nodes are added to the graph together so that they are not reordered
     // by doing so, the number of splits in the graph is reduced
@@ -8225,7 +8341,7 @@ static struct ggml_tensor * llm_build_kv(
 
     struct ggml_tensor * cur;
 
-    cur  = llm_build_kqv(ctx, model, hparams, cparams, kv, graph, wo, wo_b,
+    cur  = llm_build_kqv(ctx, lctx, kv, graph, wo, wo_b,
             q_cur, kq_mask, n_tokens, n_kv, kq_scale, cb, il);
     cb(cur, "kqv_out", il);
 
@@ -8687,21 +8803,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
@@ -8722,7 +8838,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -8745,7 +8861,7 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -8759,7 +8875,7 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_moe_ffn(ctx0, cur,
+                cur = llm_build_moe_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_gate_inp,
                         model.layers[il].ffn_up_exps,
                         model.layers[il].ffn_gate_exps,
@@ -8789,7 +8905,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -8825,13 +8941,13 @@ struct llm_build_context {
 
             // self-attention
             {
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
                 switch (model.type) {
@@ -8857,7 +8973,7 @@ struct llm_build_context {
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -8879,7 +8995,7 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -8904,7 +9020,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -8940,13 +9056,13 @@ struct llm_build_context {
 
             // self-attention
             {
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
                 Qcur = ggml_rope_ext(
@@ -8962,7 +9078,7 @@ struct llm_build_context {
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -8984,7 +9100,7 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -9007,7 +9123,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9056,7 +9172,7 @@ struct llm_build_context {
                     cur = attn_norm;
                 }
 
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
@@ -9083,7 +9199,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9100,7 +9216,7 @@ struct llm_build_context {
 
             // feed forward
             {
-                cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result
+                cur = llm_build_ffn(ctx0, lctx, attn_norm, // !! use the attn norm, not the result
                         model.layers[il].ffn_up,   NULL, NULL,
                         NULL,                      NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -9127,7 +9243,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9172,21 +9288,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
@@ -9207,7 +9323,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
@@ -9239,7 +9355,7 @@ struct llm_build_context {
                     LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_moe_ffn(ctx0, cur,
+            cur = llm_build_moe_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_gate_inp,
                     model.layers[il].ffn_up_exps,
                     model.layers[il].ffn_gate_exps,
@@ -9278,7 +9394,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
 
         // Grok
         // multiply logits by output_multiplier_scale of 0.5773502691896257
@@ -9329,7 +9445,7 @@ struct llm_build_context {
                 struct ggml_tensor * Kcur = nullptr;
                 struct ggml_tensor * Vcur = nullptr;
 
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
@@ -9357,7 +9473,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9380,7 +9496,7 @@ struct llm_build_context {
                                  LLM_NORM, cb, il);
             cb(cur, "attn_out_norm", il);
 
-            cur = llm_build_moe_ffn(ctx0, cur,
+            cur = llm_build_moe_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_gate_inp,
                     model.layers[il].ffn_up_exps,
                     model.layers[il].ffn_gate_exps,
@@ -9409,7 +9525,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
 
         cb(cur, "result_output", -1);
 
@@ -9451,7 +9567,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -9467,7 +9583,7 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9491,7 +9607,7 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -9514,7 +9630,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9546,13 +9662,13 @@ struct llm_build_context {
 
             // self-attention
             {
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
@@ -9561,7 +9677,7 @@ struct llm_build_context {
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                 cb(Qcur, "Qcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9583,7 +9699,7 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -9608,7 +9724,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9660,7 +9776,7 @@ struct llm_build_context {
 
             // self-attention
             if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_JINA_BERT_V2) {
-                Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
+                Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur), model.layers[il].bq);
                 cb(Qcur, "Qcur", il);
 
                 if (model.layers[il].attn_q_norm) {
@@ -9670,7 +9786,7 @@ struct llm_build_context {
                             LLM_NORM, cb, il);
                 }
 
-                Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
+                Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur), model.layers[il].bk);
                 cb(Kcur, "Kcur", il);
 
                 if (model.layers[il].attn_k_norm) {
@@ -9679,14 +9795,14 @@ struct llm_build_context {
                             model.layers[il].attn_k_norm_b,
                             LLM_NORM, cb, il);
                 }
-                Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
+                Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur), model.layers[il].bv);
                 cb(Vcur, "Vcur", il);
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
             } else {
                 // compute Q and K and RoPE them
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
@@ -9735,7 +9851,7 @@ struct llm_build_context {
 
             ggml_build_forward_expand(gf, cur);
 
-            cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+            cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
             if (model.layers[il].bo) {
                 cb(cur, "kqv_wo", il);
             }
@@ -9768,21 +9884,21 @@ struct llm_build_context {
 
             // feed-forward network
             if (model.arch == LLM_ARCH_BERT) {
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
             } else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL,                        NULL,
                         model.layers[il].ffn_gate, NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                         NULL,
                         LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
             } else {
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -9840,7 +9956,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -9856,7 +9972,7 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -9880,7 +9996,7 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -9903,7 +10019,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -9950,7 +10066,7 @@ struct llm_build_context {
             {
                 cur = attn_norm;
 
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 if (model.layers[il].bqkv){
@@ -9988,13 +10104,13 @@ struct llm_build_context {
                     Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                     Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
-                    cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                    cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                             model.layers[il].wo, model.layers[il].bo,
                             Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
                 } else {
                     Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                    cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                    cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                             model.layers[il].wo, model.layers[il].bo,
                             Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
                 }
@@ -10018,7 +10134,7 @@ struct llm_build_context {
                         model.layers[il].ffn_norm_b,
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -10043,7 +10159,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10083,21 +10199,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
@@ -10139,7 +10255,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -10167,7 +10283,7 @@ struct llm_build_context {
                     // parallel residual
                     cur = inpSA;
                 }
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -10193,7 +10309,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10228,7 +10344,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -10258,7 +10374,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -10280,7 +10396,7 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -10305,7 +10421,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10343,17 +10459,17 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                 cb(Vcur, "Vcur", il);
@@ -10372,7 +10488,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -10393,7 +10509,7 @@ struct llm_build_context {
                     LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
+            cur = llm_build_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_up,   NULL, NULL,
                     model.layers[il].ffn_gate, NULL, NULL,
                     model.layers[il].ffn_down, NULL, NULL,
@@ -10417,7 +10533,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10458,17 +10574,17 @@ struct llm_build_context {
             // self_attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                 cb(Vcur, "Vcur", il);
@@ -10487,7 +10603,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -10510,7 +10626,7 @@ struct llm_build_context {
             cb(cur, "ffn_norm", il);
 
             ggml_tensor * moe_out =
-                    llm_build_moe_ffn(ctx0, cur,
+                    llm_build_moe_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_gate_inp,
                         model.layers[il].ffn_up_exps,
                         model.layers[il].ffn_gate_exps,
@@ -10523,14 +10639,14 @@ struct llm_build_context {
 
             // FFN shared expert
             {
-                ggml_tensor * cur_gate_inp = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp_shexp, cur);
+                ggml_tensor * cur_gate_inp = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_gate_inp_shexp, cur);
                 cb(cur_gate_inp, "ffn_shexp_gate_inp", il);
 
                 // sigmoid
                 ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp);
                 cb(cur_gate, "ffn_shexp_gate", il);
 
-                ggml_tensor * cur_ffn = llm_build_ffn(ctx0, cur,
+                ggml_tensor * cur_ffn = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up_shexp,   NULL, NULL,
                         model.layers[il].ffn_gate_shexp, NULL, NULL,
                         model.layers[il].ffn_down_shexp, NULL, NULL,
@@ -10563,7 +10679,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10605,7 +10721,7 @@ struct llm_build_context {
                 struct ggml_tensor * Vcur = nullptr;
 
                 if (model.layers[il].wqkv) {
-                    cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
+                    cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, attn_norm_output);
                     cb(cur, "wqkv", il);
 
                     cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -10615,9 +10731,9 @@ struct llm_build_context {
                     Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                     Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
                 } else {
-                    Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
-                    Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
-                    Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+                    Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+                    Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+                    Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
                 }
 
                 cb(Qcur, "Qcur", il);
@@ -10644,7 +10760,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
@@ -10659,7 +10775,7 @@ struct llm_build_context {
 
             // FF
             {
-                ffn_output = llm_build_ffn(ctx0, attn_norm_output,
+                ffn_output = llm_build_ffn(ctx0, lctx, attn_norm_output,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -10683,7 +10799,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output_no_bias", -1);
 
         cur = ggml_add(ctx0, cur, model.output_b);
@@ -10729,7 +10845,7 @@ struct llm_build_context {
                 struct ggml_tensor * Vcur = nullptr;
 
                 if (model.layers[il].wqkv) {
-                    cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
+                    cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, attn_norm_output);
                     cb(cur, "wqkv", il);
 
                     Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0 * sizeof(float) * (n_embd)));
@@ -10737,9 +10853,9 @@ struct llm_build_context {
                     Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)));
                 }
                 else {
-                    Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
-                    Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
-                    Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+                    Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+                    Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+                    Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
                 }
 
                 cb(Qcur, "Qcur", il);
@@ -10764,7 +10880,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
@@ -10788,7 +10904,7 @@ struct llm_build_context {
             // special-case: the up and gate tensors are merged into a single tensor
             // TOOD: support into llm_build_ffn
             {
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         NULL,                      NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -10811,7 +10927,7 @@ struct llm_build_context {
             LLM_NORM_RMS, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10851,13 +10967,13 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
                 Qcur = ggml_rope_ext(
@@ -10872,7 +10988,7 @@ struct llm_build_context {
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -10890,7 +11006,7 @@ struct llm_build_context {
 
             // feed-forward network
             {
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -10916,7 +11032,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10958,7 +11074,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -10974,7 +11090,7 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -10998,7 +11114,7 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -11021,7 +11137,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -11057,7 +11173,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -11085,7 +11201,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -11109,7 +11225,7 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -11132,7 +11248,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -11170,21 +11286,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 // if (model.layers[il].bq) {
                 //     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                 //     cb(Qcur, "Qcur", il);
                 // }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 // if (model.layers[il].bk) {
                 //     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                 //     cb(Kcur, "Kcur", il);
                 // }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 // if (model.layers[il].bv) {
                 //     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
@@ -11205,7 +11321,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -11226,7 +11342,7 @@ struct llm_build_context {
                     LLM_NORM, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
+            cur = llm_build_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_up,   NULL, NULL,
                     model.layers[il].ffn_gate, NULL, NULL,
                     model.layers[il].ffn_down, NULL, NULL,
@@ -11250,7 +11366,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -11288,21 +11404,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
@@ -11323,7 +11439,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -11344,7 +11460,7 @@ struct llm_build_context {
                     LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
+            cur = llm_build_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_up,   NULL, NULL,
                     model.layers[il].ffn_gate, NULL, NULL,
                     model.layers[il].ffn_down, NULL, NULL,
@@ -11368,7 +11484,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -11419,21 +11535,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
@@ -11454,7 +11570,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -11481,7 +11597,7 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -11515,7 +11631,7 @@ struct llm_build_context {
         cb(cur, "lmhead_scaling", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -11552,13 +11668,13 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
                 Qcur = ggml_rope_ext(
@@ -11576,7 +11692,7 @@ struct llm_build_context {
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
@@ -11598,7 +11714,7 @@ struct llm_build_context {
 
             // feed-forward network
             {
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -11623,7 +11739,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -11665,13 +11781,13 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
                 Qcur = ggml_rope_ext(
@@ -11694,7 +11810,7 @@ struct llm_build_context {
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask_l, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
@@ -11721,7 +11837,7 @@ struct llm_build_context {
 
             // feed-forward network
             {
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -11751,7 +11867,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
 
         // final logit soft-capping
         cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
@@ -11796,21 +11912,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
@@ -11831,7 +11947,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -11853,7 +11969,7 @@ struct llm_build_context {
                     LLM_NORM, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
+            cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -11877,7 +11993,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -11929,7 +12045,7 @@ struct llm_build_context {
             cb(cur, "attn_norm", il);
 
             // {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens}
-            struct ggml_tensor * xz = ggml_mul_mat(ctx0, model.layers[il].ssm_in, cur);
+            struct ggml_tensor * xz = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_in, cur);
             // split the above in two
             // => {d_inner, n_tokens}
             struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0);
@@ -11968,14 +12084,14 @@ struct llm_build_context {
             // ssm
             {
                 // {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens}
-                struct ggml_tensor * x_db = ggml_mul_mat(ctx0, model.layers[il].ssm_x, x);
+                struct ggml_tensor * x_db = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_x, x);
                 // split
                 struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0);
                 struct ggml_tensor * B  = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank);
                 struct ggml_tensor * C  = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state));
 
                 // {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens}
-                dt = ggml_mul_mat(ctx0, model.layers[il].ssm_dt, dt);
+                dt = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_dt, dt);
                 dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
 
                 // Custom operator to optimize the parallel associative scan
@@ -12006,7 +12122,7 @@ struct llm_build_context {
                 y = ggml_mul(ctx0, y, ggml_silu(ctx0, z));
 
                 // {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens}
-                cur = ggml_mul_mat(ctx0, model.layers[il].ssm_out, y);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_out, y);
             }
 
             // residual
@@ -12025,7 +12141,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -12064,21 +12180,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
                     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
                     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
                     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
@@ -12124,7 +12240,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -12141,7 +12257,7 @@ struct llm_build_context {
 
             // feed-forward network
             {
-                cur = llm_build_ffn(ctx0, ffn_inp,
+                cur = llm_build_ffn(ctx0, lctx, ffn_inp,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -12168,7 +12284,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
 
         if (f_logit_scale) {
             cur = ggml_scale(ctx0, cur, f_logit_scale);
@@ -12221,21 +12337,21 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
                 if (hparams.f_clamp_kqv > 0.0f) {
                     Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                     cb(Qcur, "Qcur", il);
                 }
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
                 if (hparams.f_clamp_kqv > 0.0f) {
                     Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                     cb(Kcur, "Kcur", il);
                 }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
                 if (hparams.f_clamp_kqv > 0.0f) {
                     Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
@@ -12256,7 +12372,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, nullptr,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -12278,7 +12394,7 @@ struct llm_build_context {
                     LLM_NORM, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
+            cur = llm_build_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_up,   NULL, NULL,
                     model.layers[il].ffn_gate, NULL, NULL,
                     model.layers[il].ffn_down, NULL, NULL,
@@ -12304,7 +12420,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -12344,7 +12460,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_reshape_3d(ctx0, cur, n_embd_head_k, n_head_qkv, n_tokens);
@@ -12383,7 +12499,7 @@ struct llm_build_context {
                 Vcur = ggml_reshape_2d(ctx0, Vcur, n_embd_head * n_head_kv, n_tokens);
                 cb(Qcur, "Vcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -12405,7 +12521,7 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -12429,7 +12545,7 @@ struct llm_build_context {
                 LLM_NORM_RMS, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -12464,7 +12580,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -12492,7 +12608,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -12517,7 +12633,7 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -12548,7 +12664,7 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         NULL,                      NULL,                        NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -12571,7 +12687,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -12612,13 +12728,13 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 cb(Kcur, "Kcur", il);
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 cb(Vcur, "Vcur", il);
 
                 Qcur = ggml_rope_ext(
@@ -12635,7 +12751,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
             }
@@ -12657,7 +12773,7 @@ struct llm_build_context {
                     LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
+            cur = llm_build_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_up,   NULL, NULL,
                     model.layers[il].ffn_gate, NULL, NULL,
                     model.layers[il].ffn_down, NULL, NULL,
@@ -12674,7 +12790,7 @@ struct llm_build_context {
                     LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm_exps", il);
 
-            cur = llm_build_moe_ffn(ctx0, cur,
+            cur = llm_build_moe_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_gate_inp,
                     model.layers[il].ffn_up_exps,
                     model.layers[il].ffn_gate_exps,
@@ -12703,7 +12819,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -12857,7 +12973,7 @@ struct llm_build_context {
                 struct ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
                 cb(k_states, "k_states", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         k_states, v_states, q_states, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
             }
@@ -12879,7 +12995,7 @@ struct llm_build_context {
             cb(cur, "ffn_norm", il);
 
             if ((uint32_t) il < hparams.n_layer_dense_lead) {
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         model.layers[il].ffn_gate, NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -12889,7 +13005,7 @@ struct llm_build_context {
             } else {
                 // MoE branch
                 ggml_tensor * moe_out =
-                        llm_build_moe_ffn(ctx0, cur,
+                        llm_build_moe_ffn(ctx0, lctx, cur,
                             model.layers[il].ffn_gate_inp,
                             model.layers[il].ffn_up_exps,
                             model.layers[il].ffn_gate_exps,
@@ -12902,7 +13018,7 @@ struct llm_build_context {
 
                 // FFN shared expert
                 {
-                    ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, cur,
+                    ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, cur,
                             model.layers[il].ffn_up_shexp,   NULL, NULL,
                             model.layers[il].ffn_gate_shexp, NULL, NULL,
                             model.layers[il].ffn_down_shexp, NULL, NULL,
@@ -12967,7 +13083,7 @@ struct llm_build_context {
             // self-attention
             {
                 // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                 Qcur = ggml_mul(ctx0, Qcur, model.layers[il].wq_scale);
                 cb(Qcur, "Qcur", il);
                 if (model.layers[il].bq) {
@@ -12976,7 +13092,7 @@ struct llm_build_context {
                 }
 
                 // B1.K
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                 Kcur = ggml_mul(ctx0, Kcur, model.layers[il].wk_scale);
                 cb(Kcur, "Kcur", il);
                 if (model.layers[il].bk) {
@@ -12985,7 +13101,7 @@ struct llm_build_context {
                 }
 
                 // B1.V
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                 Vcur = ggml_mul(ctx0, Vcur, model.layers[il].wv_scale);
                 cb(Vcur, "Vcur", il);
                 if (model.layers[il].bv) {
@@ -13007,7 +13123,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         NULL, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
 
@@ -13016,7 +13132,7 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "attn_sub_norm", il);
 
-                cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
                 cur = ggml_mul(ctx0, cur, model.layers[il].wo_scale);
                 if (model.layers[il].bo) {
                     cur = ggml_add(ctx0, cur, model.layers[il].bo);
@@ -13040,7 +13156,7 @@ struct llm_build_context {
                     LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_norm", il);
 
-            cur = llm_build_ffn(ctx0, cur,
+            cur = llm_build_ffn(ctx0, lctx, cur,
                     model.layers[il].ffn_up,   NULL, model.layers[il].ffn_up_scale,
                     model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_scale,
                     NULL,                      NULL, NULL,
@@ -13053,7 +13169,7 @@ struct llm_build_context {
                             LLM_NORM_RMS, cb, il);
             cb(cur, "ffn_sub_norm", il);
 
-            cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down, cur);
+            cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_down, cur);
             cur = ggml_mul(ctx0, cur, model.layers[il].ffn_down_scale);
             cb(cur, "ffn_down", il);
 
@@ -13072,7 +13188,7 @@ struct llm_build_context {
         cb(cur, "result_norm", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.tok_embd, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -13174,7 +13290,7 @@ struct llm_build_context {
                     cb(cur, "ffn_norm", il);
 
                     // T5 uses relu, flan-T5 uses gelu-gated
-                    cur = llm_build_ffn(ctx0, cur,
+                    cur = llm_build_ffn(ctx0, lctx, cur,
                             model.layers[il].ffn_up_enc,   NULL, NULL,
                             model.layers[il].ffn_gate_enc, NULL, NULL,
                             model.layers[il].ffn_down_enc, NULL, NULL,
@@ -13354,7 +13470,7 @@ struct llm_build_context {
                     cb(cur, "ffn_norm", il);
 
                     // T5 uses relu, flan-T5 uses gelu-gated
-                    cur = llm_build_ffn(ctx0, cur,
+                    cur = llm_build_ffn(ctx0, lctx, cur,
                             model.layers[il].ffn_up,   NULL, NULL,
                             model.layers[il].ffn_gate, NULL, NULL,
                             model.layers[il].ffn_down, NULL, NULL,
@@ -13420,7 +13536,7 @@ struct llm_build_context {
 
             // self-attention
             {
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -13436,7 +13552,7 @@ struct llm_build_context {
 
                 Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/float(n_embd_head), cb, il);
             }
@@ -13460,7 +13576,7 @@ struct llm_build_context {
                         LLM_NORM, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                         model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
                         model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
@@ -13479,7 +13595,7 @@ struct llm_build_context {
                 LLM_NORM, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
 
         cb(cur, "result_output", -1);
 
@@ -13521,7 +13637,7 @@ struct llm_build_context {
                 struct ggml_tensor * Kcur = nullptr;
                 struct ggml_tensor * Vcur = nullptr;
 
-                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                 cb(cur, "wqkv", il);
 
                 cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
@@ -13549,7 +13665,7 @@ struct llm_build_context {
                 );
                 cb(Kcur, "Kcur_rope", il);
 
-                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, NULL,
                         Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
 
@@ -13574,7 +13690,7 @@ struct llm_build_context {
                         LLM_NORM_RMS, cb, il);
                 cb(cur, "ffn_norm", il);
 
-                cur = llm_build_ffn(ctx0, cur,
+                cur = llm_build_ffn(ctx0, lctx, cur,
                         model.layers[il].ffn_up,   NULL, NULL,
                         NULL,                      NULL, NULL,
                         model.layers[il].ffn_down, NULL, NULL,
@@ -13594,7 +13710,7 @@ struct llm_build_context {
                 LLM_NORM_RMS, cb, -1);
         cb(cur, "result_norm", -1);
 
-        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -18458,282 +18574,210 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     }
 }
 
-static int llama_apply_lora_from_file_internal(
-    const struct llama_model & model, const char * path_lora, float scale, const char * path_base_model, int n_threads
-) {
-    LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
-
-    const int64_t t_start_lora_us = ggml_time_us();
-
-    llama_file fin(path_lora, "rb");
-
-    // verify magic and version
-    {
-        uint32_t magic = fin.read_u32();
-        if (magic != LLAMA_FILE_MAGIC_GGLA) {
-            LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
-            return 1;
-        }
-
-        uint32_t format_version = fin.read_u32();
-        if (format_version != 1) {
-            LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
-            return 1;
-        }
-    }
-
-    int32_t lora_r = fin.read_u32();
-    int32_t lora_alpha = fin.read_u32();
-    float scaling = scale * (float)lora_alpha / (float)lora_r;
-
-    LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
+static void llama_lora_adapter_init_internal(struct llama_model * model, const char * path_lora, struct llama_lora_adapter & adapter) {
+    LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
 
-    // load base model
-    std::unique_ptr<llama_model_loader> ml;
-    if (path_base_model) {
-        LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
-        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*check_tensors*/ false, /*kv_overrides*/ nullptr));
-        ml->init_mappings(/*prefetch*/ false); // no prefetching
-    }
-
-    struct tensor_meta {
-        std::string name;
-        ggml_type type;
-        int32_t ne[2];
-        size_t offset;
+    ggml_context * ctx = nullptr;
+    struct gguf_init_params meta_gguf_params = {
+        /* .no_alloc = */ true,
+        /* .ctx      = */ &ctx,
     };
-    std::map<std::string, tensor_meta> tensor_meta_map;
-
-    // load all tensor meta
-    while (true) {
-        if (fin.tell() == fin.size) {
-            // eof
-            break;
-        }
-
-        int32_t n_dims;
-        int32_t name_len;
-        int32_t ftype;
-
-        fin.read_raw(&n_dims, sizeof(n_dims));
-        fin.read_raw(&name_len, sizeof(name_len));
-        fin.read_raw(&ftype, sizeof(ftype));
-
-        if (n_dims != 1 && n_dims != 2) {
-            LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
-            return 1;
-        }
+    struct gguf_context * ctx_gguf = gguf_init_from_file(path_lora, meta_gguf_params);
+    if (!ctx_gguf) {
+        throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora));
+    }
 
-        int32_t ne[2] = { 1, 1 };
-        for (int i = 0; i < n_dims; ++i) {
-            fin.read_raw(&ne[i], sizeof(ne[i]));
-        }
+    // check metadata
+    {
+        auto get_kv_str = [&](const std::string & key) -> std::string {
+            int id = gguf_find_key(ctx_gguf, key.c_str());
+            return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id));
+        };
+        auto get_kv_f32 = [&](const std::string & key) -> float {
+            int id = gguf_find_key(ctx_gguf, key.c_str());
+            return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf, id);
+        };
+        LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
 
-        std::string name;
-        {
-            GGML_ASSERT(name_len < GGML_MAX_NAME);
-            char buf[GGML_MAX_NAME];
-            fin.read_raw(buf, name_len);
-            name = std::string(buf, name_len);
+        auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE));
+        if (general_type != "adapter") {
+            gguf_free(ctx_gguf);
+            throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
         }
 
-        // check for lora suffix
-        std::string lora_suffix;
-        if (name.length() > 6) {
-            lora_suffix = name.substr(name.length() - 6);
-        }
-        if (lora_suffix != ".loraA" && lora_suffix != ".loraB") {
-            LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
-            return 1;
+        auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));
+        auto general_arch = llm_arch_from_string(general_arch_str);
+        if (general_arch != model->arch) {
+            gguf_free(ctx_gguf);
+            throw std::runtime_error("model arch and LoRA arch mismatch");
         }
 
-        // tensor type
-        ggml_type wtype;
-        switch (ftype) {
-            case 0: wtype = GGML_TYPE_F32;  break;
-            case 1: wtype = GGML_TYPE_F16;  break;
-            default:
-                    {
-                        LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
-                                __func__, ftype);
-                        return 1;
-                    }
+        auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE));
+        if (adapter_type != "lora") {
+            gguf_free(ctx_gguf);
+            throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
         }
 
-        // data offset
-        size_t offset = fin.tell();
-        offset = (offset + 31) & -32;
-
-        // skip tensor data
-        fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET);
-
-        tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset });
+        adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA));
     }
 
-    bool warned = false;
-    int n_tensors = 0;
-
-    // apply
-    ggml_backend_t backend_cpu = ggml_backend_cpu_init();
-    if (backend_cpu == nullptr) {
-        LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__);
-        return 1;
-    }
-    ggml_backend_cpu_set_n_threads(backend_cpu, n_threads);
-
-    std::vector<no_init<uint8_t>> read_buf;
-    for (const auto & it : model.tensors_by_name) {
-        const std::string & base_name = it.first;
-        ggml_tensor * model_t = it.second;
-
-        if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() ||
-            tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) {
-            continue;
-        }
-
-        tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA");
-        tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB");
+    int n_tensors = gguf_get_n_tensors(ctx_gguf);
 
-        ggml_init_params lora_init_params = {
-            /* .mem_size   */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
-            /* .mem_buffer */ nullptr,
-            /* .no_alloc   */ true,
+    // contexts for each buffer type
+    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+    auto get_ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
+        auto it = ctx_map.find(buft);
+        if (it == ctx_map.end()) {
+            // add a new context
+            struct ggml_init_params params = {
+                /*.mem_size   =*/ n_tensors*ggml_tensor_overhead(),
+                /*.mem_buffer =*/ NULL,
+                /*.no_alloc   =*/ true,
+            };
+            ggml_context * buft_ctx = ggml_init(params);
+            ctx_map[buft] = buft_ctx;
+            return buft_ctx;
         };
-        ggml_context * lora_ctx = ggml_init(lora_init_params);
-        if (lora_ctx == nullptr) {
-            LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__);
-            ggml_backend_free(backend_cpu);
-            return 1;
-        }
-
-        // create tensors
-        ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]);
-        ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]);
-        ggml_set_name(loraA, metaA.name.c_str());
-        ggml_set_name(loraB, metaB.name.c_str());
+        return it->second;
+    };
 
-        ggml_tensor * base_t;
-        if (ml) {
-            if (!ml->get_tensor_meta(base_name.c_str())) {
-                LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
-                return 1;
+    // bundle lora_a and lora_b into pairs
+    std::map<std::string, llama_lora_weight> ab_map;
+    auto str_endswith = [](const std::string & str, const std::string & suffix) {
+        return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
+    };
+    for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+        std::string name(cur->name);
+        if (str_endswith(name, ".lora_a")) {
+            replace_all(name, ".lora_a", "");
+            if (ab_map.find(name) == ab_map.end()) {
+                ab_map[name] = llama_lora_weight(cur, nullptr);
+            } else {
+                ab_map[name].a = cur;
+            }
+        } else if (str_endswith(name, ".lora_b")) {
+            replace_all(name, ".lora_b", "");
+            if (ab_map.find(name) == ab_map.end()) {
+                ab_map[name] = llama_lora_weight(nullptr, cur);
+            } else {
+                ab_map[name].b = cur;
             }
-            base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str()));
         } else {
-            base_t = ggml_dup_tensor(lora_ctx, model_t);
-        }
-        ggml_set_name(base_t, base_name.c_str());
-
-        // allocate in backend buffer
-        ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
-        if (lora_buf == nullptr) {
-            LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__);
-            return 1;
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
         }
+    }
 
-        // load tensor data
-        auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
-            read_buf.resize(ggml_nbytes(tensor));
-            fin.seek(tensor_meta.offset, SEEK_SET);
-            fin.read_raw(read_buf.data(), ggml_nbytes(tensor));
-            ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size());
-        };
-        load_tensor(metaA, loraA);
-        load_tensor(metaB, loraB);
+    // add tensors
+    for (auto & it : ab_map) {
+        const std::string & name = it.first;
+        llama_lora_weight & w = it.second;
 
-        // load base model tensor data
-        if (ml) {
-            ml->load_data_for(base_t);
-        } else {
-            ggml_backend_tensor_copy(model_t, base_t);
+        if (!w.a || !w.b) {
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
         }
 
-        if (ggml_is_quantized(base_t->type) && !warned) {
-            LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
-                            "use a f16 or f32 base model with --lora-base\n", __func__);
-            warned = true;
+        // device buft and device ctx
+        auto * model_tensor = llama_get_model_tensor(model, name.c_str());
+        if (!model_tensor) {
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model");
         }
-
-        if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
-            LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
-                            " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
-            ggml_free(lora_ctx);
-            ggml_backend_buffer_free(lora_buf);
-            ggml_backend_free(backend_cpu);
-            return 1;
+        struct ggml_context * dev_ctx = get_ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
+        // validate tensor shape
+        if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            throw std::runtime_error("tensor '" + name + "' has incorrect shape");
         }
+        if (w.a->ne[1] != w.b->ne[0]) {
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
+        }
+        // save tensor to adapter
+        struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
+        struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
+        ggml_set_name(tensor_a, w.a->name);
+        ggml_set_name(tensor_b, w.b->name);
+        adapter.ab_map[name] = llama_lora_weight(tensor_a, tensor_b);
+    }
 
-        auto build_lora_graph = [&]() {
-            // w = w + BA*s
-            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
-            ggml_set_name(BA, "BA");
-
-            if (scaling != 1.0f) {
-                BA = ggml_scale(lora_ctx, BA, scaling);
-                ggml_set_name(BA, "BA_scaled");
-            }
-
-            ggml_tensor * r;
-            r = ggml_add_inplace(lora_ctx, base_t, BA);
-            ggml_set_name(r, "r_add");
-
-            if (base_t->type != model_t->type) {
-                // convert the result to the model type
-                r = ggml_cast(lora_ctx, r, model_t->type);
-                ggml_set_name(r, "r_cast");
+    // allocate tensors / buffers and zero
+    {
+        adapter.ctxs.reserve(ctx_map.size());
+        adapter.bufs.reserve(ctx_map.size());
+        for (auto it : ctx_map) {
+            ggml_backend_buffer_type_t buft = it.first;
+            ggml_context * ctx_dev = it.second;
+            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft);
+            if (!buf) {
+                gguf_free(ctx_gguf);
+                ggml_free(ctx);
+                throw std::runtime_error("failed to allocate buffer for lora adapter\n");
             }
+            LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
+            adapter.ctxs.push_back(ctx_dev);
+            adapter.bufs.push_back(buf);
+        }
+    }
 
-            return r;
+    // set tensor data
+    {
+        llama_file gguf_file(path_lora, "rb");
+        std::vector<uint8_t> read_buf;
+        auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
+            size_t offs = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, gguf_find_tensor(ctx_gguf, orig->name));
+            size_t size = ggml_nbytes(orig);
+            read_buf.resize(size);
+            gguf_file.seek(offs, SEEK_SET);
+            gguf_file.read_raw(read_buf.data(), size);
+            ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
         };
-
-        ggml_cgraph * gf = ggml_new_graph(lora_ctx);
-        ggml_tensor * r = build_lora_graph();
-        ggml_build_forward_expand(gf, r);
-
-        ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
-        if (graph_buf == nullptr) {
-            LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__);
-            ggml_free(lora_ctx);
-            ggml_backend_buffer_free(lora_buf);
-            ggml_backend_free(backend_cpu);
-            return 1;
+        for (auto & it : adapter.ab_map) {
+            auto orig = ab_map[it.first];
+            auto dev  = it.second;
+            set_tensor(orig.a, dev.a);
+            set_tensor(orig.b, dev.b);
         }
+    }
 
-        ggml_backend_graph_compute(backend_cpu, gf);
-
-        ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r));
-
-#if 0
-        // TODO: use scheduler with fallback to CPU for less copies between CPU and GPU
-        //ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE);
-
-        // sched compute
-        ggml_build_forward_expand(gf, build_graph());
-        ggml_backend_sched_init_measure(sched, gf);
-
-        // create the graph again, since the previous one was destroyed by the measure
-        ggml_graph_clear(gf);
-        ggml_build_forward_expand(gf, build_graph());
-        ggml_backend_sched_graph_compute(sched, gf);
-        ggml_backend_sched_free(sched);
-#endif
+    LLAMA_LOG_INFO("%s: loaded %ld tensors from lora file\n", __func__, adapter.ab_map.size()*2);
 
-        ggml_backend_buffer_free(lora_buf);
-        ggml_backend_buffer_free(graph_buf);
-        ggml_free(lora_ctx);
+    // free ctx for reading gguf
+    gguf_free(ctx_gguf);
+    ggml_free(ctx);
+}
 
-        n_tensors++;
-        if (n_tensors % 4 == 0) {
-            LLAMA_LOG_INFO(".");
-        }
+int32_t llama_lora_adapter_set(
+            struct llama_context * ctx,
+            struct llama_lora_adapter * adapter,
+            float scale) {
+    if (ctx->cparams.flash_attn) {
+        LLAMA_LOG_ERROR("%s: flash_attn is not compatible with LoRA\n", __func__);
+        return -1;
     }
+    ctx->lora_adapters[adapter] = scale;
+    return 0;
+}
 
-    ggml_backend_free(backend_cpu);
-
-    const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
-    LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
+int32_t llama_lora_adapter_remove(
+            struct llama_context * ctx,
+            struct llama_lora_adapter * adapter) {
+    auto pos = ctx->lora_adapters.find(adapter);
+    if (pos != ctx->lora_adapters.end()) {
+        ctx->lora_adapters.erase(pos);
+        return 0;
+    }
+    return -1;
+}
 
-    return 0;
+void llama_lora_adapter_free(struct llama_lora_adapter * adapter) {
+    delete adapter;
 }
 
 //
@@ -19514,12 +19558,14 @@ uint32_t llama_model_quantize(
     }
 }
 
-int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
+struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model, const char * path_lora) {
     try {
-        return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
+        struct llama_lora_adapter * adapter = new llama_lora_adapter(model);
+        llama_lora_adapter_init_internal(model, path_lora, *adapter);
+        return adapter;
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
-        return 1;
+        return nullptr;
     }
 }
 

From 7acfd4e8d55082c1b597dfc3ffe04fb5d530c6dc Mon Sep 17 00:00:00 2001
From: compilade <git@compilade.net>
Date: Mon, 15 Jul 2024 23:13:10 -0400
Subject: [PATCH 060/125] convert_hf : faster lazy safetensors (#8482)

* convert_hf : faster lazy safetensors

This makes '--dry-run' much, much faster.

* convert_hf : fix memory leak in lazy MoE conversion

The '_lazy' queue was sometimes self-referential,
which caused reference cycles of objects old enough
to avoid garbage collection until potential memory exhaustion.
---
 convert_hf_to_gguf.py          | 48 ++++++++++++++++++++++----
 gguf-py/gguf/lazy.py           | 63 ++++++++++------------------------
 gguf-py/gguf/tensor_mapping.py | 14 ++++----
 3 files changed, 65 insertions(+), 60 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index a755b0a60bf0a7..c2aba909706d0a 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -148,9 +148,16 @@ def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
                 tensor_names_from_parts.update(model_part.keys())
 
                 for name in model_part.keys():
-                    data = model_part.get_tensor(name) if self.is_safetensors else model_part[name]
-                    if self.lazy:
-                        data = LazyTorchTensor.from_eager(data)
+                    if self.is_safetensors:
+                        if self.lazy:
+                            data = model_part.get_slice(name)
+                            data = LazyTorchTensor.from_safetensors_slice(data)
+                        else:
+                            data = model_part.get_tensor(name)
+                    else:
+                        data = model_part[name]
+                        if self.lazy:
+                            data = LazyTorchTensor.from_eager(data)
                     yield name, data
 
         # only verify tensor name presence; it doesn't matter if they are not in the right files
@@ -3424,19 +3431,46 @@ class LazyTorchTensor(gguf.LazyBase):
         torch.float32: np.float32,
     }
 
+    # used for safetensors slices
+    # ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046
+    # TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734
+    _dtype_str_map: dict[str, torch.dtype] = {
+        "F64": torch.float64,
+        "F32": torch.float32,
+        "BF16": torch.bfloat16,
+        "F16": torch.float16,
+        # "U64": torch.uint64,
+        "I64": torch.int64,
+        # "U32": torch.uint32,
+        "I32": torch.int32,
+        # "U16": torch.uint16,
+        "I16": torch.int16,
+        "U8": torch.uint8,
+        "I8": torch.int8,
+        "BOOL": torch.bool,
+        "F8_E4M3": torch.float8_e4m3fn,
+        "F8_E5M2": torch.float8_e5m2,
+    }
+
     def numpy(self) -> gguf.LazyNumpyTensor:
         dtype = self._dtype_map[self.dtype]
         return gguf.LazyNumpyTensor(
             meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
-            lazy=self._lazy,
             args=(self,),
-            func=(lambda s: s[0].numpy())
+            func=(lambda s: s.numpy())
         )
 
     @classmethod
-    def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: torch.Size) -> Tensor:
+    def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor:
         return torch.empty(size=shape, dtype=dtype, device="meta")
 
+    @classmethod
+    def from_safetensors_slice(cls, st_slice: Any) -> Tensor:
+        dtype = cls._dtype_str_map[st_slice.get_dtype()]
+        shape: tuple[int, ...] = tuple(st_slice.get_shape())
+        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[:])
+        return cast(torch.Tensor, lazy)
+
     @classmethod
     def __torch_function__(cls, func, types, args=(), kwargs=None):
         del types  # unused
@@ -3447,7 +3481,7 @@ def __torch_function__(cls, func, types, args=(), kwargs=None):
         if func is torch.Tensor.numpy:
             return args[0].numpy()
 
-        return LazyTorchTensor._wrap_fn(func)(*args, **kwargs)
+        return cls._wrap_fn(func)(*args, **kwargs)
 
 
 def parse_args() -> argparse.Namespace:
diff --git a/gguf-py/gguf/lazy.py b/gguf-py/gguf/lazy.py
index 6e266f34f76ace..ac98d9a92a3e9e 100644
--- a/gguf-py/gguf/lazy.py
+++ b/gguf-py/gguf/lazy.py
@@ -3,7 +3,6 @@
 
 import logging
 from typing import Any, Callable
-from collections import deque
 
 import numpy as np
 from numpy.typing import DTypeLike
@@ -74,20 +73,18 @@ class LazyBase(ABC, metaclass=LazyMeta):
     _tensor_type: type
     _meta: Any
     _data: Any | None
-    _lazy: deque[LazyBase]  # shared within a graph, to avoid deep recursion when making eager
     _args: tuple
-    _func: Callable[[tuple], Any] | None
+    _kwargs: dict[str, Any]
+    _func: Callable[[Any], Any] | None
 
-    def __init__(self, *, meta: Any, data: Any | None = None, lazy: deque[LazyBase] | None = None, args: tuple = (), func: Callable[[tuple], Any] | None = None):
+    def __init__(self, *, meta: Any, data: Any | None = None, args: tuple = (), kwargs: dict[str, Any] | None = None, func: Callable[[Any], Any] | None = None):
         super().__init__()
         self._meta = meta
         self._data = data
-        self._lazy = lazy if lazy is not None else deque()
         self._args = args
+        self._kwargs = kwargs if kwargs is not None else {}
         self._func = func
         assert self._func is not None or self._data is not None
-        if self._data is None:
-            self._lazy.append(self)
 
     def __init_subclass__(cls) -> None:
         if "_tensor_type" not in cls.__dict__:
@@ -117,6 +114,7 @@ def wrapped_fn(*args, **kwargs):
             args = ((use_self,) if use_self is not None else ()) + args
 
             meta_args = LazyBase._recurse_apply(args, lambda t: t._meta)
+            # TODO: maybe handle tensors in kwargs too
 
             if isinstance(meta_noop, bool) and not meta_noop:
                 try:
@@ -140,23 +138,7 @@ def wrapped_fn(*args, **kwargs):
                         res = cls.meta_with_dtype_and_shape(meta_noop, res.shape)
 
             if isinstance(res, cls._tensor_type):
-                class CollectSharedLazy:
-                    # emulating a static variable
-                    shared_lazy: None | deque[LazyBase] = None
-
-                    @staticmethod
-                    def collect_replace(t: LazyBase):
-                        if CollectSharedLazy.shared_lazy is None:
-                            CollectSharedLazy.shared_lazy = t._lazy
-                        else:
-                            CollectSharedLazy.shared_lazy.extend(t._lazy)
-                            t._lazy = CollectSharedLazy.shared_lazy
-
-                LazyBase._recurse_apply(args, CollectSharedLazy.collect_replace)
-
-                shared_lazy = CollectSharedLazy.shared_lazy
-
-                return cls(meta=cls.eager_to_meta(res), lazy=shared_lazy, args=args, func=lambda a: fn(*a, **kwargs))
+                return cls(meta=cls.eager_to_meta(res), args=args, kwargs=kwargs, func=fn)
             else:
                 del res  # not needed
                 # non-tensor return likely relies on the contents of the args
@@ -168,26 +150,18 @@ def collect_replace(t: LazyBase):
     @classmethod
     def to_eager(cls, t: Any) -> Any:
         def simple_to_eager(_t: LazyBase) -> Any:
-            def already_eager_to_eager(_t: LazyBase) -> Any:
-                assert _t._data is not None
+            if _t._data is not None:
                 return _t._data
 
-            while _t._data is None:
-                lt = _t._lazy.popleft()
-                if lt._data is not None:
-                    # Lazy tensor did not belong in the lazy queue.
-                    # Weirdly only happens with Bloom models...
-                    # likely because tensors aren't unique in the queue.
-                    # The final output is still the same as in eager mode,
-                    # so it's safe to ignore this.
-                    continue
-                assert lt._func is not None
-                lt._args = cls._recurse_apply(lt._args, already_eager_to_eager)
-                lt._data = lt._func(lt._args)
-                # sanity check
-                assert lt._data is not None
-                assert lt._data.dtype == lt._meta.dtype
-                assert lt._data.shape == lt._meta.shape
+            # NOTE: there's a recursion limit in Python (usually 1000)
+
+            assert _t._func is not None
+            _t._args = cls._recurse_apply(_t._args, simple_to_eager)
+            _t._data = _t._func(*_t._args, **_t._kwargs)
+            # sanity check
+            assert _t._data is not None
+            assert _t._data.dtype == _t._meta.dtype
+            assert _t._data.shape == _t._meta.shape
 
             return _t._data
 
@@ -206,7 +180,7 @@ def meta_with_dtype_and_shape(cls, dtype: Any, shape: Any) -> Any: pass
     @classmethod
     def from_eager(cls, t: Any) -> Any:
         if type(t) is cls:
-            # already eager
+            # already lazy
             return t
         elif isinstance(t, cls._tensor_type):
             return cls(meta=cls.eager_to_meta(t), data=t)
@@ -228,8 +202,7 @@ def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: tuple[int, ...]) ->
     def astype(self, dtype, *args, **kwargs):
         meta = type(self).meta_with_dtype_and_shape(dtype, self._meta.shape)
         full_args = (self, dtype,) + args
-        # very important to pass the shared _lazy deque, or else there's an infinite loop somewhere.
-        return type(self)(meta=meta, args=full_args, lazy=self._lazy, func=(lambda a: a[0].astype(*a[1:], **kwargs)))
+        return type(self)(meta=meta, args=full_args, kwargs=kwargs, func=(lambda a, *args, **kwargs: a.astype(*args, **kwargs)))
 
     def tofile(self, *args, **kwargs):
         eager = LazyNumpyTensor.to_eager(self)
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 7264240f5e17a2..9aa2209e2f69f8 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -602,14 +602,12 @@ def __init__(self, arch: MODEL_ARCH, n_blocks: int):
             for tensor, keys in self.block_mappings_cfg.items():
                 if tensor not in MODEL_TENSORS[arch]:
                     continue
-                # TODO: make this configurable
-                n_experts = 160
-                for xid in range(n_experts):
-                    tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid)
-                    self.mapping[tensor_name] = (tensor, tensor_name)
-                    for key in keys:
-                        key = key.format(bid = bid, xid = xid)
-                        self.mapping[key] = (tensor, tensor_name)
+
+                tensor_name = TENSOR_NAMES[tensor].format(bid = bid)
+                self.mapping[tensor_name] = (tensor, tensor_name)
+                for key in keys:
+                    key = key.format(bid = bid)
+                    self.mapping[key] = (tensor, tensor_name)
 
     def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None:
         result = self.mapping.get(key)

From 0efec57787cb8d8d76b17cd3765e6521e22c1f19 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 16 Jul 2024 10:00:30 +0300
Subject: [PATCH 061/125] llama : valign + remove unused ftype (#8502)

---
 examples/quantize/quantize.cpp | 74 +++++++++++++++++-----------------
 include/llama.h                |  2 +-
 src/llama.cpp                  | 72 ++++++++++++++++-----------------
 3 files changed, 72 insertions(+), 76 deletions(-)

diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp
index 1578c4afb5dfab..8d764725878b99 100644
--- a/examples/quantize/quantize.cpp
+++ b/examples/quantize/quantize.cpp
@@ -16,44 +16,44 @@ struct quant_option {
 };
 
 static const std::vector<struct quant_option> QUANT_OPTIONS = {
-    { "Q4_0",   LLAMA_FTYPE_MOSTLY_Q4_0,   " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
-    { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 4.78G, +0.4511 ppl @ Llama-3-8B",  },
-    { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 5.21G, +0.1316 ppl @ Llama-3-8B",  },
-    { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 5.65G, +0.1062 ppl @ Llama-3-8B",  },
-    { "IQ2_XXS",LLAMA_FTYPE_MOSTLY_IQ2_XXS," 2.06 bpw quantization",            },
-    { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization",            },
-    { "IQ2_S",  LLAMA_FTYPE_MOSTLY_IQ2_S,  " 2.5  bpw quantization",            },
-    { "IQ2_M",  LLAMA_FTYPE_MOSTLY_IQ2_M,  " 2.7  bpw quantization",            },
-    { "IQ1_S",  LLAMA_FTYPE_MOSTLY_IQ1_S,  " 1.56 bpw quantization",            },
-    { "IQ1_M",  LLAMA_FTYPE_MOSTLY_IQ1_M,  " 1.75 bpw quantization",            },
-    { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.96G, +3.5199 ppl @ Llama-3-8B",  },
-    { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.96G, +3.1836 ppl @ Llama-3-8B",  },
-    { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization",            },
-    { "IQ3_S",  LLAMA_FTYPE_MOSTLY_IQ3_S,  " 3.44 bpw quantization",            },
-    { "IQ3_M",  LLAMA_FTYPE_MOSTLY_IQ3_M,  " 3.66 bpw quantization mix",        },
-    { "Q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M"                   },
-    { "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization",             },
-    { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 3.41G, +1.6321 ppl @ Llama-3-8B",  },
-    { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.74G, +0.6569 ppl @ Llama-3-8B",  },
-    { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 4.03G, +0.5562 ppl @ Llama-3-8B",  },
-    { "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
-    { "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
-    { "Q4_K",   LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M",                  },
-    { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 4.37G, +0.2689 ppl @ Llama-3-8B",  },
-    { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 4.58G, +0.1754 ppl @ Llama-3-8B",  },
-    { "Q5_K",   LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M",                  },
-    { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 5.21G, +0.1049 ppl @ Llama-3-8B",  },
-    { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 5.33G, +0.0569 ppl @ Llama-3-8B",  },
-    { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 6.14G, +0.0217 ppl @ Llama-3-8B",  },
-    { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 7.96G, +0.0026 ppl @ Llama-3-8B",  },
-    { "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
-    { "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
-    { "Q4_0_8_8", LLAMA_FTYPE_MOSTLY_Q4_0_8_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
-    { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "14.00G, +0.0020 ppl @ Mistral-7B",  },
-    { "BF16",   LLAMA_FTYPE_MOSTLY_BF16,   "14.00G, -0.0050 ppl @ Mistral-7B",  },
-    { "F32",    LLAMA_FTYPE_ALL_F32,       "26.00G              @ 7B",          },
+    { "Q4_0",     LLAMA_FTYPE_MOSTLY_Q4_0,     " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
+    { "Q4_1",     LLAMA_FTYPE_MOSTLY_Q4_1,     " 4.78G, +0.4511 ppl @ Llama-3-8B",  },
+    { "Q5_0",     LLAMA_FTYPE_MOSTLY_Q5_0,     " 5.21G, +0.1316 ppl @ Llama-3-8B",  },
+    { "Q5_1",     LLAMA_FTYPE_MOSTLY_Q5_1,     " 5.65G, +0.1062 ppl @ Llama-3-8B",  },
+    { "IQ2_XXS",  LLAMA_FTYPE_MOSTLY_IQ2_XXS,  " 2.06 bpw quantization",            },
+    { "IQ2_XS",   LLAMA_FTYPE_MOSTLY_IQ2_XS,   " 2.31 bpw quantization",            },
+    { "IQ2_S",    LLAMA_FTYPE_MOSTLY_IQ2_S,    " 2.5  bpw quantization",            },
+    { "IQ2_M",    LLAMA_FTYPE_MOSTLY_IQ2_M,    " 2.7  bpw quantization",            },
+    { "IQ1_S",    LLAMA_FTYPE_MOSTLY_IQ1_S,    " 1.56 bpw quantization",            },
+    { "IQ1_M",    LLAMA_FTYPE_MOSTLY_IQ1_M,    " 1.75 bpw quantization",            },
+    { "Q2_K",     LLAMA_FTYPE_MOSTLY_Q2_K,     " 2.96G, +3.5199 ppl @ Llama-3-8B",  },
+    { "Q2_K_S",   LLAMA_FTYPE_MOSTLY_Q2_K_S,   " 2.96G, +3.1836 ppl @ Llama-3-8B",  },
+    { "IQ3_XXS",  LLAMA_FTYPE_MOSTLY_IQ3_XXS,  " 3.06 bpw quantization",            },
+    { "IQ3_S",    LLAMA_FTYPE_MOSTLY_IQ3_S,    " 3.44 bpw quantization",            },
+    { "IQ3_M",    LLAMA_FTYPE_MOSTLY_IQ3_M,    " 3.66 bpw quantization mix",        },
+    { "Q3_K",     LLAMA_FTYPE_MOSTLY_Q3_K_M,   "alias for Q3_K_M"                   },
+    { "IQ3_XS",   LLAMA_FTYPE_MOSTLY_IQ3_XS,   " 3.3 bpw quantization",             },
+    { "Q3_K_S",   LLAMA_FTYPE_MOSTLY_Q3_K_S,   " 3.41G, +1.6321 ppl @ Llama-3-8B",  },
+    { "Q3_K_M",   LLAMA_FTYPE_MOSTLY_Q3_K_M,   " 3.74G, +0.6569 ppl @ Llama-3-8B",  },
+    { "Q3_K_L",   LLAMA_FTYPE_MOSTLY_Q3_K_L,   " 4.03G, +0.5562 ppl @ Llama-3-8B",  },
+    { "IQ4_NL",   LLAMA_FTYPE_MOSTLY_IQ4_NL,   " 4.50 bpw non-linear quantization", },
+    { "IQ4_XS",   LLAMA_FTYPE_MOSTLY_IQ4_XS,   " 4.25 bpw non-linear quantization", },
+    { "Q4_K",     LLAMA_FTYPE_MOSTLY_Q4_K_M,   "alias for Q4_K_M",                  },
+    { "Q4_K_S",   LLAMA_FTYPE_MOSTLY_Q4_K_S,   " 4.37G, +0.2689 ppl @ Llama-3-8B",  },
+    { "Q4_K_M",   LLAMA_FTYPE_MOSTLY_Q4_K_M,   " 4.58G, +0.1754 ppl @ Llama-3-8B",  },
+    { "Q5_K",     LLAMA_FTYPE_MOSTLY_Q5_K_M,   "alias for Q5_K_M",                  },
+    { "Q5_K_S",   LLAMA_FTYPE_MOSTLY_Q5_K_S,   " 5.21G, +0.1049 ppl @ Llama-3-8B",  },
+    { "Q5_K_M",   LLAMA_FTYPE_MOSTLY_Q5_K_M,   " 5.33G, +0.0569 ppl @ Llama-3-8B",  },
+    { "Q6_K",     LLAMA_FTYPE_MOSTLY_Q6_K,     " 6.14G, +0.0217 ppl @ Llama-3-8B",  },
+    { "Q8_0",     LLAMA_FTYPE_MOSTLY_Q8_0,     " 7.96G, +0.0026 ppl @ Llama-3-8B",  },
+    { "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
+    { "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
+    { "Q4_0_8_8", LLAMA_FTYPE_MOSTLY_Q4_0_8_8, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
+    { "F16",      LLAMA_FTYPE_MOSTLY_F16,      "14.00G, +0.0020 ppl @ Mistral-7B",  },
+    { "BF16",     LLAMA_FTYPE_MOSTLY_BF16,     "14.00G, -0.0050 ppl @ Mistral-7B",  },
+    { "F32",      LLAMA_FTYPE_ALL_F32,         "26.00G              @ 7B",          },
     // Note: Ensure COPY comes after F32 to avoid ftype 0 from matching.
-    { "COPY",   LLAMA_FTYPE_ALL_F32,       "only copy tensors, no quantizing",  },
+    { "COPY",     LLAMA_FTYPE_ALL_F32,         "only copy tensors, no quantizing",  },
 };
 
 static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE       = "quantize.imatrix.file";
diff --git a/include/llama.h b/include/llama.h
index c57d21f0c70b95..c0fb53060eae44 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -133,7 +133,7 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_F16           = 1,  // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q4_0          = 2,  // except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q4_1          = 3,  // except 1d tensors
-        LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4,  // tok_embeddings.weight and output.weight are F16
+        // LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4,  // tok_embeddings.weight and output.weight are F16
         // LLAMA_FTYPE_MOSTLY_Q4_2       = 5,  // support has been removed
         // LLAMA_FTYPE_MOSTLY_Q4_3       = 6,  // support has been removed
         LLAMA_FTYPE_MOSTLY_Q8_0          = 7,  // except 1d tensors
diff --git a/src/llama.cpp b/src/llama.cpp
index 07bb427135d8c5..4a9903cc33e474 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -4510,40 +4510,36 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
     }
 
     switch (ftype) {
-        case LLAMA_FTYPE_ALL_F32:     return "all F32";
-        case LLAMA_FTYPE_MOSTLY_F16:  return "F16";
-        case LLAMA_FTYPE_MOSTLY_BF16: return "BF16";
-        case LLAMA_FTYPE_MOSTLY_Q4_0: return "Q4_0";
-        case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1";
-        case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
-                                      return "Q4_1, some F16";
-        case LLAMA_FTYPE_MOSTLY_Q5_0: return "Q5_0";
-        case LLAMA_FTYPE_MOSTLY_Q5_1: return "Q5_1";
-        case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0";
-
-        // K-quants
-        case LLAMA_FTYPE_MOSTLY_Q2_K:   return "Q2_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q2_K_S: return "Q2_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large";
-        case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "Q4_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "Q4_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q6_K:   return "Q6_K";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XXS - 2.0625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_S:  return "IQ2_S - 2.5 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_M:  return "IQ2_M - 2.7 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ1_S  :return "IQ1_S - 1.5625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ1_M  :return "IQ1_M - 1.75 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_S:  return "IQ3_S - 3.4375 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_M:  return "IQ3_S mix - 3.66 bpw";
+        case LLAMA_FTYPE_ALL_F32:         return "all F32";
+        case LLAMA_FTYPE_MOSTLY_F16:      return "F16";
+        case LLAMA_FTYPE_MOSTLY_BF16:     return "BF16";
+        case LLAMA_FTYPE_MOSTLY_Q4_0:     return "Q4_0";
+        case LLAMA_FTYPE_MOSTLY_Q4_1:     return "Q4_1";
+        case LLAMA_FTYPE_MOSTLY_Q5_0:     return "Q5_0";
+        case LLAMA_FTYPE_MOSTLY_Q5_1:     return "Q5_1";
+        case LLAMA_FTYPE_MOSTLY_Q8_0:     return "Q8_0";
+        case LLAMA_FTYPE_MOSTLY_Q2_K:     return "Q2_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q2_K_S:   return "Q2_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_S:   return "Q3_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_M:   return "Q3_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L:   return "Q3_K - Large";
+        case LLAMA_FTYPE_MOSTLY_Q4_K_S:   return "Q4_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M:   return "Q4_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q5_K_S:   return "Q5_K - Small";
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M:   return "Q5_K - Medium";
+        case LLAMA_FTYPE_MOSTLY_Q6_K:     return "Q6_K";
+        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:  return "IQ2_XXS - 2.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_XS:   return "IQ2_XS - 2.3125 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_S:    return "IQ2_S - 2.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ2_M:    return "IQ2_M - 2.7 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XS:   return "IQ3_XS - 3.3 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:  return "IQ3_XXS - 3.0625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_S:    return "IQ1_S - 1.5625 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ1_M:    return "IQ1_M - 1.75 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL:   return "IQ4_NL - 4.5 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ4_XS:   return "IQ4_XS - 4.25 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_S:    return "IQ3_S - 3.4375 bpw";
+        case LLAMA_FTYPE_MOSTLY_IQ3_M:    return "IQ3_S mix - 3.66 bpw";
         case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: return "Q4_0_4_4";
         case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: return "Q4_0_4_8";
         case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: return "Q4_0_8_8";
@@ -18069,10 +18065,10 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
     //    if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
     //}
     bool convert_incompatible_tensor = false;
-    if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
-        new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
-        new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
-        new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
+    if (new_type == GGML_TYPE_Q2_K    || new_type == GGML_TYPE_Q3_K    || new_type == GGML_TYPE_Q4_K   ||
+        new_type == GGML_TYPE_Q5_K    || new_type == GGML_TYPE_Q6_K    || new_type == GGML_TYPE_IQ4_XS ||
+        new_type == GGML_TYPE_IQ2_XS  || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S  ||
+        new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S   || new_type == GGML_TYPE_IQ3_S  ||
         new_type == GGML_TYPE_IQ1_M) {
         int nx = tensor->ne[0];
         int ny = tensor->ne[1];

From 37b12f92ab696d70f9a65d7447ce721b094fb32e Mon Sep 17 00:00:00 2001
From: Steve Bonds <sbonds@gmail.com>
Date: Tue, 16 Jul 2024 00:04:45 -0700
Subject: [PATCH 062/125] export-lora : handle help argument (#8497)

The --help option on export-lora isn't accepted as valid. The help still gets displayed by default, but the script exits with an error message and nonzero status.
---
 examples/export-lora/export-lora.cpp | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp
index 08413f57e4c3a3..16f27aa77b500d 100644
--- a/examples/export-lora/export-lora.cpp
+++ b/examples/export-lora/export-lora.cpp
@@ -190,6 +190,9 @@ static bool export_lora_params_parse(int argc, char ** argv, struct export_lora_
             if (params->n_threads <= 0) {
                 params->n_threads = std::thread::hardware_concurrency();
             }
+        } else if (arg == "-h" || arg == "--help") {
+            export_lora_print_usage(argc, argv, &default_params);
+            exit(0);
         } else {
             fprintf(stderr, "error: unknown argument: '%s'\n", arg.c_str());
             export_lora_print_usage(argc, argv, &default_params);

From 1666f92dcda14e002cbb08e1028f9fff341d73ad Mon Sep 17 00:00:00 2001
From: Brian <mofosyne@gmail.com>
Date: Tue, 16 Jul 2024 17:14:16 +1000
Subject: [PATCH 063/125] gguf-hash : update clib.json to point to original
 xxhash repo (#8491)

* Update clib.json to point to Cyan4973 original xxhash

Convinced Cyan4973 to add clib.json directly to his repo, so can now point the clib package directly to him now. Previously pointed to my fork with the clib.json package metadata

https://github.com/Cyan4973/xxHash/pull/954

* gguf-hash: readme update to point to Cyan4973 xxHash repo [no ci]
---
 examples/gguf-hash/README.md             | 2 +-
 examples/gguf-hash/deps/xxhash/clib.json | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/gguf-hash/README.md b/examples/gguf-hash/README.md
index 2320ffd8181b35..9871651e38ba8f 100644
--- a/examples/gguf-hash/README.md
+++ b/examples/gguf-hash/README.md
@@ -201,6 +201,6 @@ Verification results for test.gguf.manifest - Success
 
 These micro c libraries dependencies was installed via the [clib c package manager](https://github.com/clibs)
 
-- https://github.com/mofosyne/xxHash (From: https://github.com/Cyan4973/xxHash)
+- https://github.com/Cyan4973/xxHash
 - https://github.com/clibs/sha1/
 - https://github.com/jb55/sha256.c
diff --git a/examples/gguf-hash/deps/xxhash/clib.json b/examples/gguf-hash/deps/xxhash/clib.json
index 0048c74dc27410..242343c5d992d4 100644
--- a/examples/gguf-hash/deps/xxhash/clib.json
+++ b/examples/gguf-hash/deps/xxhash/clib.json
@@ -1,7 +1,7 @@
 {
   "name": "xxhash",
   "version": "0.8.2",
-  "repo": "mofosyne/xxhash",
+  "repo": "Cyan4973/xxhash",
   "description": "Extremely fast non-cryptographic hash algorithm",
   "keywords": ["xxhash", "hashing"],
   "license": "BSD-2-Clause",

From 5e116e8dd51775f8f1c090570be148d5d7eea6c3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 16 Jul 2024 21:20:59 +0200
Subject: [PATCH 064/125] make/cmake: add missing force MMQ/cuBLAS for HIP
 (#8515)

---
 Makefile                | 8 ++++++++
 ggml/src/CMakeLists.txt | 4 ++++
 2 files changed, 12 insertions(+)

diff --git a/Makefile b/Makefile
index bec332ec57b551..4584594af4174f 100644
--- a/Makefile
+++ b/Makefile
@@ -795,6 +795,14 @@ ifdef GGML_CUDA_FORCE_DMMV
 	HIPFLAGS += -DGGML_CUDA_FORCE_DMMV
 endif # GGML_CUDA_FORCE_DMMV
 
+ifdef GGML_CUDA_FORCE_MMQ
+	HIPFLAGS += -DGGML_CUDA_FORCE_MMQ
+endif # GGML_CUDA_FORCE_MMQ
+
+ifdef GGML_CUDA_FORCE_CUBLAS
+	HIPFLAGS += -DGGML_CUDA_FORCE_CUBLAS
+endif # GGML_CUDA_FORCE_CUBLAS
+
 ifdef GGML_CUDA_NO_PEER_COPY
 	HIPFLAGS += -DGGML_CUDA_NO_PEER_COPY
 endif # GGML_CUDA_NO_PEER_COPY
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 41e50ad2c9e337..cbadaf4d931c30 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -440,6 +440,10 @@ if (GGML_HIPBLAS)
         add_compile_definitions(GGML_CUDA_FORCE_MMQ)
     endif()
 
+    if (GGML_CUDA_FORCE_CUBLAS)
+        add_compile_definitions(GGML_CUDA_FORCE_CUBLAS)
+    endif()
+
     if (GGML_CUDA_NO_PEER_COPY)
         add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
     endif()

From d65a8361fed8be97389776fb94217e937ec6b03c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 17 Jul 2024 10:32:59 +0300
Subject: [PATCH 065/125] llama : disable context-shift for DeepSeek v2 (#8501)

---
 src/llama.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/llama.cpp b/src/llama.cpp
index 4a9903cc33e474..89b5763dcd7f87 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -15133,6 +15133,10 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) {
 
     // apply K-shift if needed
     if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) {
+        if (lctx.model.arch == LLM_ARCH_DEEPSEEK2) { // not supported due to MLA
+            GGML_ASSERT(false && "Deepseek2 does not support K-shift");
+        }
+
         {
             ggml_backend_sched_reset(lctx.sched);
 

From da3913d8f9475b0d4bcbeb4936c724af4eade092 Mon Sep 17 00:00:00 2001
From: "Masaya, Kato" <62578291+msy-kato@users.noreply.github.com>
Date: Wed, 17 Jul 2024 16:34:28 +0900
Subject: [PATCH 066/125] batched: fix n_predict parameter (#8527)

---
 examples/batched/batched.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 2442e954dcc1a0..53fbfb0a8cf2ae 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -31,7 +31,7 @@ int main(int argc, char ** argv) {
     int n_parallel = params.n_parallel;
 
     // total length of the sequences including the prompt
-    int n_predict = 32;
+    int n_predict = params.n_predict;
 
     // init LLM
 

From 1bdd8ae19f50bc6f108fa247e90688e5c60559fc Mon Sep 17 00:00:00 2001
From: hipudding <huafengchun@gmail.com>
Date: Wed, 17 Jul 2024 19:23:50 +0800
Subject: [PATCH 067/125] [CANN] Add Ascend NPU backend (#6035)

* [CANN] Add Ascend NPU backend

Ascend is a full-stack AI computing infrastructure for industry
applications and services based on Huawei Ascend processors and
software.

CANN (Compute Architecture of Neural Networks), developped by
Huawei, is a heterogeneous computing architecture for AI.

Co-authored-by: wangshuai09 <391746016@qq.com>

* delete trailing whitespaces

* Modify the code based on review comment

* Rename LLAMA_CANN to GGML_CANN

* Make ggml-common.h private

* add ggml_cann prefix for acl funcs

* Add logging for CANN backend

* Delete Trailing whitespace

---------

Co-authored-by: wangshuai09 <391746016@qq.com>
---
 CMakeLists.txt                                |    1 +
 examples/llama-bench/llama-bench.cpp          |   15 +
 examples/llava/clip.cpp                       |    9 +
 ggml/include/ggml-cann.h                      |  125 +
 ggml/include/ggml.h                           |    3 +
 ggml/src/CMakeLists.txt                       |   69 +
 ggml/src/ggml-backend.c                       |    5 +
 ggml/src/ggml-cann.cpp                        | 2023 +++++++++++
 ggml/src/ggml-cann/.clang-format              |  168 +
 ggml/src/ggml-cann/Doxyfile                   | 2579 +++++++++++++++
 ggml/src/ggml-cann/acl_tensor.cpp             |  198 ++
 ggml/src/ggml-cann/acl_tensor.h               |  230 ++
 ggml/src/ggml-cann/aclnn_ops.cpp              | 2944 +++++++++++++++++
 ggml/src/ggml-cann/aclnn_ops.h                |  592 ++++
 ggml/src/ggml-cann/common.h                   |  282 ++
 ggml/src/ggml-cann/kernels/CMakeLists.txt     |   32 +
 ggml/src/ggml-cann/kernels/ascendc_kernels.h  |   17 +
 ggml/src/ggml-cann/kernels/dup.cpp            |  223 ++
 ggml/src/ggml-cann/kernels/get_row_f16.cpp    |  186 ++
 ggml/src/ggml-cann/kernels/get_row_f32.cpp    |  180 +
 ggml/src/ggml-cann/kernels/get_row_q4_0.cpp   |  193 ++
 ggml/src/ggml-cann/kernels/get_row_q8_0.cpp   |  191 ++
 .../ggml-cann/kernels/quantize_f16_q8_0.cpp   |  208 ++
 .../ggml-cann/kernels/quantize_f32_q8_0.cpp   |  206 ++
 ggml/src/ggml.c                               |   11 +-
 src/llama.cpp                                 |   41 +
 tests/test-backend-ops.cpp                    |   33 +-
 27 files changed, 10756 insertions(+), 8 deletions(-)
 create mode 100644 ggml/include/ggml-cann.h
 create mode 100644 ggml/src/ggml-cann.cpp
 create mode 100644 ggml/src/ggml-cann/.clang-format
 create mode 100644 ggml/src/ggml-cann/Doxyfile
 create mode 100644 ggml/src/ggml-cann/acl_tensor.cpp
 create mode 100644 ggml/src/ggml-cann/acl_tensor.h
 create mode 100644 ggml/src/ggml-cann/aclnn_ops.cpp
 create mode 100644 ggml/src/ggml-cann/aclnn_ops.h
 create mode 100644 ggml/src/ggml-cann/common.h
 create mode 100644 ggml/src/ggml-cann/kernels/CMakeLists.txt
 create mode 100644 ggml/src/ggml-cann/kernels/ascendc_kernels.h
 create mode 100644 ggml/src/ggml-cann/kernels/dup.cpp
 create mode 100644 ggml/src/ggml-cann/kernels/get_row_f16.cpp
 create mode 100644 ggml/src/ggml-cann/kernels/get_row_f32.cpp
 create mode 100644 ggml/src/ggml-cann/kernels/get_row_q4_0.cpp
 create mode 100644 ggml/src/ggml-cann/kernels/get_row_q8_0.cpp
 create mode 100644 ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp
 create mode 100644 ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 67dcf86d4fab75..7937091227ad4c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -106,6 +106,7 @@ llama_option_depr(WARNING     LLAMA_NATIVE              GGML_NATIVE)
 llama_option_depr(WARNING     LLAMA_RPC                 GGML_RPC)
 llama_option_depr(WARNING     LLAMA_SYCL                GGML_SYCL)
 llama_option_depr(WARNING     LLAMA_SYCL_F16            GGML_SYCL_F16)
+llama_option_depr(WARNING     LLAMA_CANN                GGML_CANN)
 
 #
 # build the library
diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp
index d641a9f12b3887..a6497b6e0bf823 100644
--- a/examples/llama-bench/llama-bench.cpp
+++ b/examples/llama-bench/llama-bench.cpp
@@ -23,6 +23,10 @@
 #include "ggml-cuda.h"
 #include "ggml-sycl.h"
 
+#ifdef GGML_USE_CANN
+#include "ggml-cann.h"
+#endif
+
 // utils
 static uint64_t get_time_ns() {
     using clock = std::chrono::high_resolution_clock;
@@ -120,6 +124,17 @@ static std::string get_gpu_info() {
             id += "/";
         }
     }
+#endif
+#ifdef GGML_USE_CANN
+    uint32_t count = ggml_backend_cann_get_device_count();
+    for (uint32_t i = 0; i < count; i++) {
+        char buf[128];
+        ggml_backend_cann_get_device_description(i, buf, sizeof(buf));
+        id += buf;
+        if (i < count - 1) {
+            id += "/";
+        }
+    }
 #endif
     // TODO: other backends
     return id;
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index d6882eec31e1ab..d23e282fbe6552 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -16,6 +16,10 @@
 #include "ggml-metal.h"
 #endif
 
+#ifdef GGML_USE_CANN
+#include "ggml-cann.h"
+#endif
+
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb_image.h"
 
@@ -1001,6 +1005,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
     LOG_TEE("%s: CLIP using Metal backend\n", __func__);
 #endif
 
+#ifdef GGML_USE_CANN
+    new_clip->backend = ggml_backend_cann_init(0);
+    LOG_TEE("%s: CLIP using CANN backend\n", __func__);
+#endif
+
 
     if (!new_clip->backend) {
         new_clip->backend = ggml_backend_cpu_init();
diff --git a/ggml/include/ggml-cann.h b/ggml/include/ggml-cann.h
new file mode 100644
index 00000000000000..ca73211fea7362
--- /dev/null
+++ b/ggml/include/ggml-cann.h
@@ -0,0 +1,125 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml-backend.h"
+#include "ggml.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @brief Maximum number of CANN devices supported.
+ */
+#define GGML_CANN_MAX_DEVICES 16
+
+/**
+ * @brief Initializes the CANN backend for a specified device.
+ *
+ * This function initializes the CANN backend for the given device.
+ * It verifies the device index, allocates a context, and creates a backend
+ * instance.
+ *
+ * @param device The index of the device to initialize.
+ * @return A pointer to the initialized backend instance, or nullptr on failure.
+ */
+GGML_API GGML_CALL ggml_backend_t ggml_backend_cann_init(int32_t device);
+
+/**
+ * @brief Checks if a given backend is a CANN backend.
+ *
+ * This function verifies if the provided backend is a CANN backend by comparing
+ * its GUID with the CANN backend's GUID.
+ *
+ * @param backend The backend instance to check.
+ * @return True if the backend is a CANN backend, false otherwise.
+ */
+GGML_API GGML_CALL bool ggml_backend_is_cann(ggml_backend_t backend);
+
+/**
+ * @brief Retrieves the CANN buffer type for a specified device.
+ *
+ * This function initializes and returns the buffer type interface associated
+ * with the given device. It ensures thread-safe access using a mutex.
+ *
+ * @param device The device index for which to retrieve the buffer type.
+ * @return A pointer to the buffer type interface for the specified device, or
+ * nullptr if the device index is out of range.
+ */
+GGML_API GGML_CALL ggml_backend_buffer_type_t
+ggml_backend_cann_buffer_type(int32_t device);
+
+/**
+ * @brief Retrieves the number of CANN devices available.
+ *
+ * This function returns the number of CANN devices available based on
+ * information obtained from `ggml_cann_info()`.
+ *
+ * @return The number of CANN devices available.
+ */
+GGML_API GGML_CALL int32_t ggml_backend_cann_get_device_count(void);
+
+/**
+ * @brief Retrieves the description of a specific CANN device.
+ *
+ * This function sets the specified device, retrieves the SoC name,
+ * and writes it into the provided description buffer.
+ *
+ * @param device The device index to retrieve the description for.
+ * @param description Pointer to a buffer where the description will be written.
+ * @param description_size Size of the description buffer.
+ */
+GGML_API GGML_CALL void ggml_backend_cann_get_device_description(
+    int32_t device, char* description, size_t description_size);
+
+/**
+ * @brief Retrieves the memory information of a specific CANN device.
+ *
+ * This function sets the specified device, retrieves the free and total
+ * memory information of the specified type (ACL_HBM_MEM), and stores them
+ * in the provided pointers.
+ *
+ * @param device The device index to retrieve memory information for.
+ * @param free Pointer to a variable where the free memory size will be stored.
+ * @param total Pointer to a variable where the total memory size will be
+ * stored.
+ */
+GGML_API GGML_CALL void ggml_backend_cann_get_device_memory(int32_t device,
+                                                            size_t* free,
+                                                            size_t* total);
+
+/**
+ * @brief Set the logging callback for GGML.
+ *
+ * This function sets the logging callback and user data for logging.
+ *
+ * @param log_callback The logging callback to set.
+ * @param user_data User data to pass to the logging callback.
+ */
+GGML_API void ggml_backend_cann_log_set_callback(ggml_log_callback log_callback,
+                                                 void* user_data);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index f2145ff356cbd1..2fdb9fa40274bc 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -753,6 +753,8 @@ extern "C" {
     GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1);
     GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
 
+    GGML_API bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
+
     // use this to compute the memory overhead of a tensor
     GGML_API size_t ggml_tensor_overhead(void);
 
@@ -2397,6 +2399,7 @@ extern "C" {
     GGML_API int ggml_cpu_has_rpc        (void);
     GGML_API int ggml_cpu_has_vsx        (void);
     GGML_API int ggml_cpu_has_matmul_int8(void);
+    GGML_API int ggml_cpu_has_cann       (void);
 
     //
     // Internal types and functions exposed for tests and benchmarks
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index cbadaf4d931c30..3f4c66bf70fc9a 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -770,6 +770,74 @@ if (GGML_CPU_HBM)
     target_link_libraries(ggml PUBLIC memkind)
 endif()
 
+if (GGML_CANN)
+    if ("cann${CANN_INSTALL_DIR}" STREQUAL "cann" AND DEFINED ENV{ASCEND_TOOLKIT_HOME})
+        set(CANN_INSTALL_DIR $ENV{ASCEND_TOOLKIT_HOME})
+        message(STATUS "CANN: updated CANN_INSTALL_DIR from ASCEND_TOOLKIT_HOME=$ENV{ASCEND_TOOLKIT_HOME}")
+    endif()
+
+    if (CANN_INSTALL_DIR)
+        # Only Support Linux.
+        if (GGML_CANN)
+            if (NOT UNIX)
+                set(GGML_CANN OFF)
+                message(WARNING "CANN: CANN toolkit supports unix but not ${CMAKE_SYSTEM_NAME}. Turning off GGML_CANN")
+            endif()
+        endif()
+
+        # Supported platforms: x86-64, arm64
+        if (GGML_CANN)
+            if (CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64")
+            elseif (CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64" OR CMAKE_SYSTEM_PROCESSOR STREQUAL "amd64")
+            else()
+                set(GGML_CANN OFF)
+                message(WARNING "CANN: CANN toolkit supports x86-64 and arm64 but not ${CMAKE_SYSTEM_PROCESSOR}. Turning off GGML_CANN")
+            endif()
+        endif()
+
+        # Set header and libs
+        if(GGML_CANN)
+            set(CANN_INCLUDE_DIRS
+                ${CANN_INSTALL_DIR}/include
+                ${CANN_INSTALL_DIR}/include/aclnn
+                ${CANN_INSTALL_DIR}/acllib/include
+            )
+
+            # TODO: find libs
+            link_directories(
+                ${CANN_INSTALL_DIR}/lib64
+            )
+
+            add_subdirectory(ggml-cann/kernels)
+            list(APPEND CANN_LIBRARIES
+                ascendcl
+                nnopbase
+                opapi
+                acl_op_compiler
+                ascendc_kernels
+            )
+
+            set(GGML_HEADERS_CANN "../include/ggml-cann.h")
+            file(GLOB GGML_SOURCES_CANN "ggml-cann/*.cpp")
+            list(APPEND GGML_SOURCES_CANN "ggml-cann.cpp")
+
+            message(STATUS "CANN: CANN_INCLUDE_DIRS =  ${CANN_INCLUDE_DIRS}")
+            message(STATUS "CANN: CANN_LIBRARIES =  ${CANN_LIBRARIES}")
+
+            set(GGML_EXTRA_LIBS     ${GGML_EXTRA_LIBS}     ${CANN_LIBRARIES} )
+            set(GGML_EXTRA_INCLUDES ${GGML_EXTRA_INCLUDES} ${CANN_INCLUDE_DIRS})
+            list(APPEND GGML_CDEF_PUBLIC GGML_USE_CANN)
+        endif()
+    else()
+        set(GGML_CANN OFF)
+        message(WARNING "CANN: Can't find CANN_INSTALL_DIR, do you forget to source set_var.sh. Turning off GGML_CANN")
+    endif()
+
+    if(NOT GGML_CANN)
+        message(WARNING "CANN: GGML_CANN is turned OFF, see above for details.")
+    endif()
+endif()
+
 function(get_flags CCID CCVER)
     set(C_FLAGS "")
     set(CXX_FLAGS "")
@@ -1184,6 +1252,7 @@ add_library(ggml
             ${GGML_SOURCES_ROCM}      ${GGML_HEADERS_ROCM}
             ${GGML_SOURCES_BLAS}      ${GGML_HEADERS_BLAS}
             ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE}
+            ${GGML_SOURCES_CANN}      ${GGML_HEADERS_CANN}
             ggml-aarch64.c            ggml-aarch64.h
             )
 
diff --git a/ggml/src/ggml-backend.c b/ggml/src/ggml-backend.c
index dbbaa3941febea..01c87efd0db6ec 100644
--- a/ggml/src/ggml-backend.c
+++ b/ggml/src/ggml-backend.c
@@ -445,6 +445,11 @@ GGML_CALL static void ggml_backend_registry_init(void) {
     extern GGML_CALL void ggml_backend_kompute_reg_devices(void);
     ggml_backend_kompute_reg_devices();
 #endif
+
+#ifdef GGML_USE_CANN
+    extern GGML_CALL int ggml_backend_cann_reg_devices(void);
+    ggml_backend_cann_reg_devices();
+#endif
 }
 
 GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
diff --git a/ggml/src/ggml-cann.cpp b/ggml/src/ggml-cann.cpp
new file mode 100644
index 00000000000000..9bf7e332a7e06e
--- /dev/null
+++ b/ggml/src/ggml-cann.cpp
@@ -0,0 +1,2023 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#include "ggml-cann.h"
+
+#include <acl/acl.h>
+#include <stdarg.h>
+
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <mutex>
+
+#include "ggml-backend-impl.h"
+#include "ggml-cann/aclnn_ops.h"
+#include "ggml-cann/common.h"
+
+#define GGML_COMMON_DECL_C
+
+#include "ggml-common.h"
+
+/**
+ * @brief Default logging callback for GGML.
+ *
+ * This function is the default logging callback that logs messages to stderr.
+ *
+ * @param level The log level.
+ * @param msg The log message.
+ * @param user_data User data passed to the callback.
+ */
+static void ggml_cann_default_log_callback(enum ggml_log_level level,
+                                           const char* msg, void* user_data) {
+    GGML_UNUSED(level);
+    GGML_UNUSED(user_data);
+    fprintf(stderr, "%s", msg);
+}
+
+ggml_log_callback ggml_cann_log_callback = ggml_cann_default_log_callback;
+void* ggml_cann_log_user_data = NULL;
+
+GGML_API void ggml_backend_cann_log_set_callback(ggml_log_callback log_callback,
+                                                 void* user_data) {
+    ggml_cann_log_callback = log_callback;
+    ggml_cann_log_user_data = user_data;
+}
+
+#define GGML_CANN_LOG_INFO(...) ggml_cann_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__)
+#define GGML_CANN_LOG_WARN(...) ggml_cann_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__)
+#define GGML_CANN_LOG_ERROR(...) \
+    ggml_cann_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
+
+GGML_ATTRIBUTE_FORMAT(2, 3)
+
+/**
+ * @brief Log a message using the current logging callback.
+ *
+ * This function formats a log message and passes it to the current logging
+ * callback.
+ *
+ * @param level The log level.
+ * @param format The format string for the log message.
+ * @param ... The arguments for the format string.
+ */
+static void ggml_cann_log(enum ggml_log_level level, const char* format, ...) {
+    if (ggml_cann_log_callback != NULL) {
+        va_list args;
+        va_start(args, format);
+        char buffer[128];
+        int len = vsnprintf(buffer, 128, format, args);
+        if (len < 128) {
+            ggml_cann_log_callback(level, buffer, ggml_cann_log_user_data);
+        } else {
+             // vsnprintf adds a null terminator
+            std::vector<char> buffer2(len + 1);
+            va_end(args);
+            va_start(args, format);
+            vsnprintf(&buffer2[0], buffer2.size(), format, args);
+            ggml_cann_log_callback(level, buffer2.data(),
+                                   ggml_cann_log_user_data);
+        }
+        va_end(args);
+    }
+}
+
+/**
+ * @brief Handles CANN errors by printing an error message and aborting.
+ *
+ * @param stmt The statement that caused the error.
+ * @param func The function in which the error occurred.
+ * @param file The file in which the error occurred.
+ * @param line The line number where the error occurred.
+ * @param msg The error message.
+ */
+[[noreturn]] void ggml_cann_error(const char* stmt, const char* func,
+                                  const char* file, int line, const char* msg) {
+    int32_t id = -1;
+    aclrtGetDevice(&id);
+
+    GGML_CANN_LOG_ERROR("CANN error: %s\n", msg);
+    GGML_CANN_LOG_ERROR("  current device: %d, in function %s at %s:%d\n", id, func,
+            file, line);
+    GGML_CANN_LOG_ERROR("  %s\n", stmt);
+    // abort with GGML_ASSERT to get a stack trace
+    GGML_ASSERT(!"CANN error");
+}
+
+/**
+ * @brief Sets the device to be used by CANN.
+ *
+ * @param device The device ID to set.
+ */
+void ggml_cann_set_device(const int32_t device) {
+    // TODO: uncomment these lines after empty context has fixed.
+    // int current_device;
+    // ACL_CHECK(aclrtGetDevice(&current_device));
+
+    // if (device == current_device) {
+    //   return;
+    // }
+    ACL_CHECK(aclrtSetDevice(device));
+}
+
+/**
+ * @brief Retrieves the current device ID.
+ *
+ * @return The current device ID.
+ */
+int32_t ggml_cann_get_device() {
+    int32_t id;
+    ACL_CHECK(aclrtGetDevice(&id));
+    return id;
+}
+
+/**
+ * @brief Initialize the CANN device information.
+ *
+ * This function initializes the CANN device information by obtaining the
+ * device count and setting the memory allocation granularity for each device.
+ *
+ * @return A structure containing the device information.
+ */
+static ggml_cann_device_info ggml_cann_init() {
+    ggml_cann_device_info info = {};
+
+    aclError err = aclrtGetDeviceCount((uint32_t*)&info.device_count);
+
+    if (err != ACL_SUCCESS) {
+        GGML_CANN_LOG_ERROR("%s: failed to initialize CANN: %s\n",
+                __func__, aclGetRecentErrMsg());
+        return info;
+    }
+
+    GGML_ASSERT(info.device_count <= GGML_CANN_MAX_DEVICES);
+
+    for (int id = 0; id < info.device_count; ++id) {
+        aclrtPhysicalMemProp prop = {};
+        prop.handleType = ACL_MEM_HANDLE_TYPE_NONE;
+        prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED;
+        prop.memAttr = ACL_HBM_MEM_HUGE;
+        prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE;
+        prop.location.id = id;
+        prop.reserve = 0;
+        ACL_CHECK(aclrtMemGetAllocationGranularity(
+            &prop, ACL_RT_MEM_ALLOC_GRANULARITY_RECOMMENDED,
+            &info.devices[id].vmm_granularity));
+    }
+
+    // TODO: add more device info later.
+    return info;
+}
+
+/**
+ * @brief Retrieve the CANN device information.
+ *
+ * This function returns a reference to a structure containing the CANN device
+ * information. The device information is initialized once and reused on
+ * subsequent calls.
+ *
+ * @return A reference to the structure containing the device information.
+ */
+const ggml_cann_device_info& ggml_cann_info() {
+    static ggml_cann_device_info info = ggml_cann_init();
+    return info;
+}
+
+//#define DEBUG_CANN_MALLOC
+/**
+ * @brief A pool of CANN buffers(legacy).
+ *
+ * This class manages a pool of CANN buffers for a specific device.
+ */
+struct ggml_cann_pool_leg : public ggml_cann_pool {
+    /**
+     * @brief The maximum number of buffers in the pool.
+     */
+    static const int MAX_BUFFERS = 256;
+
+    /**
+     * @brief The device ID associated with this buffer pool.
+     */
+    int device;
+
+    /**
+     * @brief Structure representing a CANN buffer.
+     */
+    struct ggml_cann_buffer {
+        void* ptr = nullptr;  ///< Pointer to the buffer memory.
+        size_t size = 0;      ///< Size of the buffer.
+    };
+
+    /**
+     * @brief Array of CANN buffers in the pool.
+     */
+    ggml_cann_buffer buffer_pool[MAX_BUFFERS] = {};
+
+    /**
+     * @brief Total size of all buffers in the pool.
+     */
+    size_t pool_size = 0;
+
+    /**
+     * @brief Constructor to initialize the buffer pool for a specific device.
+     *
+     * @param device The device ID to associate with this buffer pool.
+     */
+    explicit ggml_cann_pool_leg(int device) : device(device) {}
+
+    /**
+     * @brief Destructor to free all buffers in the pool.
+     */
+    ~ggml_cann_pool_leg() {
+        ggml_cann_set_device(device);
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cann_buffer& b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+                ACL_CHECK(aclrtFree(b.ptr));
+                pool_size -= b.size;
+            }
+        }
+        GGML_ASSERT(pool_size == 0);
+    }
+
+    /**
+     * @brief Allocate a buffer of the given size.
+     *
+     * @param size The size of the buffer to allocate.
+     * @param actual_size A pointer to a variable to receive the actual size of
+     * the allocated buffer.
+     * @return A pointer to the allocated buffer.
+     */
+    void* alloc(size_t size, size_t* actual_size) override {
+#ifdef DEBUG_CANN_MALLOC
+        int nnz = 0;
+        size_t max_size = 0;
+#endif
+        size_t best_diff = 1ull << 36;
+        int ibest = -1;
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cann_buffer& b = buffer_pool[i];
+            if (b.ptr != nullptr) {
+#ifdef DEBUG_CANN_MALLOC
+                ++nnz;
+                if (b.size > max_size) max_size = b.size;
+#endif
+                if (b.size >= size) {
+                    size_t diff = b.size - size;
+                    if (diff < best_diff) {
+                        best_diff = diff;
+                        ibest = i;
+                        if (!best_diff) {
+                            void* ptr = b.ptr;
+                            *actual_size = b.size;
+                            b.ptr = nullptr;
+                            b.size = 0;
+                            return ptr;
+                        }
+                    }
+                }
+            }
+        }
+        if (ibest >= 0) {
+            ggml_cann_buffer& b = buffer_pool[ibest];
+            void* ptr = b.ptr;
+            *actual_size = b.size;
+            b.ptr = nullptr;
+            b.size = 0;
+            return ptr;
+        }
+        void* ptr;
+        size_t look_ahead_size = (size_t)(1.05 * size);
+        look_ahead_size = 256 * ((look_ahead_size + 255) / 256);
+        ggml_cann_set_device(device);
+        ACL_CHECK(
+            aclrtMalloc(&ptr, look_ahead_size, ACL_MEM_MALLOC_HUGE_FIRST));
+        *actual_size = look_ahead_size;
+        pool_size += look_ahead_size;
+#ifdef DEBUG_CANN_MALLOC
+        GGML_CANN_LOG_INFO(
+            "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, "
+            "requested %u MB\n",
+            __func__, device, nnz, (uint32_t)(max_size / 1024 / 1024),
+            (uint32_t)(pool_size / 1024 / 1024),
+            (uint32_t)(size / 1024 / 1024));
+#endif
+        return ptr;
+    }
+
+    /**
+     * @brief Free a buffer and return it to the pool.
+     *
+     * @param ptr Pointer to the buffer to free.
+     * @param size Size of the buffer to free.
+     */
+    void free(void* ptr, size_t size) override {
+        for (int i = 0; i < MAX_BUFFERS; ++i) {
+            ggml_cann_buffer& b = buffer_pool[i];
+            if (b.ptr == nullptr) {
+                b.ptr = ptr;
+                b.size = size;
+                return;
+            }
+        }
+        // memory should always buffered. these memory may still needed by
+        // tasks in stream.
+        // TODO, fix me.
+        GGML_ASSERT(!"Cann buffer pool full, increase MAX_CANN_BUFFERS\n");
+    }
+};
+
+/**
+ * @brief A pool of CANN buffers with virtual memory.
+ *
+ * This class manages a pool of CANN buffers with virtual memory for a specific
+ * device.
+ */
+struct ggml_cann_pool_vmm : public ggml_cann_pool {
+    /**
+     * @brief The maximum size of the virtual memory pool (32 GB).
+     */
+    static const size_t CANN_POOL_VMM_MAX_SIZE = 1ull << 35;  // 32 GB
+
+    /**
+     * @brief The device ID associated with this buffer pool.
+     */
+    int device;
+
+    /**
+     * @brief Pointer to the start of the virtual memory pool.
+     */
+    void* pool_addr = 0;
+
+    /**
+     * @brief Amount of virtual memory used in the pool.
+     */
+    size_t pool_used = 0;
+
+    /**
+     * @brief Total size of the virtual memory pool.
+     */
+    size_t pool_size = 0;
+
+    /**
+     * @brief Allocation granularity for the virtual memory pool.
+     */
+    size_t granularity;
+
+    /**
+     * @brief Handles for the physical memory allocated.
+     */
+    std::vector<aclrtDrvMemHandle> handles;
+
+    /**
+     * @brief Offsets for the mapped memory regions.
+     */
+    std::vector<void*> map_offsets;
+
+    /**
+     * @brief Constructor to initialize the buffer pool with virtual memory for
+     * a specific device.
+     *
+     * @param device The device ID to associate with this buffer pool.
+     */
+    explicit ggml_cann_pool_vmm(int device)
+        : device(device),
+          granularity(ggml_cann_info().devices[device].vmm_granularity) {}
+
+    /**
+     * @brief Destructor to free all buffers in the virtual memory pool.
+     */
+    ~ggml_cann_pool_vmm() {
+        if (pool_addr != 0) {
+            for (auto& offset : map_offsets) {
+                ACL_CHECK(aclrtUnmapMem(offset));
+            }
+            for (auto& handle : handles) {
+                ACL_CHECK(aclrtFreePhysical(handle));
+            }
+            ACL_CHECK(aclrtReleaseMemAddress(pool_addr));
+        }
+    }
+
+    /**
+     * @brief Allocate a buffer of the given size in the virtual memory pool.
+     *
+     * @param size The size of the buffer to allocate.
+     * @param actual_size A pointer to a variable to receive the actual size of
+     * the allocated buffer.
+     * @return A pointer to the allocated buffer.
+     */
+    void* alloc(size_t size, size_t* actual_size) override {
+        // round up the allocation size to the alignment to ensure that all
+        // allocations are aligned for all data types
+        const size_t alignment = 128;
+        size = alignment * ((size + alignment - 1) / alignment);
+
+        size_t avail = pool_size - pool_used;
+
+        if (size > avail) {
+            // round up to the next multiple of the granularity
+            size_t reserve_size = size - avail;
+            reserve_size =
+                granularity * ((reserve_size + granularity - 1) / granularity);
+
+            GGML_ASSERT(pool_size + reserve_size <= CANN_POOL_VMM_MAX_SIZE);
+
+            // allocate more physical memory
+            aclrtPhysicalMemProp prop = {};
+            prop.handleType = ACL_MEM_HANDLE_TYPE_NONE;
+            prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED;
+            prop.memAttr = ACL_HBM_MEM_HUGE;
+            prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE;
+            prop.location.id = device;
+            prop.reserve = 0;
+            aclrtDrvMemHandle handle;
+            ACL_CHECK(aclrtMallocPhysical(&handle, reserve_size, &prop, 0));
+
+            // reserve virtual address space (if not already reserved)
+            if (pool_addr == 0) {
+                ACL_CHECK(aclrtReserveMemAddress(
+                    &pool_addr, CANN_POOL_VMM_MAX_SIZE, 0, NULL, 1));
+            }
+
+            // map at the end of the pool
+            ACL_CHECK(aclrtMapMem((char*)pool_addr + pool_size, reserve_size, 0,
+                                  handle, 0));
+
+            handles.push_back(handle);
+            map_offsets.push_back((char*)pool_addr + pool_size);
+
+            // add to the pool
+            pool_size += reserve_size;
+
+            // GGML_CANN_LOG_INFO("cann pool[%d]: size increased to %llu MB (
+            // reserved %llu MB)\n",
+            //       device, (unsigned long long) (pool_size/1024/1024),
+            //       (unsigned long long) (reserve_size/1024/1024));
+        }
+
+        GGML_ASSERT(pool_addr != 0);
+
+        void* ptr = (void*)((char*)pool_addr + pool_used);
+        *actual_size = size;
+        pool_used += size;
+
+#ifdef DEBUG_CANN_MALLOC
+        GGML_CANN_LOG_INFO("cann pool[%d]: allocated %llu bytes at %llx\n", device,
+               (unsigned long long)size, (unsigned long long)ptr);
+#endif
+        return ptr;
+    }
+
+    /**
+     * @brief Free a buffer and return it to the virtual memory pool.
+     *
+     * @param ptr Pointer to the buffer to free.
+     * @param size Size of the buffer to free.
+     */
+    void free(void* ptr, size_t size) override {
+#ifdef DEBUG_CANN_MALLOC
+        GGML_CANN_LOG_INFO("cann pool[%d]: freed %llu bytes at %llx\n", device,
+               (unsigned long long)size, (unsigned long long)ptr);
+#endif
+
+        pool_used -= size;
+
+        // all deallocations must be in reverse order of the allocations
+        GGML_ASSERT(ptr == (void*)((char*)pool_addr + pool_used));
+    }
+};
+
+/**
+ * @brief Create a new CANN pool for a specific device.
+ *
+ * Factory method to create a new CANN pool object based on the device type.
+ *
+ * @param device The device ID for which to create the pool.
+ * @return A unique pointer to the created CANN pool.
+ */
+std::unique_ptr<ggml_cann_pool> ggml_backend_cann_context::new_pool_for_device(
+    int device) {
+    // return std::unique_ptr<ggml_cann_pool>(new ggml_cann_pool_leg(device));
+    return std::unique_ptr<ggml_cann_pool>(new ggml_cann_pool_vmm(device));
+}
+
+// cann buffer
+/**
+ * @brief Context for managing a CANN buffer associated with a specific device.
+ *
+ * This structure holds information about a CANN buffer, including the device
+ * ID, device pointer, and a name derived from GGML_CANN_NAME and the device ID.
+ */
+struct ggml_backend_cann_buffer_context {
+    int32_t device;  ///< The device ID associated with this buffer context.
+    void* dev_ptr =
+        nullptr;  ///< Pointer to the device memory allocated for the buffer.
+
+    /**
+     * @brief Constructor to initialize the CANN buffer context.
+     *
+     * @param device The device ID associated with this buffer context.
+     * @param dev_ptr Pointer to the device memory allocated for the buffer.
+     */
+    ggml_backend_cann_buffer_context(int32_t device, void* dev_ptr)
+        : device(device),
+          dev_ptr(dev_ptr) {}
+
+    /**
+     * @brief Destructor to free the device memory allocated for the buffer.
+     */
+    ~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); }
+};
+
+/**
+ * @brief Retrieve the name associated with a CANN buffer.
+ *
+ * This function returns the name of a CANN buffer, which is stored in the
+ * context of the buffer.
+ *
+ * @param buffer The CANN buffer whose name is to be retrieved.
+ * @return A pointer to a C-string containing the name of the buffer.
+ */
+
+GGML_CALL static const char* ggml_backend_cann_buffer_get_name(
+    ggml_backend_buffer_t buffer) {
+    return "CANN";
+
+    GGML_UNUSED(buffer);
+}
+
+/**
+ * @brief Check if a buffer is a CANN buffer.
+ *
+ * This function checks if a given buffer is a CANN buffer by comparing its
+ * `get_name` function pointer to `ggml_backend_cann_buffer_get_name`.
+ *
+ * @param buffer The buffer to check.
+ * @return true if the buffer is a CANN buffer, false otherwise.
+ */
+GGML_CALL static bool ggml_backend_buffer_is_cann(
+    ggml_backend_buffer_t buffer) {
+    return buffer->iface.get_name == ggml_backend_cann_buffer_get_name;
+}
+
+/**
+ * @brief Free resources associated with a CANN buffer.
+ *
+ * This function frees the resources associated with a CANN buffer, including
+ * its context.
+ *
+ * @param buffer The CANN buffer to free.
+ */
+GGML_CALL static void ggml_backend_cann_buffer_free_buffer(
+    ggml_backend_buffer_t buffer) {
+    ggml_backend_cann_buffer_context* ctx =
+        (ggml_backend_cann_buffer_context*)buffer->context;
+    delete ctx;
+}
+
+/**
+ * @brief Retrieve the base pointer of a CANN buffer.
+ *
+ * This function returns the base pointer of a CANN buffer, which points to the
+ * device memory allocated for the buffer.
+ *
+ * @param buffer The CANN buffer whose base pointer is to be retrieved.
+ * @return A pointer to the base of the device memory allocated for the buffer.
+ */
+GGML_CALL static void* ggml_backend_cann_buffer_get_base(
+    ggml_backend_buffer_t buffer) {
+    ggml_backend_cann_buffer_context* ctx =
+        (ggml_backend_cann_buffer_context*)buffer->context;
+    return ctx->dev_ptr;
+}
+
+/**
+ * @brief Transform quantized Q4.0 tensor data into a format suitable for CANN
+ * processing.
+ *
+ * This function transforms quantized Q4.0 tensor data into a format suitable
+ * for CANN processing. It extracts quantization values and scales from the
+ * source data and prepares them in a format expected by CANN operations.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source data in Q4.0 format.
+ * @param dst Pointer to the destination buffer where transformed data will be
+ * stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor,
+                                                       const void* src,
+                                                       void* dst) {
+    GGML_ASSERT(tensor->op == GGML_OP_NONE);
+
+    int64_t n_elems = ggml_nelements(tensor);
+    int64_t groups = n_elems / QK4_0;
+    size_t quant_bytes = n_elems * sizeof(uint8_t) / 2;
+
+    uint8_t* quant_offset = (uint8_t*)dst;
+    uint16_t* scale_offset = (uint16_t*)((char*)dst + quant_bytes);
+
+    for (int i = 0; i < groups; i++) {
+        const block_q4_0* group =
+            (const block_q4_0*)((const char*)src + i * sizeof(block_q4_0));
+        *scale_offset = group->d;
+        scale_offset++;
+
+        // 0-15
+        for (int j = 0; j < QK4_0 / 2; j += 2) {
+            (*quant_offset) = (group->qs[j] & 0x0F);
+            (*quant_offset) |= ((group->qs[j + 1] << 4));
+            quant_offset++;
+        }
+
+        // 16-31
+        for (int j = 0; j < QK4_0 / 2; j += 2) {
+            (*quant_offset) = (group->qs[j] >> 4);
+            (*quant_offset) |= (group->qs[j + 1] & 0xF0);
+            quant_offset++;
+        }
+    }
+
+    // put (uint4b_t -8) into int4b_t
+    for (quant_offset = (uint8_t*)dst;
+         quant_offset < (uint8_t*)dst + quant_bytes; quant_offset++) {
+        (*quant_offset) ^= 0x88;
+    }
+}
+
+/**
+ * @brief Transform CANN processed data back into quantized Q4.0 format.
+ *
+ * This function transforms CANN processed data back into quantized Q4.0 format.
+ * It reverses the transformation performed by
+ * ggml_backend_cann_transform_q4_0(), converting the data back into its
+ * original quantized form.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source buffer containing transformed data.
+ * @param dst Pointer to the destination buffer where the Q4.0 formatted data
+ * will be stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform_back_q4_0(
+    const ggml_tensor* tensor, void* src, void* dst) {
+    GGML_ASSERT(tensor->op == GGML_OP_NONE);
+
+    int64_t n_elems = ggml_nelements(tensor);
+    int64_t groups = n_elems / QK4_0;
+    size_t quant_bytes = n_elems * sizeof(uint8_t) / 2;
+
+    uint8_t* quant_offset = (uint8_t*)src;
+    uint16_t* scale_offset = (uint16_t*)((char*)src + quant_bytes);
+
+    for (; quant_offset < (uint8_t*)src + quant_bytes; quant_offset++) {
+        (*quant_offset) ^= 0x88;
+    }
+    quant_offset = (uint8_t*)src;
+
+    for (int i = 0; i < groups; i++) {
+        block_q4_0* group = (block_q4_0*)((char*)dst + i * sizeof(block_q4_0));
+        group->d = *scale_offset;
+        scale_offset++;
+
+        // 0-15
+        for (int j = 0; j < QK4_0 / 2; j += 2) {
+            group->qs[j] = ((*quant_offset) & 0x0F);
+            group->qs[j + 1] = ((*quant_offset) >> 4);
+            quant_offset++;
+        }
+
+        // 16-31
+        for (int j = 0; j < QK4_0 / 2; j += 2) {
+            group->qs[j] |= ((*quant_offset) << 4);
+            group->qs[j + 1] |= ((*quant_offset) & 0xF0);
+            quant_offset++;
+        }
+    }
+}
+
+/**
+ * @brief Transform quantized Q8.0 tensor data into a format suitable for CANN
+ * processing.
+ *
+ * This function transforms quantized Q8.0 tensor data into a format suitable
+ * for CANN processing. It extracts quantization values and scales from the
+ * source data and prepares them in a format expected by CANN operations.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source data in Q8.0 format.
+ * @param dst Pointer to the destination buffer where transformed data will be
+ * stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform_q8_0(ggml_tensor* tensor,
+                                                       const void* src,
+                                                       void* dst) {
+    int64_t n_elems = ggml_nelements(tensor);
+    int64_t groups = n_elems / QK8_0;
+    size_t quant_bytes = n_elems * sizeof(uint8_t);
+
+    uint8_t* quant_offset = (uint8_t*)dst;
+    uint16_t* scale_offset = (uint16_t*)((char*)dst + quant_bytes);
+
+    for (int i = 0; i < groups; i++) {
+        const block_q8_0* group =
+            (const block_q8_0*)((const char*)src + i * sizeof(block_q8_0));
+        *scale_offset = group->d;
+        scale_offset++;
+        size_t group_quant_size = QK8_0 * sizeof(uint8_t);
+        memcpy(quant_offset, group->qs, group_quant_size);
+        quant_offset += group_quant_size;
+    }
+}
+
+/**
+ * @brief Transform CANN processed data back into quantized Q8.0 format.
+ *
+ * This function transforms CANN processed data back into quantized Q8.0 format.
+ * It reverses the transformation performed by
+ * ggml_backend_cann_transform_q8_0(), converting the data back into its
+ * original quantized form.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source buffer containing transformed data.
+ * @param dst Pointer to the destination buffer where the Q8.0 formatted data
+ * will be stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform_back_q8_0(
+    const ggml_tensor* tensor, const void* src, void* dst) {
+    int64_t n_elems = ggml_nelements(tensor);
+    int64_t groups = n_elems / QK8_0;
+    size_t quant_bytes = n_elems * sizeof(uint8_t);
+
+    const uint8_t* quant_offset = (const uint8_t*)src;
+    const uint16_t* scale_offset =
+        (const uint16_t*)((const char*)src + quant_bytes);
+
+    for (int i = 0; i < groups; i++) {
+        block_q8_0* group = (block_q8_0*)((char*)dst + i * sizeof(block_q8_0));
+        group->d = *scale_offset;
+        scale_offset++;
+        size_t group_quant_size = QK8_0 * sizeof(uint8_t);
+        memcpy(group->qs, quant_offset, group_quant_size);
+        quant_offset += group_quant_size;
+    }
+}
+
+/**
+ * @brief Transform tensor data based on its type for CANN processing.
+ *
+ * This function transforms tensor data based on its quantization type for CANN
+ * processing. It dispatches the transformation based on the tensor's type to
+ * specialized functions handling Q4.0 and Q8.0 formats.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source data to be transformed.
+ * @param dst Pointer to the destination buffer where transformed data will be
+ * stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform(ggml_tensor* tensor,
+                                                  const void* src, void* dst) {
+    switch (tensor->type) {
+        case GGML_TYPE_Q4_0:
+            ggml_backend_cann_transform_q4_0(tensor, src, dst);
+            break;
+        case GGML_TYPE_Q8_0:
+            ggml_backend_cann_transform_q8_0(tensor, src, dst);
+            break;
+        default:
+            break;
+    }
+}
+
+/**
+ * @brief Transform CANN processed data back into tensor data based on its type.
+ *
+ * This function transforms CANN processed data back into tensor data based on
+ * its quantization type for Q4.0 and Q8.0 formats. It dispatches the
+ * transformation based on the tensor's type to specialized functions.
+ *
+ * @param tensor Pointer to the tensor information.
+ * @param src Pointer to the source data containing CANN processed data.
+ * @param dst Pointer to the destination buffer where transformed tensor data
+ * will be stored.
+ */
+GGML_CALL static void ggml_backend_cann_transform_back(
+    const ggml_tensor* tensor, void* src, void* dst) {
+    switch (tensor->type) {
+        case GGML_TYPE_Q4_0:
+            ggml_backend_cann_transform_back_q4_0(tensor, src, dst);
+            break;
+        case GGML_TYPE_Q8_0:
+            ggml_backend_cann_transform_back_q8_0(tensor, src, dst);
+            break;
+        default:
+            break;
+    }
+}
+
+/**
+ * @brief Check if transformation is needed for a given tensor type.
+ *
+ * This function checks if transformation is needed for a given tensor type
+ * to prepare data for CANN processing.
+ *
+ * @param type The tensor type to check.
+ * @return true if transformation is needed, false otherwise.
+ */
+GGML_CALL static bool need_transform(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+            return true;
+        default:
+            return false;
+    }
+}
+
+/**
+ * @brief Initialize a tensor using data from a CANN buffer.
+ *
+ * This function initializes a tensor using data from a CANN buffer.
+ * It handles special cases such as views and quantization.
+ *
+ * @param buffer The CANN buffer from which to initialize the tensor.
+ * @param tensor Pointer to the tensor to be initialized.
+ */
+GGML_CALL static void ggml_backend_cann_buffer_init_tensor(
+    ggml_backend_buffer_t buffer, ggml_tensor* tensor) {
+    if (tensor->view_src != NULL && tensor->view_offs == 0) {
+        GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft);
+        return;
+    }
+
+    // TODO: can backend doesn't support quantized yet. Just leave the code
+    // here.
+    if (ggml_is_quantized(tensor->type)) {
+        // Initialize padding to 0 to avoid possible NaN values
+        size_t original_size = ggml_nbytes(tensor);
+        size_t padded_size =
+            ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
+
+        if (padded_size > original_size && tensor->view_src == nullptr) {
+            size_t memset_size = padded_size - original_size;
+            ACL_CHECK(aclrtMemset((char*)tensor->data + original_size,
+                                  memset_size, 0, memset_size));
+        }
+    }
+}
+
+// TODO: need handle tensor which has paddings.
+/**
+ * @brief Set tensor data in a CANN buffer.
+ *
+ * This function sets tensor data in a CANN buffer, handling transformations
+ * if needed based on the tensor's type.
+ *
+ * @param buffer The CANN buffer where the tensor data will be set.
+ * @param tensor Pointer to the tensor whose data will be set.
+ * @param data Pointer to the source data to be copied into the tensor.
+ * @param offset Offset in the source data from where to start copying.
+ * @param size Size of the data to be copied, in bytes.
+ */
+GGML_CALL static void ggml_backend_cann_buffer_set_tensor(
+    ggml_backend_buffer_t buffer, ggml_tensor* tensor, const void* data,
+    size_t offset, size_t size) {
+    // GGML_ASSERT(size == ggml_nbytes(tensor));
+    ggml_backend_cann_buffer_context* ctx =
+        (ggml_backend_cann_buffer_context*)buffer->context;
+
+    ggml_cann_set_device(ctx->device);
+    // TODO: refer to cann(#6017), it use thread's default stream.
+    // For acl, synchronous functions use this default stream.
+    // Why aclrtSynchronizeDevice?
+
+    if (!need_transform(tensor->type)) {
+        ACL_CHECK(aclrtMemcpy(tensor->data, size, (const char*)data + offset,
+                              size, ACL_MEMCPY_HOST_TO_DEVICE));
+    } else {
+        void* transform_buffer = malloc(size);
+        ggml_backend_cann_transform(tensor, (const char*)data + offset,
+                                    transform_buffer);
+
+#ifndef NDEBUG
+        void* check_buffer = malloc(size);
+        ggml_backend_cann_transform_back(tensor, transform_buffer,
+                                         check_buffer);
+        GGML_ASSERT(memcmp((const char*)data + offset, check_buffer, size) ==
+                    0);
+        free(check_buffer);
+#endif
+        ACL_CHECK(aclrtMemcpy(tensor->data, size, transform_buffer, size,
+                              ACL_MEMCPY_HOST_TO_DEVICE));
+        free(transform_buffer);
+    }
+}
+
+/**
+ * @brief Get tensor data from a CANN buffer.
+ *
+ * This function retrieves tensor data from a CANN buffer, handling
+ * transformations if needed based on the tensor's type.
+ *
+ * @param buffer The CANN buffer from which to retrieve tensor data.
+ * @param tensor Pointer to the tensor whose data will be retrieved.
+ * @param data Pointer to the destination buffer where the tensor data will be
+ * copied.
+ * @param offset Offset in the destination buffer where to start copying.
+ * @param size Size of the data to be copied, in bytes.
+ */
+GGML_CALL static void ggml_backend_cann_buffer_get_tensor(
+    ggml_backend_buffer_t buffer, const ggml_tensor* tensor, void* data,
+    size_t offset, size_t size) {
+    GGML_ASSERT(size == ggml_nbytes(tensor));
+    ggml_backend_cann_buffer_context* ctx =
+        (ggml_backend_cann_buffer_context*)buffer->context;
+
+    ggml_cann_set_device(ctx->device);
+
+    if (!need_transform(tensor->type)) {
+        ACL_CHECK(aclrtMemcpy((char*)data + offset, size, tensor->data, size,
+                              ACL_MEMCPY_DEVICE_TO_HOST));
+    } else {
+        void* transform_buffer = malloc(size);
+        ACL_CHECK(aclrtMemcpy(transform_buffer, size, tensor->data, size,
+                              ACL_MEMCPY_DEVICE_TO_HOST));
+        ggml_backend_cann_transform_back(tensor, transform_buffer,
+                                         (char*)data + offset);
+        free(transform_buffer);
+    }
+}
+
+/**
+ * @brief Copy tensor data between CANN buffers if possible.
+ *
+ * This function copies tensor data between CANN buffers if the source and
+ * destination buffers are CANN buffers and they meet the necessary conditions
+ * (same device or devices can access each other).
+ *
+ * @param buffer The destination CANN buffer where the tensor data will be
+ * copied.
+ * @param src Pointer to the source tensor whose data will be copied.
+ * @param dst Pointer to the destination tensor where the data will be copied.
+ * @return true if the copy operation succeeded, false otherwise.
+ */
+GGML_CALL static bool ggml_backend_cann_buffer_cpy_tensor(
+    ggml_backend_buffer_t buffer, const ggml_tensor* src, ggml_tensor* dst) {
+    if (ggml_backend_buffer_is_cann(src->buffer)) {
+        ggml_backend_cann_buffer_context* src_ctx =
+            (ggml_backend_cann_buffer_context*)src->buffer->context;
+        ggml_backend_cann_buffer_context* dst_ctx =
+            (ggml_backend_cann_buffer_context*)buffer->context;
+
+        size_t memcpy_size = ggml_nbytes(src);
+        // Same device.
+        if (src_ctx->device == dst_ctx->device) {
+            ACL_CHECK(aclrtMemcpy((char*)dst->data, memcpy_size,
+                                  (const char*)src->data, memcpy_size,
+                                  ACL_MEMCPY_DEVICE_TO_DEVICE));
+            return true;
+        } else {
+            // Different device but can access by peer.
+            int32_t canAccessPeer = 0;
+            ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, src_ctx->device,
+                                               dst_ctx->device));
+            if (canAccessPeer) {
+                ggml_cann_set_device(src_ctx->device);
+                ACL_CHECK(aclrtDeviceEnablePeerAccess(dst_ctx->device, 0));
+                ACL_CHECK(aclrtMemcpy((char*)dst->data, memcpy_size,
+                                      (const char*)src->data, memcpy_size,
+                                      ACL_MEMCPY_DEVICE_TO_DEVICE));
+                return true;
+            }
+        }
+    }
+    return false;
+}
+
+/**
+ * @brief Clear a CANN buffer by setting all its memory to a specified value.
+ *
+ * This function clears a CANN buffer by setting all its memory to a specified
+ * value.
+ *
+ * @param buffer The CANN buffer to be cleared.
+ * @param value The value to which each byte in the buffer will be set.
+ */
+GGML_CALL static void ggml_backend_cann_buffer_clear(
+    ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_backend_cann_buffer_context* ctx =
+        (ggml_backend_cann_buffer_context*)buffer->context;
+
+    ggml_cann_set_device(ctx->device);
+    ACL_CHECK(aclrtMemset(ctx->dev_ptr, buffer->size, value, buffer->size));
+}
+
+/**
+ * @brief Interface for a CANN buffer in the backend.
+ *
+ * This structure defines function pointers to operations that can be performed
+ * on a CANN buffer within the backend.
+ */
+static ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
+    /* .get_name        = */ ggml_backend_cann_buffer_get_name,
+    /* .free_buffer     = */ ggml_backend_cann_buffer_free_buffer,
+    /* .get_base        = */ ggml_backend_cann_buffer_get_base,
+    /* .init_tensor     = */ ggml_backend_cann_buffer_init_tensor,
+    /* .set_tensor      = */ ggml_backend_cann_buffer_set_tensor,
+    /* .get_tensor      = */ ggml_backend_cann_buffer_get_tensor,
+    /* .cpy_tensor      = */ ggml_backend_cann_buffer_cpy_tensor,
+    /* .clear           = */ ggml_backend_cann_buffer_clear,
+    /* .reset           = */ NULL,
+};
+
+// cann buffer type
+/**
+ * @brief Structure representing context information for a specific backend
+ * buffer type.
+ */
+struct ggml_backend_cann_buffer_type_context {
+    int32_t
+        device; /**< Device identifier associated with the buffer context. */
+    std::string name; /**< Name associated with the buffer context. */
+};
+
+/**
+ * @brief Retrieves the name associated with a CANN buffer type.
+ *
+ * This function returns the descriptive name associated with the specified
+ * CANN buffer type context.
+ *
+ * @param buft Pointer to the buffer type context.
+ * @return Const pointer to the C-style string containing the name.
+ */
+GGML_CALL static const char* ggml_backend_cann_buffer_type_name(
+    ggml_backend_buffer_type_t buft) {
+    return "CANN";
+
+    GGML_UNUSED(buft);
+}
+
+/**
+ * @brief Allocates a new CANN buffer of the specified type and size.
+ *
+ * This function allocates a new CANN buffer on the specified device with the
+ * given size.
+ *
+ * @param buft Pointer to the buffer type context.
+ * @param size Size in bytes of the buffer to allocate.
+ * @return Pointer to the allocated buffer, or nullptr if allocation fails.
+ */
+GGML_CALL static ggml_backend_buffer_t
+ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
+                                           size_t size) {
+    ggml_backend_cann_buffer_type_context* buft_ctx =
+        (ggml_backend_cann_buffer_type_context*)buft->context;
+
+    ggml_cann_set_device(buft_ctx->device);
+
+    size = std::max(size, (size_t)1);
+
+    void* dev_ptr;
+    aclError err = aclrtMalloc(&dev_ptr, size, ACL_MEM_MALLOC_HUGE_FIRST);
+    if (err != ACL_SUCCESS) {
+        GGML_CANN_LOG_ERROR(
+            "%s: allocating %.2f MiB on device %d: aclrtMalloc failed: %s\n",
+            __func__, size / 1024.0 / 1024.0, buft_ctx->device,
+            aclGetRecentErrMsg());
+        return nullptr;
+    }
+
+    ggml_backend_cann_buffer_context* ctx =
+        new ggml_backend_cann_buffer_context(buft_ctx->device, dev_ptr);
+
+    return ggml_backend_buffer_init(buft, ggml_backend_cann_buffer_interface,
+                                    ctx, size);
+}
+
+/**
+ * @brief Retrieves the memory alignment requirement for CANN buffers of this
+ * type.
+ *
+ * This function returns the alignment requirement in bytes for memory allocated
+ * by the CANN buffer type.
+ *
+ * @param buft Pointer to the buffer type context (unused in this
+ * implementation).
+ * @return The alignment requirement in bytes (fixed at 128 bytes for CANN
+ * buffers).
+ */
+GGML_CALL static size_t ggml_backend_cann_buffer_type_get_alignment(
+    ggml_backend_buffer_type_t buft) {
+    return 128;
+
+    GGML_UNUSED(buft);
+}
+
+/**
+ * @brief Calculates the allocation size required for a tensor in a CANN buffer.
+ *
+ * Computes the total allocation size needed for storing the tensor's data in a
+ * CANN buffer, considering any necessary padding or adjustments for quantized
+ * types.
+ *
+ * @param buft Pointer to the buffer type context (unused in this
+ * implementation).
+ * @param tensor Pointer to the tensor for which the allocation size is
+ * calculated.
+ * @return The total allocation size in bytes required for the tensor in the
+ * CANN buffer.
+ */
+GGML_CALL static size_t ggml_backend_cann_buffer_type_get_alloc_size(
+    ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) {
+    size_t size = ggml_nbytes(tensor);
+    int64_t ne0 = tensor->ne[0];
+
+    // last line must bigger than 32, because every single op deal at
+    // least 32 bytes.
+    // TODO: quantized type?
+    // int64_t line_size = ne0 * ggml_element_size(tensor);
+    // int64_t line_size_align_32 = (line_size + 31) & ~31;
+    // size += (line_size_align_32 - line_size);
+
+    // TODO: not support quantized yet.
+    // TODO: consider un-continue tensor.
+    if (ggml_is_quantized(tensor->type)) {
+        if (ne0 % MATRIX_ROW_PADDING != 0) {
+            size += ggml_row_size(
+                tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
+        }
+    }
+
+    return size;
+
+    GGML_UNUSED(buft);
+}
+
+/**
+ * @brief Interface for managing CANN buffer types in the GGML backend.
+ *
+ * Provides function pointers for allocating, querying properties, and managing
+ * memory for CANN buffer types in the GGML backend.
+ */
+static ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface = {
+    /* .get_name         = */ ggml_backend_cann_buffer_type_name,
+    /* .alloc_buffer     = */ ggml_backend_cann_buffer_type_alloc_buffer,
+    /* .get_alignment    = */ ggml_backend_cann_buffer_type_get_alignment,
+    /* .get_max_size     = */ NULL,  // defaults to SIZE_MAX
+    /* .get_alloc_size   = */ ggml_backend_cann_buffer_type_get_alloc_size,
+    /* .is_host          = */ NULL,
+};
+
+/**
+ * @brief Retrieves the CANN buffer type for a specified device.
+ *
+ * This function initializes and returns the buffer type interface associated
+ * with the given device. It ensures thread-safe access using a mutex.
+ *
+ * @param device The device index for which to retrieve the buffer type.
+ * @return A pointer to the buffer type interface for the specified device, or
+ * nullptr if the device index is out of range.
+ */
+GGML_CALL ggml_backend_buffer_type_t
+ggml_backend_cann_buffer_type(int32_t device) {
+    static std::mutex mutex;
+    std::lock_guard<std::mutex> lock(mutex);
+
+    if (device >= ggml_backend_cann_get_device_count()) {
+        return nullptr;
+    }
+
+    static ggml_backend_buffer_type
+        ggml_backend_cann_buffer_types[GGML_CANN_MAX_DEVICES];
+
+    static bool ggml_backend_cann_buffer_type_initialized = false;
+
+    if (!ggml_backend_cann_buffer_type_initialized) {
+        for (int32_t i = 0; i < GGML_CANN_MAX_DEVICES; i++) {
+            ggml_backend_cann_buffer_types[i] = {
+                /* .iface    = */ ggml_backend_cann_buffer_type_interface,
+                /* .context  = */
+                 new ggml_backend_cann_buffer_type_context{
+                    i, "CANN" + std::to_string(i)},
+            };
+        }
+        ggml_backend_cann_buffer_type_initialized = true;
+    }
+
+    return &ggml_backend_cann_buffer_types[device];
+}
+
+/**
+ * @brief Computes the forward operation for a given tensor using CANN
+ * operations.
+ *
+ * This function selects the appropriate CANN operation based on the type of
+ * operation specified in the tensor and performs the computation.
+ *
+ * @param ctx The CANN context containing necessary resources and
+ * configurations.
+ * @param dst The destination tensor where the result of the computation will be
+ * stored.
+ * @return true if the computation was successful; false otherwise.
+ */
+static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
+                                      struct ggml_tensor* dst) {
+    switch (dst->op) {
+        case GGML_OP_REPEAT:
+            ggml_cann_repeat(ctx, dst);
+            break;
+        case GGML_OP_GET_ROWS:
+            ggml_cann_get_rows(ctx, dst);
+            break;
+        case GGML_OP_DUP:
+            ggml_cann_dup(ctx, dst);
+            break;
+        case GGML_OP_ADD:
+            ggml_cann_add(ctx, dst);
+            break;
+        case GGML_OP_ACC:
+            ggml_cann_acc(ctx, dst);
+            break;
+        case GGML_OP_MUL:
+            ggml_cann_mul_div<aclnnMulGetWorkspaceSize, aclnnMul>(ctx, dst);
+            break;
+        case GGML_OP_DIV:
+            ggml_cann_mul_div<aclnnDivGetWorkspaceSize, aclnnDiv>(ctx, dst);
+            break;
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(dst)) {
+                case GGML_UNARY_OP_GELU:
+                    ggml_cann_activation<aclnnGeluGetWorkspaceSize, aclnnGelu>(
+                        ctx, dst);
+                    break;
+                case GGML_UNARY_OP_SILU:
+                    ggml_cann_activation<aclnnSiluGetWorkspaceSize, aclnnSilu>(
+                        ctx, dst);
+                    break;
+                // TODO: Use faster gelu??
+                case GGML_UNARY_OP_GELU_QUICK:
+                    ggml_cann_activation<aclnnGeluGetWorkspaceSize, aclnnGelu>(
+                        ctx, dst);
+                    break;
+                case GGML_UNARY_OP_TANH:
+                    ggml_cann_activation<aclnnTanhGetWorkspaceSize, aclnnTanh>(
+                        ctx, dst);
+                    break;
+                case GGML_UNARY_OP_RELU:
+                    ggml_cann_activation<aclnnReluGetWorkspaceSize, aclnnRelu>(
+                        ctx, dst);
+                    break;
+                case GGML_UNARY_OP_HARDSIGMOID:
+                    ggml_cann_activation<aclnnHardsigmoidGetWorkspaceSize,
+                                         aclnnHardsigmoid>(ctx, dst);
+                    break;
+                case GGML_UNARY_OP_HARDSWISH:
+                    ggml_cann_activation<aclnnHardswishGetWorkspaceSize,
+                                         aclnnHardswish>(ctx, dst);
+                    break;
+                default:
+                    return false;
+            }
+            break;
+        case GGML_OP_NORM:
+            ggml_cann_norm(ctx, dst);
+            break;
+        case GGML_OP_GROUP_NORM:
+            ggml_cann_group_norm(ctx, dst);
+            break;
+        case GGML_OP_CONCAT:
+            ggml_cann_concat(ctx, dst);
+            break;
+        case GGML_OP_UPSCALE:
+            ggml_cann_upsample_nearest2d(ctx, dst);
+            break;
+        case GGML_OP_PAD:
+            ggml_cann_pad(ctx, dst);
+            break;
+        case GGML_OP_ARANGE:
+            ggml_cann_arange(ctx, dst);
+            break;
+        case GGML_OP_TIMESTEP_EMBEDDING:
+            ggml_cann_timestep_embedding(ctx, dst);
+            break;
+        case GGML_OP_LEAKY_RELU:
+            ggml_cann_leaky_relu(ctx, dst);
+            break;
+        case GGML_OP_RMS_NORM:
+            ggml_cann_rms_norm(ctx, dst);
+            break;
+        case GGML_OP_MUL_MAT:
+            ggml_cann_mul_mat(ctx, dst);
+            break;
+        case GGML_OP_MUL_MAT_ID:
+            return false;
+        case GGML_OP_SCALE:
+            ggml_cann_scale(ctx, dst);
+            break;
+        case GGML_OP_SQR:
+            ggml_cann_sqr(ctx, dst);
+            break;
+        case GGML_OP_CLAMP:
+            ggml_cann_clamp(ctx, dst);
+            break;
+        case GGML_OP_CPY:
+            ggml_cann_cpy(ctx, dst);
+            break;
+        case GGML_OP_CONT:
+            ggml_cann_dup(ctx, dst);
+            break;
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+            break;
+        case GGML_OP_DIAG_MASK_INF:
+            ggml_cann_diag_mask(ctx, dst, -INFINITY);
+            break;
+        case GGML_OP_SOFT_MAX:
+            ggml_cann_softmax(ctx, dst);
+            break;
+        case GGML_OP_ROPE:
+            ggml_cann_rope(ctx, dst);
+            break;
+        case GGML_OP_IM2COL:
+            ggml_cann_im2col(ctx, dst);
+            break;
+        case GGML_OP_POOL_2D:
+            ggml_cann_pool2d(ctx, dst);
+            break;
+        case GGML_OP_SUM_ROWS:
+            ggml_cann_sum_rows(ctx, dst);
+            break;
+        case GGML_OP_ARGSORT:
+            ggml_cann_argsort(ctx, dst);
+            break;
+        default:
+            return false;
+    }
+
+    return true;
+}
+
+// backend
+/**
+ * @brief Retrieves the name associated with the CANN backend.
+ *
+ * This function returns the name assigned to the CANN backend, which is stored
+ * in the context of the provided backend structure.
+ *
+ * @param backend Pointer to the CANN backend structure.
+ * @return A pointer to a constant string representing the backend name.
+ */
+GGML_CALL static const char* ggml_backend_cann_name(ggml_backend_t backend) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    return cann_ctx->name.c_str();
+}
+
+/**
+ * @brief Frees resources associated with the CANN backend.
+ *
+ * This function releases resources associated with the CANN backend context
+ * and resets the device associated with the backend to its initial state.
+ *
+ * @param backend Pointer to the CANN backend structure to be freed.
+ */
+GGML_CALL static void ggml_backend_cann_free(ggml_backend_t backend) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+    ACL_CHECK(aclrtSynchronizeDevice());
+    ACL_CHECK(aclrtResetDevice(cann_ctx->device));
+
+    // finalize when last backend freed.
+    if (cann_ctx->device == ggml_backend_cann_get_device_count() - 1) {
+        ACL_CHECK(aclFinalize());
+    }
+
+    delete cann_ctx;
+    delete backend;
+}
+
+/**
+ * @brief Retrieves the default buffer type associated with the CANN backend.
+ *
+ * This function returns the buffer type specific to the device associated
+ * with the CANN backend. It is used to allocate buffers for computations
+ * performed by the backend.
+ *
+ * @param backend Pointer to the CANN backend structure.
+ * @return Pointer to the buffer type structure for the CANN backend.
+ */
+GGML_CALL static ggml_backend_buffer_type_t
+ggml_backend_cann_get_default_buffer_type(ggml_backend_t backend) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    return ggml_backend_cann_buffer_type(cann_ctx->device);
+}
+
+/**
+ * @brief Sets tensor data asynchronously in the CANN backend.
+ *
+ * This function asynchronously sets tensor data in the CANN backend. Depending
+ * on the tensor type, it may perform data transformations before copying data
+ * to the device.
+ *
+ * @param backend Pointer to the CANN backend structure.
+ * @param tensor Pointer to the tensor structure to set data for.
+ * @param data Pointer to the host data to copy to the tensor.
+ * @param offset Offset in bytes within the host data.
+ * @param size Size of the data to copy in bytes.
+ */
+GGML_CALL static void ggml_backend_cann_set_tensor_async(ggml_backend_t backend,
+                                                         ggml_tensor* tensor,
+                                                         const void* data,
+                                                         size_t offset,
+                                                         size_t size) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    if (!need_transform(tensor->type)) {
+        ACL_CHECK(aclrtMemcpyAsync(
+            tensor->data, size, (const char*)data + offset, size,
+            ACL_MEMCPY_HOST_TO_DEVICE, cann_ctx->stream()));
+    } else {
+        void* transform_buffer = malloc(size);
+        ggml_backend_cann_transform(tensor, (const char*)data + offset,
+                                    transform_buffer);
+
+#ifndef NDEBUG
+        void* check_buffer = malloc(size);
+        ggml_backend_cann_transform_back(tensor, transform_buffer,
+                                         check_buffer);
+        GGML_ASSERT(memcmp((const char*)data + offset, check_buffer, size));
+        free(check_buffer);
+#endif
+        ACL_CHECK(aclrtMemcpyAsync(tensor->data, size, transform_buffer, size,
+                                   ACL_MEMCPY_HOST_TO_DEVICE,
+                                   cann_ctx->stream()));
+        ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
+        free(transform_buffer);
+    }
+}
+
+GGML_CALL static void ggml_backend_cann_get_tensor_async(
+    ggml_backend_t backend, const ggml_tensor* tensor, void* data,
+    size_t offset, size_t size) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+    ggml_backend_buffer_t buf =
+        tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+
+    GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) &&
+                "unsupported buffer type");
+
+    if (!need_transform(tensor->type)) {
+        ACL_CHECK(aclrtMemcpyAsync((char*)data + offset, size, tensor->data,
+                                   size, ACL_MEMCPY_DEVICE_TO_HOST,
+                                   cann_ctx->stream()));
+    } else {
+        void* transform_buffer = malloc(size);
+        ACL_CHECK(aclrtMemcpyAsync(transform_buffer, size, tensor->data, size,
+                                   ACL_MEMCPY_DEVICE_TO_HOST,
+                                   cann_ctx->stream()));
+        ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
+        ggml_backend_cann_transform_back(tensor, transform_buffer,
+                                         (char*)data + offset);
+        free(transform_buffer);
+    }
+}
+
+/**
+ * @brief Asynchronously copies tensor data between CANN backends.
+ *
+ * This function copies tensor data asynchronously between two CANN backends. It
+ * checks if both tensors reside in CANN buffers and whether the devices support
+ * peer-to-peer access for direct copying. If not, it returns false.
+ *
+ * @param backend_src Pointer to the source CANN backend structure.
+ * @param backend_dst Pointer to the destination CANN backend structure.
+ * @param src Pointer to the source tensor to copy data from.
+ * @param dst Pointer to the destination tensor to copy data to.
+ * @return true if the copy operation succeeds, false otherwise.
+ */
+GGML_CALL static bool ggml_backend_cann_cpy_tensor_async(
+    ggml_backend_t backend_src, ggml_backend_t backend_dst,
+    const ggml_tensor* src, ggml_tensor* dst) {
+    GGML_ASSERT(ggml_backend_is_cann(backend_src) ||
+                ggml_backend_is_cann(backend_dst));
+
+    if (!ggml_backend_buffer_is_cann(src->buffer) ||
+        !ggml_backend_buffer_is_cann(dst->buffer)) {
+        return false;
+    }
+
+    ggml_backend_buffer_t buf_src =
+        src->view_src ? src->view_src->buffer : src->buffer;
+    ggml_backend_buffer_t buf_dst =
+        dst->view_src ? dst->view_src->buffer : dst->buffer;
+
+    ggml_backend_cann_context* cann_ctx_src =
+        (ggml_backend_cann_context*)backend_src->context;
+    ggml_backend_cann_context* cann_ctx_dst =
+        (ggml_backend_cann_context*)backend_dst->context;
+
+    size_t copy_size = ggml_nbytes(dst);
+    if (backend_src != backend_dst) {
+        ggml_backend_cann_buffer_context* buf_ctx_src =
+            (ggml_backend_cann_buffer_context*)buf_src->context;
+        ggml_backend_cann_buffer_context* buf_ctx_dst =
+            (ggml_backend_cann_buffer_context*)buf_dst->context;
+
+        GGML_ASSERT(cann_ctx_src->device == buf_ctx_src->device);
+        GGML_ASSERT(cann_ctx_dst->device == buf_ctx_dst->device);
+
+        int32_t canAccessPeer = 0;
+        ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, cann_ctx_src->device,
+                                           cann_ctx_dst->device));
+        if (!canAccessPeer) {
+            return false;
+        }
+
+        ggml_cann_set_device(cann_ctx_src->device);
+        ACL_CHECK(aclrtDeviceEnablePeerAccess(cann_ctx_dst->device, 0));
+        ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size,
+                                   ACL_MEMCPY_DEVICE_TO_DEVICE,
+                                   cann_ctx_dst->stream()));
+
+        // record event on src stream
+        if (!cann_ctx_src->copy_event) {
+            ACL_CHECK(aclrtCreateEvent(&cann_ctx_src->copy_event));
+        }
+
+        ACL_CHECK(
+            aclrtRecordEvent(cann_ctx_src->copy_event, cann_ctx_src->stream()));
+
+        // wait on dst stream for the copy to complete
+        ACL_CHECK(aclrtStreamWaitEvent(cann_ctx_dst->stream(),
+                                       cann_ctx_src->copy_event));
+    } else {
+        // src and dst are on the same backend
+        ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size,
+                                   ACL_MEMCPY_DEVICE_TO_DEVICE,
+                                   cann_ctx_dst->stream()));
+    }
+
+    return true;
+}
+
+/**
+ * @brief Synchronizes a CANN backend.
+ *
+ * This function synchronizes the specified CANN backend by waiting for all
+ * operations in its associated stream to complete.
+ *
+ * @param backend Pointer to the CANN backend structure to synchronize.
+ */
+GGML_CALL static void ggml_backend_cann_synchronize(ggml_backend_t backend) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    ggml_cann_set_device(cann_ctx->device);
+
+    ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
+}
+
+/**
+ * @brief Computes a computational graph using a CANN backend.
+ *
+ * This function computes the operations defined in the computational graph
+ * using the specified CANN backend.
+ *
+ * @param backend Pointer to the CANN backend structure to use for computation.
+ * @param cgraph Pointer to the computational graph structure containing nodes
+ *               representing operations to be computed.
+ * @return enum ggml_status Returns GGML_STATUS_SUCCESS if computation
+ *         completes successfully, otherwise an appropriate error status.
+ */
+GGML_CALL static enum ggml_status ggml_backend_cann_graph_compute(
+    ggml_backend_t backend, ggml_cgraph* cgraph) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    ggml_cann_set_device(cann_ctx->device);
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor* node = cgraph->nodes[i];
+
+        if (ggml_is_empty(node) || node->op == GGML_OP_NONE) {
+            continue;
+        }
+
+        bool ok = ggml_cann_compute_forward(*cann_ctx, node);
+
+        if (!ok) {
+            GGML_CANN_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__,
+                    node->name, ggml_op_name(node->op));
+        }
+        GGML_ASSERT(ok);
+    }
+
+    return GGML_STATUS_SUCCESS;
+}
+
+/**
+ * @brief Checks if the CANN backend supports a specific operation.
+ *
+ * This function checks whether the specified operation is supported by the
+ * CANN backend.
+ *
+ * @param backend Pointer to the CANN backend structure to check support for
+ *                the operation.
+ * @param op Pointer to the tensor representing the operation to check.
+ * @return bool Returns true if the operation is supported by the backend,
+ *              otherwise false.
+ */
+GGML_CALL static bool ggml_backend_cann_supports_op(ggml_backend_t backend,
+                                                    const ggml_tensor* op) {
+    switch (op->op) {
+        case GGML_OP_UNARY:
+            switch (ggml_get_unary_op(op)) {
+                case GGML_UNARY_OP_GELU:
+                case GGML_UNARY_OP_SILU:
+                case GGML_UNARY_OP_RELU:
+                case GGML_UNARY_OP_HARDSIGMOID:
+                case GGML_UNARY_OP_HARDSWISH:
+                case GGML_UNARY_OP_GELU_QUICK:
+                case GGML_UNARY_OP_TANH:
+                    return true;
+                default:
+                    return false;
+            }
+        case GGML_OP_MUL_MAT: {
+            switch (op->src[0]->type) {
+                // case GGML_TYPE_Q4_0:
+                case GGML_TYPE_F16:
+                case GGML_TYPE_F32:
+                case GGML_TYPE_Q8_0:
+                    return true;
+                default:
+                    return false;
+            }
+        }
+        case GGML_OP_MUL_MAT_ID:
+            return false;
+        // embedding
+        case GGML_OP_GET_ROWS: {
+            switch (op->src[0]->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q4_0:
+                case GGML_TYPE_Q8_0:
+                    return true;
+                default:
+                    return false;
+            }
+        } break;
+        case GGML_OP_CPY: {
+            switch (op->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q8_0:
+                    return true;
+                default:
+                    return false;
+            }
+        }
+        case GGML_OP_DUP:
+        case GGML_OP_REPEAT:
+        case GGML_OP_CONCAT:
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_NORM:
+        case GGML_OP_ADD:
+        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_SCALE:
+        case GGML_OP_SQR:
+        case GGML_OP_CLAMP:
+        case GGML_OP_CONT:
+        case GGML_OP_DIAG_MASK_INF:
+        case GGML_OP_SOFT_MAX:
+        case GGML_OP_ROPE:
+        case GGML_OP_IM2COL:
+        case GGML_OP_POOL_2D:
+        case GGML_OP_SUM_ROWS:
+        case GGML_OP_ARGSORT:
+        case GGML_OP_ACC:
+        case GGML_OP_GROUP_NORM:
+        case GGML_OP_UPSCALE:
+        case GGML_OP_PAD:
+        case GGML_OP_ARANGE:
+        case GGML_OP_TIMESTEP_EMBEDDING:
+        case GGML_OP_LEAKY_RELU:
+            return true;
+        default:
+            return false;
+    }
+
+    GGML_UNUSED(backend);
+}
+
+/**
+ * @brief Checks if the backend buffer type is associated with the CANN backend.
+ *
+ * This function checks whether the provided backend buffer type is associated
+ * with the CANN backend based on the comparison of its name retrieval function
+ * pointer.
+ *
+ * @param buft Pointer to the backend buffer type to check.
+ * @return bool Returns true if the buffer type is associated with the CANN
+ * backend, otherwise false.
+ */
+static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
+}
+
+/**
+ * @brief Checks if the CANN backend supports a specific backend buffer type.
+ *
+ * This function determines whether the CANN backend supports the given backend
+ * buffer type by comparing the device context of the backend and buffer type.
+ * It returns true if the device associated with the buffer type matches the
+ * device associated with the backend.
+ *
+ * @param backend Pointer to the CANN backend.
+ * @param buft Pointer to the backend buffer type to check.
+ * @return bool Returns true if the CANN backend supports the buffer type,
+ *              otherwise false.
+ */
+GGML_CALL static bool ggml_backend_cann_supports_buft(
+    ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
+
+    GGML_UNUSED(backend);
+}
+
+/**
+ * @brief Determines if a tensor operation should be offloaded to the CANN
+ * backend.
+ *
+ * This function checks if a given tensor operation should be offloaded to the
+ * CANN backend based on the operation type and the size of the tensor. It
+ * returns true if the second dimension (ne[1]) of the tensor is greater than or
+ * equal to the minimum batch size and the operation is not GGML_OP_GET_ROWS.
+ *
+ * @param backend Pointer to the CANN backend.
+ * @param op Pointer to the tensor operation to check.
+ * @return bool Returns true if the operation should be offloaded, otherwise
+ * false.
+ */
+GGML_CALL static bool ggml_backend_cann_offload_op(ggml_backend_t backend,
+                                                   const ggml_tensor* op) {
+    const int min_batch_size = 32;
+    GGML_UNUSED(backend);
+
+    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS;
+}
+
+/**
+ * @brief Creates a new event for the CANN backend.
+ *
+ * This function initializes a new event for the CANN backend by setting the
+ * device and creating an ACL runtime event. The created event is then wrapped
+ * in a ggml_backend_event structure and returned.
+ *
+ * @param backend Pointer to the CANN backend.
+ * @return ggml_backend_event_t Returns a pointer to the new event structure.
+ */
+static ggml_backend_event_t ggml_backend_cann_event_new(
+    ggml_backend_t backend) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    ggml_cann_set_device(cann_ctx->device);
+
+    aclrtEvent event;
+    ACL_CHECK(aclrtCreateEvent(&event));
+
+    return new ggml_backend_event{
+        /* .backend = */ backend,
+        /* .context = */ event,
+    };
+}
+
+/**
+ * @brief Frees a CANN backend event.
+ *
+ * This function destroys the ACL runtime event associated with the given CANN
+ * backend event and then deletes the event structure itself.
+ *
+ * @param event Pointer to the event structure to be freed.
+ */
+static void ggml_backend_cann_event_free(ggml_backend_event_t event) {
+    ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context));
+
+    delete event;
+}
+
+/**
+ * @brief Records an event on the CANN backend stream.
+ *
+ * This function records the given event on the ACL runtime stream associated
+ * with the backend context.
+ *
+ * @param event Pointer to the event structure to be recorded.
+ */
+static void ggml_backend_cann_event_record(ggml_backend_event_t event) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)event->backend->context;
+
+    ACL_CHECK(aclrtRecordEvent((aclrtEvent)event->context, cann_ctx->stream()));
+}
+
+/**
+ * @brief Waits for a recorded event to complete on the CANN backend stream.
+ *
+ * This function makes the given backend wait for the event to complete on its
+ * ACL runtime stream.
+ *
+ * @param backend Pointer to the backend structure.
+ * @param event Pointer to the event structure that the backend needs to wait
+ * for.
+ */
+static void ggml_backend_cann_event_wait(ggml_backend_t backend,
+                                         ggml_backend_event_t event) {
+    ggml_backend_cann_context* cann_ctx =
+        (ggml_backend_cann_context*)backend->context;
+
+    if (ggml_backend_is_cann(event->backend)) {
+        ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(),
+                                       (aclrtEvent)event->context));
+    } else {
+        GGML_ASSERT(false);
+    }
+}
+
+/**
+ * @brief Synchronizes the given event on the CANN backend.
+ *
+ * This function waits for the specified event to complete on the ACL runtime.
+ *
+ * @param event Pointer to the event structure to be synchronized.
+ */
+static void ggml_backend_cann_event_synchronize(ggml_backend_event_t event) {
+    ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context));
+}
+
+/**
+ * @brief Structure defining the interface for the CANN backend.
+ *
+ * This structure contains function pointers for various operations
+ * supported by the CANN backend, including name retrieval, memory
+ * management, tensor operations, synchronization, and event handling.
+ */
+static ggml_backend_i ggml_backend_cann_interface = {
+    /* .get_name                = */ ggml_backend_cann_name,
+    /* .free                    = */ ggml_backend_cann_free,
+    /* .get_default_buffer_type = */ ggml_backend_cann_get_default_buffer_type,
+    /* .set_tensor_async        = */ ggml_backend_cann_set_tensor_async,
+    /* .get_tensor_async        = */ ggml_backend_cann_get_tensor_async,
+    /* .cpy_tensor_async        = */ ggml_backend_cann_cpy_tensor_async,
+    /* .synchronize             = */ ggml_backend_cann_synchronize,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_cann_graph_compute,
+    /* .supports_op             = */ ggml_backend_cann_supports_op,
+    /* .supports_buft           = */ ggml_backend_cann_supports_buft,
+    /* .offload_op              = */ ggml_backend_cann_offload_op,
+    /* .event_new               = */ ggml_backend_cann_event_new,
+    /* .event_free              = */ ggml_backend_cann_event_free,
+    /* .event_record            = */ ggml_backend_cann_event_record,
+    /* .event_wait              = */ ggml_backend_cann_event_wait,
+    /* .event_synchronize       = */ ggml_backend_cann_event_synchronize,
+};
+
+/**
+ * @brief Return the hardcoded GUID for the CANN backend.
+ *
+ * This function returns a static GUID which uniquely identifies the CANN
+ * backend.
+ *
+ * @return A pointer to the static GUID.
+ */
+static ggml_guid_t ggml_backend_cann_guid() {
+    static ggml_guid guid = {0xa1, 0x94, 0xaf, 0xac, 0xbd, 0x4f, 0x47, 0x34,
+                             0xbe, 0x1a, 0x9e, 0x71, 0x1f, 0x9e, 0xed, 0x64};
+    return &guid;
+}
+
+GGML_CALL ggml_backend_t ggml_backend_cann_init(int32_t device) {
+    aclInit(nullptr);
+    if (device < 0 || device >= ggml_backend_cann_get_device_count()) {
+        GGML_CANN_LOG_ERROR("%s: error: invalid device %d\n", __func__, device);
+        return nullptr;
+    }
+
+    ggml_backend_cann_context* ctx = new ggml_backend_cann_context(device);
+    if (ctx == nullptr) {
+        GGML_CANN_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
+        return nullptr;
+    }
+
+    ggml_backend_t cann_backend =
+        new ggml_backend{/* .guid      = */ ggml_backend_cann_guid(),
+                         /* .interface = */ ggml_backend_cann_interface,
+                         /* .context   = */ ctx};
+
+    return cann_backend;
+}
+
+GGML_CALL bool ggml_backend_is_cann(ggml_backend_t backend) {
+    return backend != NULL &&
+           ggml_guid_matches(backend->guid, ggml_backend_cann_guid());
+}
+
+GGML_CALL int32_t ggml_backend_cann_get_device_count() {
+    return ggml_cann_info().device_count;
+}
+
+GGML_CALL void ggml_backend_cann_get_device_description(
+    int32_t device, char* description, size_t description_size) {
+    ggml_cann_set_device(device);
+    const char* soc_name = aclrtGetSocName();
+    snprintf(description, description_size, "%s", soc_name);
+}
+
+GGML_CALL void ggml_backend_cann_get_device_memory(int32_t device, size_t* free,
+                                                   size_t* total) {
+    ggml_cann_set_device(device);
+    ACL_CHECK(aclrtGetMemInfo(ACL_HBM_MEM, free, total));
+}
+
+// backend registry
+/**
+ * @brief Initializes a CANN backend based on the provided parameters.
+ *
+ * This function initializes a CANN backend using the device index and then
+ * initializes the backend using `ggml_backend_cann_init`.
+ *
+ * @param params Parameters for initialization (unused in this implementation).
+ * @param user_data User data containing the device index to initialize the
+ * backend.
+ * @return ggml_backend_t The initialized CANN backend.
+ */
+GGML_CALL static ggml_backend_t ggml_backend_reg_cann_init(const char* params,
+                                                           void* user_data) {
+    ggml_backend_t cann_backend =
+        ggml_backend_cann_init((int)(intptr_t)user_data);
+    return cann_backend;
+
+    GGML_UNUSED(params);
+}
+
+extern "C" GGML_CALL int ggml_backend_cann_reg_devices();
+
+/**
+ * @brief Registers CANN (Ascend) devices as backend options.
+ *
+ * This function initializes ACL, retrieves the number of available CANN
+ * devices, and registers each device as a backend option using
+ * `ggml_backend_register`. Each device is given a unique name based on
+ * `GGML_CANN_NAME` followed by its index.
+ *
+ * @return int The number of CANN devices registered.
+ */
+GGML_CALL int ggml_backend_cann_reg_devices() {
+    uint32_t device_count = ggml_backend_cann_get_device_count();
+    // initialization
+    for (uint32_t i = 0; i < device_count; i++) {
+        char name[128];
+        snprintf(name, sizeof(name), "CANN%d", i);
+        ggml_backend_register(name, ggml_backend_reg_cann_init,
+                              ggml_backend_cann_buffer_type(i),
+                              (void*)(intptr_t)i);
+    }
+    return device_count;
+}
diff --git a/ggml/src/ggml-cann/.clang-format b/ggml/src/ggml-cann/.clang-format
new file mode 100644
index 00000000000000..2ad03d73919364
--- /dev/null
+++ b/ggml/src/ggml-cann/.clang-format
@@ -0,0 +1,168 @@
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+      - PARSE_TEST_PROTO
+      - PARSE_TEXT_PROTO
+      - ParseTextOrDie
+      - ParseTextProtoOrDie
+    CanonicalDelimiter: ''
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diff --git a/ggml/src/ggml-cann/Doxyfile b/ggml/src/ggml-cann/Doxyfile
new file mode 100644
index 00000000000000..2b009e8f9bd26a
--- /dev/null
+++ b/ggml/src/ggml-cann/Doxyfile
@@ -0,0 +1,2579 @@
+# Doxyfile 1.8.17
+
+# This file describes the settings to be used by the documentation system
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+#
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+#
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+PROJECT_NAME           = "llama.cpp"
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+
+PROJECT_BRIEF          = "llama inference engine"
+
+# With the PROJECT_LOGO tag one can specify a logo or an icon that is included
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+
+PROJECT_LOGO           =
+
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+
+OUTPUT_DIRECTORY       = docs
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+# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub-
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+# The default value is: NO.
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+CREATE_SUBDIRS         = NO
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+# The default value is: NO.
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+ALLOW_UNICODE_NAMES    = NO
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+OUTPUT_LANGUAGE        = English
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+OUTPUT_TEXT_DIRECTION  = None
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+# be prevented in individual cases by putting a % sign in front of the word or
+# globally by setting AUTOLINK_SUPPORT to NO.
+# The default value is: YES.
+
+AUTOLINK_SUPPORT       = YES
+
+# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want
+# to include (a tag file for) the STL sources as input, then you should set this
+# tag to YES in order to let doxygen match functions declarations and
+# definitions whose arguments contain STL classes (e.g. func(std::string);
+# versus func(std::string) {}). This also make the inheritance and collaboration
+# diagrams that involve STL classes more complete and accurate.
+# The default value is: NO.
+
+BUILTIN_STL_SUPPORT    = NO
+
+# If you use Microsoft's C++/CLI language, you should set this option to YES to
+# enable parsing support.
+# The default value is: NO.
+
+CPP_CLI_SUPPORT        = NO
+
+# Set the SIP_SUPPORT tag to YES if your project consists of sip (see:
+# https://www.riverbankcomputing.com/software/sip/intro) sources only. Doxygen
+# will parse them like normal C++ but will assume all classes use public instead
+# of private inheritance when no explicit protection keyword is present.
+# The default value is: NO.
+
+SIP_SUPPORT            = NO
+
+# For Microsoft's IDL there are propget and propput attributes to indicate
+# getter and setter methods for a property. Setting this option to YES will make
+# doxygen to replace the get and set methods by a property in the documentation.
+# This will only work if the methods are indeed getting or setting a simple
+# type. If this is not the case, or you want to show the methods anyway, you
+# should set this option to NO.
+# The default value is: YES.
+
+IDL_PROPERTY_SUPPORT   = YES
+
+# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC
+# tag is set to YES then doxygen will reuse the documentation of the first
+# member in the group (if any) for the other members of the group. By default
+# all members of a group must be documented explicitly.
+# The default value is: NO.
+
+DISTRIBUTE_GROUP_DOC   = NO
+
+# If one adds a struct or class to a group and this option is enabled, then also
+# any nested class or struct is added to the same group. By default this option
+# is disabled and one has to add nested compounds explicitly via \ingroup.
+# The default value is: NO.
+
+GROUP_NESTED_COMPOUNDS = NO
+
+# Set the SUBGROUPING tag to YES to allow class member groups of the same type
+# (for instance a group of public functions) to be put as a subgroup of that
+# type (e.g. under the Public Functions section). Set it to NO to prevent
+# subgrouping. Alternatively, this can be done per class using the
+# \nosubgrouping command.
+# The default value is: YES.
+
+SUBGROUPING            = YES
+
+# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions
+# are shown inside the group in which they are included (e.g. using \ingroup)
+# instead of on a separate page (for HTML and Man pages) or section (for LaTeX
+# and RTF).
+#
+# Note that this feature does not work in combination with
+# SEPARATE_MEMBER_PAGES.
+# The default value is: NO.
+
+INLINE_GROUPED_CLASSES = NO
+
+# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions
+# with only public data fields or simple typedef fields will be shown inline in
+# the documentation of the scope in which they are defined (i.e. file,
+# namespace, or group documentation), provided this scope is documented. If set
+# to NO, structs, classes, and unions are shown on a separate page (for HTML and
+# Man pages) or section (for LaTeX and RTF).
+# The default value is: NO.
+
+INLINE_SIMPLE_STRUCTS  = NO
+
+# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or
+# enum is documented as struct, union, or enum with the name of the typedef. So
+# typedef struct TypeS {} TypeT, will appear in the documentation as a struct
+# with name TypeT. When disabled the typedef will appear as a member of a file,
+# namespace, or class. And the struct will be named TypeS. This can typically be
+# useful for C code in case the coding convention dictates that all compound
+# types are typedef'ed and only the typedef is referenced, never the tag name.
+# The default value is: NO.
+
+TYPEDEF_HIDES_STRUCT   = NO
+
+# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This
+# cache is used to resolve symbols given their name and scope. Since this can be
+# an expensive process and often the same symbol appears multiple times in the
+# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small
+# doxygen will become slower. If the cache is too large, memory is wasted. The
+# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range
+# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536
+# symbols. At the end of a run doxygen will report the cache usage and suggest
+# the optimal cache size from a speed point of view.
+# Minimum value: 0, maximum value: 9, default value: 0.
+
+LOOKUP_CACHE_SIZE      = 0
+
+#---------------------------------------------------------------------------
+# Build related configuration options
+#---------------------------------------------------------------------------
+
+# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in
+# documentation are documented, even if no documentation was available. Private
+# class members and static file members will be hidden unless the
+# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.
+# Note: This will also disable the warnings about undocumented members that are
+# normally produced when WARNINGS is set to YES.
+# The default value is: NO.
+
+EXTRACT_ALL            = YES
+
+# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will
+# be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PRIVATE        = YES
+
+# If the EXTRACT_PRIV_VIRTUAL tag is set to YES, documented private virtual
+# methods of a class will be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PRIV_VIRTUAL   = YES
+
+# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal
+# scope will be included in the documentation.
+# The default value is: NO.
+
+EXTRACT_PACKAGE        = YES
+
+# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be
+# included in the documentation.
+# The default value is: NO.
+
+EXTRACT_STATIC         = YES
+
+# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined
+# locally in source files will be included in the documentation. If set to NO,
+# only classes defined in header files are included. Does not have any effect
+# for Java sources.
+# The default value is: YES.
+
+EXTRACT_LOCAL_CLASSES  = YES
+
+# This flag is only useful for Objective-C code. If set to YES, local methods,
+# which are defined in the implementation section but not in the interface are
+# included in the documentation. If set to NO, only methods in the interface are
+# included.
+# The default value is: NO.
+
+EXTRACT_LOCAL_METHODS  = YES
+
+# If this flag is set to YES, the members of anonymous namespaces will be
+# extracted and appear in the documentation as a namespace called
+# 'anonymous_namespace{file}', where file will be replaced with the base name of
+# the file that contains the anonymous namespace. By default anonymous namespace
+# are hidden.
+# The default value is: NO.
+
+EXTRACT_ANON_NSPACES   = NO
+
+# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all
+# undocumented members inside documented classes or files. If set to NO these
+# members will be included in the various overviews, but no documentation
+# section is generated. This option has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_MEMBERS     = NO
+
+# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all
+# undocumented classes that are normally visible in the class hierarchy. If set
+# to NO, these classes will be included in the various overviews. This option
+# has no effect if EXTRACT_ALL is enabled.
+# The default value is: NO.
+
+HIDE_UNDOC_CLASSES     = NO
+
+# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend
+# declarations. If set to NO, these declarations will be included in the
+# documentation.
+# The default value is: NO.
+
+HIDE_FRIEND_COMPOUNDS  = NO
+
+# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any
+# documentation blocks found inside the body of a function. If set to NO, these
+# blocks will be appended to the function's detailed documentation block.
+# The default value is: NO.
+
+HIDE_IN_BODY_DOCS      = NO
+
+# The INTERNAL_DOCS tag determines if documentation that is typed after a
+# \internal command is included. If the tag is set to NO then the documentation
+# will be excluded. Set it to YES to include the internal documentation.
+# The default value is: NO.
+
+INTERNAL_DOCS          = NO
+
+# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file
+# names in lower-case letters. If set to YES, upper-case letters are also
+# allowed. This is useful if you have classes or files whose names only differ
+# in case and if your file system supports case sensitive file names. Windows
+# (including Cygwin) ands Mac users are advised to set this option to NO.
+# The default value is: system dependent.
+
+CASE_SENSE_NAMES       = YES
+
+# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with
+# their full class and namespace scopes in the documentation. If set to YES, the
+# scope will be hidden.
+# The default value is: NO.
+
+HIDE_SCOPE_NAMES       = NO
+
+# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will
+# append additional text to a page's title, such as Class Reference. If set to
+# YES the compound reference will be hidden.
+# The default value is: NO.
+
+HIDE_COMPOUND_REFERENCE= NO
+
+# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of
+# the files that are included by a file in the documentation of that file.
+# The default value is: YES.
+
+SHOW_INCLUDE_FILES     = YES
+
+# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each
+# grouped member an include statement to the documentation, telling the reader
+# which file to include in order to use the member.
+# The default value is: NO.
+
+SHOW_GROUPED_MEMB_INC  = NO
+
+# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include
+# files with double quotes in the documentation rather than with sharp brackets.
+# The default value is: NO.
+
+FORCE_LOCAL_INCLUDES   = NO
+
+# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the
+# documentation for inline members.
+# The default value is: YES.
+
+INLINE_INFO            = YES
+
+# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the
+# (detailed) documentation of file and class members alphabetically by member
+# name. If set to NO, the members will appear in declaration order.
+# The default value is: YES.
+
+SORT_MEMBER_DOCS       = YES
+
+# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief
+# descriptions of file, namespace and class members alphabetically by member
+# name. If set to NO, the members will appear in declaration order. Note that
+# this will also influence the order of the classes in the class list.
+# The default value is: NO.
+
+SORT_BRIEF_DOCS        = NO
+
+# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the
+# (brief and detailed) documentation of class members so that constructors and
+# destructors are listed first. If set to NO the constructors will appear in the
+# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.
+# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief
+# member documentation.
+# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting
+# detailed member documentation.
+# The default value is: NO.
+
+SORT_MEMBERS_CTORS_1ST = NO
+
+# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy
+# of group names into alphabetical order. If set to NO the group names will
+# appear in their defined order.
+# The default value is: NO.
+
+SORT_GROUP_NAMES       = NO
+
+# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by
+# fully-qualified names, including namespaces. If set to NO, the class list will
+# be sorted only by class name, not including the namespace part.
+# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.
+# Note: This option applies only to the class list, not to the alphabetical
+# list.
+# The default value is: NO.
+
+SORT_BY_SCOPE_NAME     = NO
+
+# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper
+# type resolution of all parameters of a function it will reject a match between
+# the prototype and the implementation of a member function even if there is
+# only one candidate or it is obvious which candidate to choose by doing a
+# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still
+# accept a match between prototype and implementation in such cases.
+# The default value is: NO.
+
+STRICT_PROTO_MATCHING  = NO
+
+# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo
+# list. This list is created by putting \todo commands in the documentation.
+# The default value is: YES.
+
+GENERATE_TODOLIST      = YES
+
+# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test
+# list. This list is created by putting \test commands in the documentation.
+# The default value is: YES.
+
+GENERATE_TESTLIST      = YES
+
+# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug
+# list. This list is created by putting \bug commands in the documentation.
+# The default value is: YES.
+
+GENERATE_BUGLIST       = YES
+
+# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)
+# the deprecated list. This list is created by putting \deprecated commands in
+# the documentation.
+# The default value is: YES.
+
+GENERATE_DEPRECATEDLIST= YES
+
+# The ENABLED_SECTIONS tag can be used to enable conditional documentation
+# sections, marked by \if <section_label> ... \endif and \cond <section_label>
+# ... \endcond blocks.
+
+ENABLED_SECTIONS       =
+
+# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the
+# initial value of a variable or macro / define can have for it to appear in the
+# documentation. If the initializer consists of more lines than specified here
+# it will be hidden. Use a value of 0 to hide initializers completely. The
+# appearance of the value of individual variables and macros / defines can be
+# controlled using \showinitializer or \hideinitializer command in the
+# documentation regardless of this setting.
+# Minimum value: 0, maximum value: 10000, default value: 30.
+
+MAX_INITIALIZER_LINES  = 30
+
+# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at
+# the bottom of the documentation of classes and structs. If set to YES, the
+# list will mention the files that were used to generate the documentation.
+# The default value is: YES.
+
+SHOW_USED_FILES        = YES
+
+# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This
+# will remove the Files entry from the Quick Index and from the Folder Tree View
+# (if specified).
+# The default value is: YES.
+
+SHOW_FILES             = YES
+
+# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces
+# page. This will remove the Namespaces entry from the Quick Index and from the
+# Folder Tree View (if specified).
+# The default value is: YES.
+
+SHOW_NAMESPACES        = YES
+
+# The FILE_VERSION_FILTER tag can be used to specify a program or script that
+# doxygen should invoke to get the current version for each file (typically from
+# the version control system). Doxygen will invoke the program by executing (via
+# popen()) the command command input-file, where command is the value of the
+# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided
+# by doxygen. Whatever the program writes to standard output is used as the file
+# version. For an example see the documentation.
+
+FILE_VERSION_FILTER    =
+
+# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed
+# by doxygen. The layout file controls the global structure of the generated
+# output files in an output format independent way. To create the layout file
+# that represents doxygen's defaults, run doxygen with the -l option. You can
+# optionally specify a file name after the option, if omitted DoxygenLayout.xml
+# will be used as the name of the layout file.
+#
+# Note that if you run doxygen from a directory containing a file called
+# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE
+# tag is left empty.
+
+LAYOUT_FILE            =
+
+# The CITE_BIB_FILES tag can be used to specify one or more bib files containing
+# the reference definitions. This must be a list of .bib files. The .bib
+# extension is automatically appended if omitted. This requires the bibtex tool
+# to be installed. See also https://en.wikipedia.org/wiki/BibTeX for more info.
+# For LaTeX the style of the bibliography can be controlled using
+# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the
+# search path. See also \cite for info how to create references.
+
+CITE_BIB_FILES         =
+
+#---------------------------------------------------------------------------
+# Configuration options related to warning and progress messages
+#---------------------------------------------------------------------------
+
+# The QUIET tag can be used to turn on/off the messages that are generated to
+# standard output by doxygen. If QUIET is set to YES this implies that the
+# messages are off.
+# The default value is: NO.
+
+QUIET                  = NO
+
+# The WARNINGS tag can be used to turn on/off the warning messages that are
+# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES
+# this implies that the warnings are on.
+#
+# Tip: Turn warnings on while writing the documentation.
+# The default value is: YES.
+
+WARNINGS               = YES
+
+# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate
+# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag
+# will automatically be disabled.
+# The default value is: YES.
+
+WARN_IF_UNDOCUMENTED   = YES
+
+# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for
+# potential errors in the documentation, such as not documenting some parameters
+# in a documented function, or documenting parameters that don't exist or using
+# markup commands wrongly.
+# The default value is: YES.
+
+WARN_IF_DOC_ERROR      = YES
+
+# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that
+# are documented, but have no documentation for their parameters or return
+# value. If set to NO, doxygen will only warn about wrong or incomplete
+# parameter documentation, but not about the absence of documentation. If
+# EXTRACT_ALL is set to YES then this flag will automatically be disabled.
+# The default value is: NO.
+
+WARN_NO_PARAMDOC       = NO
+
+# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when
+# a warning is encountered.
+# The default value is: NO.
+
+WARN_AS_ERROR          = NO
+
+# The WARN_FORMAT tag determines the format of the warning messages that doxygen
+# can produce. The string should contain the $file, $line, and $text tags, which
+# will be replaced by the file and line number from which the warning originated
+# and the warning text. Optionally the format may contain $version, which will
+# be replaced by the version of the file (if it could be obtained via
+# FILE_VERSION_FILTER)
+# The default value is: $file:$line: $text.
+
+WARN_FORMAT            = "$file:$line: $text"
+
+# The WARN_LOGFILE tag can be used to specify a file to which warning and error
+# messages should be written. If left blank the output is written to standard
+# error (stderr).
+
+WARN_LOGFILE           =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the input files
+#---------------------------------------------------------------------------
+
+# The INPUT tag is used to specify the files and/or directories that contain
+# documented source files. You may enter file names like myfile.cpp or
+# directories like /usr/src/myproject. Separate the files or directories with
+# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING
+# Note: If this tag is empty the current directory is searched.
+
+INPUT                  =
+
+# This tag can be used to specify the character encoding of the source files
+# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
+# libiconv (or the iconv built into libc) for the transcoding. See the libiconv
+# documentation (see: https://www.gnu.org/software/libiconv/) for the list of
+# possible encodings.
+# The default value is: UTF-8.
+
+INPUT_ENCODING         = UTF-8
+
+# If the value of the INPUT tag contains directories, you can use the
+# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and
+# *.h) to filter out the source-files in the directories.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# read by doxygen.
+#
+# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,
+# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,
+# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,
+# *.m, *.markdown, *.md, *.mm, *.dox (to be provided as doxygen C comment),
+# *.doc (to be provided as doxygen C comment), *.txt (to be provided as doxygen
+# C comment), *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, *.f, *.for, *.tcl, *.vhd,
+# *.vhdl, *.ucf, *.qsf and *.ice.
+
+FILE_PATTERNS          = *.c \
+                         *.cc \
+                         *.cxx \
+                         *.cpp \
+                         *.c++ \
+                         *.java \
+                         *.ii \
+                         *.ixx \
+                         *.ipp \
+                         *.i++ \
+                         *.inl \
+                         *.idl \
+                         *.ddl \
+                         *.odl \
+                         *.h \
+                         *.hh \
+                         *.hxx \
+                         *.hpp \
+                         *.h++ \
+                         *.cs \
+                         *.d \
+                         *.php \
+                         *.php4 \
+                         *.php5 \
+                         *.phtml \
+                         *.inc \
+                         *.m \
+                         *.markdown \
+                         *.md \
+                         *.mm \
+                         *.dox \
+                         *.doc \
+                         *.txt \
+                         *.py \
+                         *.pyw \
+                         *.f90 \
+                         *.f95 \
+                         *.f03 \
+                         *.f08 \
+                         *.f \
+                         *.for \
+                         *.tcl \
+                         *.vhd \
+                         *.vhdl \
+                         *.ucf \
+                         *.qsf \
+                         *.ice
+
+# The RECURSIVE tag can be used to specify whether or not subdirectories should
+# be searched for input files as well.
+# The default value is: NO.
+
+RECURSIVE              = YES
+
+# The EXCLUDE tag can be used to specify files and/or directories that should be
+# excluded from the INPUT source files. This way you can easily exclude a
+# subdirectory from a directory tree whose root is specified with the INPUT tag.
+#
+# Note that relative paths are relative to the directory from which doxygen is
+# run.
+
+EXCLUDE                =
+
+# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or
+# directories that are symbolic links (a Unix file system feature) are excluded
+# from the input.
+# The default value is: NO.
+
+EXCLUDE_SYMLINKS       = NO
+
+# If the value of the INPUT tag contains directories, you can use the
+# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude
+# certain files from those directories.
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories for example use the pattern */test/*
+
+EXCLUDE_PATTERNS       =
+
+# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names
+# (namespaces, classes, functions, etc.) that should be excluded from the
+# output. The symbol name can be a fully qualified name, a word, or if the
+# wildcard * is used, a substring. Examples: ANamespace, AClass,
+# AClass::ANamespace, ANamespace::*Test
+#
+# Note that the wildcards are matched against the file with absolute path, so to
+# exclude all test directories use the pattern */test/*
+
+EXCLUDE_SYMBOLS        =
+
+# The EXAMPLE_PATH tag can be used to specify one or more files or directories
+# that contain example code fragments that are included (see the \include
+# command).
+
+EXAMPLE_PATH           =
+
+# If the value of the EXAMPLE_PATH tag contains directories, you can use the
+# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and
+# *.h) to filter out the source-files in the directories. If left blank all
+# files are included.
+
+EXAMPLE_PATTERNS       = *
+
+# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be
+# searched for input files to be used with the \include or \dontinclude commands
+# irrespective of the value of the RECURSIVE tag.
+# The default value is: NO.
+
+EXAMPLE_RECURSIVE      = NO
+
+# The IMAGE_PATH tag can be used to specify one or more files or directories
+# that contain images that are to be included in the documentation (see the
+# \image command).
+
+IMAGE_PATH             =
+
+# The INPUT_FILTER tag can be used to specify a program that doxygen should
+# invoke to filter for each input file. Doxygen will invoke the filter program
+# by executing (via popen()) the command:
+#
+# <filter> <input-file>
+#
+# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the
+# name of an input file. Doxygen will then use the output that the filter
+# program writes to standard output. If FILTER_PATTERNS is specified, this tag
+# will be ignored.
+#
+# Note that the filter must not add or remove lines; it is applied before the
+# code is scanned, but not when the output code is generated. If lines are added
+# or removed, the anchors will not be placed correctly.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
+
+INPUT_FILTER           =
+
+# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern
+# basis. Doxygen will compare the file name with each pattern and apply the
+# filter if there is a match. The filters are a list of the form: pattern=filter
+# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how
+# filters are used. If the FILTER_PATTERNS tag is empty or if none of the
+# patterns match the file name, INPUT_FILTER is applied.
+#
+# Note that for custom extensions or not directly supported extensions you also
+# need to set EXTENSION_MAPPING for the extension otherwise the files are not
+# properly processed by doxygen.
+
+FILTER_PATTERNS        =
+
+# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using
+# INPUT_FILTER) will also be used to filter the input files that are used for
+# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).
+# The default value is: NO.
+
+FILTER_SOURCE_FILES    = NO
+
+# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file
+# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and
+# it is also possible to disable source filtering for a specific pattern using
+# *.ext= (so without naming a filter).
+# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.
+
+FILTER_SOURCE_PATTERNS =
+
+# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that
+# is part of the input, its contents will be placed on the main page
+# (index.html). This can be useful if you have a project on for instance GitHub
+# and want to reuse the introduction page also for the doxygen output.
+
+USE_MDFILE_AS_MAINPAGE =
+
+#---------------------------------------------------------------------------
+# Configuration options related to source browsing
+#---------------------------------------------------------------------------
+
+# If the SOURCE_BROWSER tag is set to YES then a list of source files will be
+# generated. Documented entities will be cross-referenced with these sources.
+#
+# Note: To get rid of all source code in the generated output, make sure that
+# also VERBATIM_HEADERS is set to NO.
+# The default value is: NO.
+
+SOURCE_BROWSER         = NO
+
+# Setting the INLINE_SOURCES tag to YES will include the body of functions,
+# classes and enums directly into the documentation.
+# The default value is: NO.
+
+INLINE_SOURCES         = NO
+
+# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any
+# special comment blocks from generated source code fragments. Normal C, C++ and
+# Fortran comments will always remain visible.
+# The default value is: YES.
+
+STRIP_CODE_COMMENTS    = YES
+
+# If the REFERENCED_BY_RELATION tag is set to YES then for each documented
+# entity all documented functions referencing it will be listed.
+# The default value is: NO.
+
+REFERENCED_BY_RELATION = NO
+
+# If the REFERENCES_RELATION tag is set to YES then for each documented function
+# all documented entities called/used by that function will be listed.
+# The default value is: NO.
+
+REFERENCES_RELATION    = NO
+
+# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set
+# to YES then the hyperlinks from functions in REFERENCES_RELATION and
+# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will
+# link to the documentation.
+# The default value is: YES.
+
+REFERENCES_LINK_SOURCE = YES
+
+# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the
+# source code will show a tooltip with additional information such as prototype,
+# brief description and links to the definition and documentation. Since this
+# will make the HTML file larger and loading of large files a bit slower, you
+# can opt to disable this feature.
+# The default value is: YES.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+SOURCE_TOOLTIPS        = YES
+
+# If the USE_HTAGS tag is set to YES then the references to source code will
+# point to the HTML generated by the htags(1) tool instead of doxygen built-in
+# source browser. The htags tool is part of GNU's global source tagging system
+# (see https://www.gnu.org/software/global/global.html). You will need version
+# 4.8.6 or higher.
+#
+# To use it do the following:
+# - Install the latest version of global
+# - Enable SOURCE_BROWSER and USE_HTAGS in the configuration file
+# - Make sure the INPUT points to the root of the source tree
+# - Run doxygen as normal
+#
+# Doxygen will invoke htags (and that will in turn invoke gtags), so these
+# tools must be available from the command line (i.e. in the search path).
+#
+# The result: instead of the source browser generated by doxygen, the links to
+# source code will now point to the output of htags.
+# The default value is: NO.
+# This tag requires that the tag SOURCE_BROWSER is set to YES.
+
+USE_HTAGS              = NO
+
+# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a
+# verbatim copy of the header file for each class for which an include is
+# specified. Set to NO to disable this.
+# See also: Section \class.
+# The default value is: YES.
+
+VERBATIM_HEADERS       = YES
+
+# If the CLANG_ASSISTED_PARSING tag is set to YES then doxygen will use the
+# clang parser (see: http://clang.llvm.org/) for more accurate parsing at the
+# cost of reduced performance. This can be particularly helpful with template
+# rich C++ code for which doxygen's built-in parser lacks the necessary type
+# information.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse_libclang=ON option for CMake.
+# The default value is: NO.
+
+CLANG_ASSISTED_PARSING = NO
+
+# If clang assisted parsing is enabled you can provide the compiler with command
+# line options that you would normally use when invoking the compiler. Note that
+# the include paths will already be set by doxygen for the files and directories
+# specified with INPUT and INCLUDE_PATH.
+# This tag requires that the tag CLANG_ASSISTED_PARSING is set to YES.
+
+CLANG_OPTIONS          =
+
+# If clang assisted parsing is enabled you can provide the clang parser with the
+# path to the compilation database (see:
+# http://clang.llvm.org/docs/HowToSetupToolingForLLVM.html) used when the files
+# were built. This is equivalent to specifying the "-p" option to a clang tool,
+# such as clang-check. These options will then be passed to the parser.
+# Note: The availability of this option depends on whether or not doxygen was
+# generated with the -Duse_libclang=ON option for CMake.
+
+CLANG_DATABASE_PATH    =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the alphabetical class index
+#---------------------------------------------------------------------------
+
+# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all
+# compounds will be generated. Enable this if the project contains a lot of
+# classes, structs, unions or interfaces.
+# The default value is: YES.
+
+ALPHABETICAL_INDEX     = YES
+
+# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in
+# which the alphabetical index list will be split.
+# Minimum value: 1, maximum value: 20, default value: 5.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+COLS_IN_ALPHA_INDEX    = 5
+
+# In case all classes in a project start with a common prefix, all classes will
+# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag
+# can be used to specify a prefix (or a list of prefixes) that should be ignored
+# while generating the index headers.
+# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.
+
+IGNORE_PREFIX          =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the HTML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output
+# The default value is: YES.
+
+GENERATE_HTML          = YES
+
+# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_OUTPUT            = html
+
+# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
+# generated HTML page (for example: .htm, .php, .asp).
+# The default value is: .html.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FILE_EXTENSION    = .html
+
+# The HTML_HEADER tag can be used to specify a user-defined HTML header file for
+# each generated HTML page. If the tag is left blank doxygen will generate a
+# standard header.
+#
+# To get valid HTML the header file that includes any scripts and style sheets
+# that doxygen needs, which is dependent on the configuration options used (e.g.
+# the setting GENERATE_TREEVIEW). It is highly recommended to start with a
+# default header using
+# doxygen -w html new_header.html new_footer.html new_stylesheet.css
+# YourConfigFile
+# and then modify the file new_header.html. See also section "Doxygen usage"
+# for information on how to generate the default header that doxygen normally
+# uses.
+# Note: The header is subject to change so you typically have to regenerate the
+# default header when upgrading to a newer version of doxygen. For a description
+# of the possible markers and block names see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_HEADER            =
+
+# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each
+# generated HTML page. If the tag is left blank doxygen will generate a standard
+# footer. See HTML_HEADER for more information on how to generate a default
+# footer and what special commands can be used inside the footer. See also
+# section "Doxygen usage" for information on how to generate the default footer
+# that doxygen normally uses.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_FOOTER            =
+
+# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style
+# sheet that is used by each HTML page. It can be used to fine-tune the look of
+# the HTML output. If left blank doxygen will generate a default style sheet.
+# See also section "Doxygen usage" for information on how to generate the style
+# sheet that doxygen normally uses.
+# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as
+# it is more robust and this tag (HTML_STYLESHEET) will in the future become
+# obsolete.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_STYLESHEET        =
+
+# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# cascading style sheets that are included after the standard style sheets
+# created by doxygen. Using this option one can overrule certain style aspects.
+# This is preferred over using HTML_STYLESHEET since it does not replace the
+# standard style sheet and is therefore more robust against future updates.
+# Doxygen will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list). For an example see the documentation.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_STYLESHEET  =
+
+# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the HTML output directory. Note
+# that these files will be copied to the base HTML output directory. Use the
+# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these
+# files. In the HTML_STYLESHEET file, use the file name only. Also note that the
+# files will be copied as-is; there are no commands or markers available.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_EXTRA_FILES       =
+
+# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen
+# will adjust the colors in the style sheet and background images according to
+# this color. Hue is specified as an angle on a colorwheel, see
+# https://en.wikipedia.org/wiki/Hue for more information. For instance the value
+# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300
+# purple, and 360 is red again.
+# Minimum value: 0, maximum value: 359, default value: 220.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_HUE    = 220
+
+# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
+# in the HTML output. For a value of 0 the output will use grayscales only. A
+# value of 255 will produce the most vivid colors.
+# Minimum value: 0, maximum value: 255, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_SAT    = 100
+
+# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
+# luminance component of the colors in the HTML output. Values below 100
+# gradually make the output lighter, whereas values above 100 make the output
+# darker. The value divided by 100 is the actual gamma applied, so 80 represents
+# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not
+# change the gamma.
+# Minimum value: 40, maximum value: 240, default value: 80.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_COLORSTYLE_GAMMA  = 80
+
+# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML
+# page will contain the date and time when the page was generated. Setting this
+# to YES can help to show when doxygen was last run and thus if the
+# documentation is up to date.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_TIMESTAMP         = NO
+
+# If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML
+# documentation will contain a main index with vertical navigation menus that
+# are dynamically created via JavaScript. If disabled, the navigation index will
+# consists of multiple levels of tabs that are statically embedded in every HTML
+# page. Disable this option to support browsers that do not have JavaScript,
+# like the Qt help browser.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_MENUS     = YES
+
+# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
+# documentation will contain sections that can be hidden and shown after the
+# page has loaded.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_DYNAMIC_SECTIONS  = NO
+
+# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries
+# shown in the various tree structured indices initially; the user can expand
+# and collapse entries dynamically later on. Doxygen will expand the tree to
+# such a level that at most the specified number of entries are visible (unless
+# a fully collapsed tree already exceeds this amount). So setting the number of
+# entries 1 will produce a full collapsed tree by default. 0 is a special value
+# representing an infinite number of entries and will result in a full expanded
+# tree by default.
+# Minimum value: 0, maximum value: 9999, default value: 100.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+HTML_INDEX_NUM_ENTRIES = 100
+
+# If the GENERATE_DOCSET tag is set to YES, additional index files will be
+# generated that can be used as input for Apple's Xcode 3 integrated development
+# environment (see: https://developer.apple.com/xcode/), introduced with OSX
+# 10.5 (Leopard). To create a documentation set, doxygen will generate a
+# Makefile in the HTML output directory. Running make will produce the docset in
+# that directory and running make install will install the docset in
+# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at
+# startup. See https://developer.apple.com/library/archive/featuredarticles/Doxy
+# genXcode/_index.html for more information.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_DOCSET        = NO
+
+# This tag determines the name of the docset feed. A documentation feed provides
+# an umbrella under which multiple documentation sets from a single provider
+# (such as a company or product suite) can be grouped.
+# The default value is: Doxygen generated docs.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_FEEDNAME        = "Doxygen generated docs"
+
+# This tag specifies a string that should uniquely identify the documentation
+# set bundle. This should be a reverse domain-name style string, e.g.
+# com.mycompany.MyDocSet. Doxygen will append .docset to the name.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_BUNDLE_ID       = org.doxygen.Project
+
+# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify
+# the documentation publisher. This should be a reverse domain-name style
+# string, e.g. com.mycompany.MyDocSet.documentation.
+# The default value is: org.doxygen.Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_ID    = org.doxygen.Publisher
+
+# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.
+# The default value is: Publisher.
+# This tag requires that the tag GENERATE_DOCSET is set to YES.
+
+DOCSET_PUBLISHER_NAME  = Publisher
+
+# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three
+# additional HTML index files: index.hhp, index.hhc, and index.hhk. The
+# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop
+# (see: https://www.microsoft.com/en-us/download/details.aspx?id=21138) on
+# Windows.
+#
+# The HTML Help Workshop contains a compiler that can convert all HTML output
+# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML
+# files are now used as the Windows 98 help format, and will replace the old
+# Windows help format (.hlp) on all Windows platforms in the future. Compressed
+# HTML files also contain an index, a table of contents, and you can search for
+# words in the documentation. The HTML workshop also contains a viewer for
+# compressed HTML files.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_HTMLHELP      = NO
+
+# The CHM_FILE tag can be used to specify the file name of the resulting .chm
+# file. You can add a path in front of the file if the result should not be
+# written to the html output directory.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_FILE               =
+
+# The HHC_LOCATION tag can be used to specify the location (absolute path
+# including file name) of the HTML help compiler (hhc.exe). If non-empty,
+# doxygen will try to run the HTML help compiler on the generated index.hhp.
+# The file has to be specified with full path.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+HHC_LOCATION           =
+
+# The GENERATE_CHI flag controls if a separate .chi index file is generated
+# (YES) or that it should be included in the master .chm file (NO).
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+GENERATE_CHI           = NO
+
+# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)
+# and project file content.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+CHM_INDEX_ENCODING     =
+
+# The BINARY_TOC flag controls whether a binary table of contents is generated
+# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it
+# enables the Previous and Next buttons.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+BINARY_TOC             = NO
+
+# The TOC_EXPAND flag can be set to YES to add extra items for group members to
+# the table of contents of the HTML help documentation and to the tree view.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTMLHELP is set to YES.
+
+TOC_EXPAND             = NO
+
+# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and
+# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that
+# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help
+# (.qch) of the generated HTML documentation.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_QHP           = NO
+
+# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify
+# the file name of the resulting .qch file. The path specified is relative to
+# the HTML output folder.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QCH_FILE               =
+
+# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help
+# Project output. For more information please see Qt Help Project / Namespace
+# (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace).
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_NAMESPACE          = org.doxygen.Project
+
+# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt
+# Help Project output. For more information please see Qt Help Project / Virtual
+# Folders (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual-
+# folders).
+# The default value is: doc.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_VIRTUAL_FOLDER     = doc
+
+# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom
+# filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_NAME   =
+
+# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the
+# custom filter to add. For more information please see Qt Help Project / Custom
+# Filters (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-
+# filters).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_CUST_FILTER_ATTRS  =
+
+# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this
+# project's filter section matches. Qt Help Project / Filter Attributes (see:
+# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#filter-attributes).
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHP_SECT_FILTER_ATTRS  =
+
+# The QHG_LOCATION tag can be used to specify the location of Qt's
+# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the
+# generated .qhp file.
+# This tag requires that the tag GENERATE_QHP is set to YES.
+
+QHG_LOCATION           =
+
+# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be
+# generated, together with the HTML files, they form an Eclipse help plugin. To
+# install this plugin and make it available under the help contents menu in
+# Eclipse, the contents of the directory containing the HTML and XML files needs
+# to be copied into the plugins directory of eclipse. The name of the directory
+# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.
+# After copying Eclipse needs to be restarted before the help appears.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_ECLIPSEHELP   = NO
+
+# A unique identifier for the Eclipse help plugin. When installing the plugin
+# the directory name containing the HTML and XML files should also have this
+# name. Each documentation set should have its own identifier.
+# The default value is: org.doxygen.Project.
+# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.
+
+ECLIPSE_DOC_ID         = org.doxygen.Project
+
+# If you want full control over the layout of the generated HTML pages it might
+# be necessary to disable the index and replace it with your own. The
+# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top
+# of each HTML page. A value of NO enables the index and the value YES disables
+# it. Since the tabs in the index contain the same information as the navigation
+# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+DISABLE_INDEX          = NO
+
+# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index
+# structure should be generated to display hierarchical information. If the tag
+# value is set to YES, a side panel will be generated containing a tree-like
+# index structure (just like the one that is generated for HTML Help). For this
+# to work a browser that supports JavaScript, DHTML, CSS and frames is required
+# (i.e. any modern browser). Windows users are probably better off using the
+# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can
+# further fine-tune the look of the index. As an example, the default style
+# sheet generated by doxygen has an example that shows how to put an image at
+# the root of the tree instead of the PROJECT_NAME. Since the tree basically has
+# the same information as the tab index, you could consider setting
+# DISABLE_INDEX to YES when enabling this option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+GENERATE_TREEVIEW      = NO
+
+# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that
+# doxygen will group on one line in the generated HTML documentation.
+#
+# Note that a value of 0 will completely suppress the enum values from appearing
+# in the overview section.
+# Minimum value: 0, maximum value: 20, default value: 4.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+ENUM_VALUES_PER_LINE   = 4
+
+# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used
+# to set the initial width (in pixels) of the frame in which the tree is shown.
+# Minimum value: 0, maximum value: 1500, default value: 250.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+TREEVIEW_WIDTH         = 250
+
+# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to
+# external symbols imported via tag files in a separate window.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+EXT_LINKS_IN_WINDOW    = NO
+
+# Use this tag to change the font size of LaTeX formulas included as images in
+# the HTML documentation. When you change the font size after a successful
+# doxygen run you need to manually remove any form_*.png images from the HTML
+# output directory to force them to be regenerated.
+# Minimum value: 8, maximum value: 50, default value: 10.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_FONTSIZE       = 10
+
+# Use the FORMULA_TRANSPARENT tag to determine whether or not the images
+# generated for formulas are transparent PNGs. Transparent PNGs are not
+# supported properly for IE 6.0, but are supported on all modern browsers.
+#
+# Note that when changing this option you need to delete any form_*.png files in
+# the HTML output directory before the changes have effect.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+FORMULA_TRANSPARENT    = YES
+
+# The FORMULA_MACROFILE can contain LaTeX \newcommand and \renewcommand commands
+# to create new LaTeX commands to be used in formulas as building blocks. See
+# the section "Including formulas" for details.
+
+FORMULA_MACROFILE      =
+
+# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see
+# https://www.mathjax.org) which uses client side JavaScript for the rendering
+# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX
+# installed or if you want to formulas look prettier in the HTML output. When
+# enabled you may also need to install MathJax separately and configure the path
+# to it using the MATHJAX_RELPATH option.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+USE_MATHJAX            = YES
+
+# When MathJax is enabled you can set the default output format to be used for
+# the MathJax output. See the MathJax site (see:
+# http://docs.mathjax.org/en/latest/output.html) for more details.
+# Possible values are: HTML-CSS (which is slower, but has the best
+# compatibility), NativeMML (i.e. MathML) and SVG.
+# The default value is: HTML-CSS.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_FORMAT         = HTML-CSS
+
+# When MathJax is enabled you need to specify the location relative to the HTML
+# output directory using the MATHJAX_RELPATH option. The destination directory
+# should contain the MathJax.js script. For instance, if the mathjax directory
+# is located at the same level as the HTML output directory, then
+# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax
+# Content Delivery Network so you can quickly see the result without installing
+# MathJax. However, it is strongly recommended to install a local copy of
+# MathJax from https://www.mathjax.org before deployment.
+# The default value is: https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_RELPATH        = https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/
+
+# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax
+# extension names that should be enabled during MathJax rendering. For example
+# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_EXTENSIONS     =
+
+# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces
+# of code that will be used on startup of the MathJax code. See the MathJax site
+# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an
+# example see the documentation.
+# This tag requires that the tag USE_MATHJAX is set to YES.
+
+MATHJAX_CODEFILE       =
+
+# When the SEARCHENGINE tag is enabled doxygen will generate a search box for
+# the HTML output. The underlying search engine uses javascript and DHTML and
+# should work on any modern browser. Note that when using HTML help
+# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)
+# there is already a search function so this one should typically be disabled.
+# For large projects the javascript based search engine can be slow, then
+# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to
+# search using the keyboard; to jump to the search box use <access key> + S
+# (what the <access key> is depends on the OS and browser, but it is typically
+# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down
+# key> to jump into the search results window, the results can be navigated
+# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel
+# the search. The filter options can be selected when the cursor is inside the
+# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>
+# to select a filter and <Enter> or <escape> to activate or cancel the filter
+# option.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_HTML is set to YES.
+
+SEARCHENGINE           = YES
+
+# When the SERVER_BASED_SEARCH tag is enabled the search engine will be
+# implemented using a web server instead of a web client using JavaScript. There
+# are two flavors of web server based searching depending on the EXTERNAL_SEARCH
+# setting. When disabled, doxygen will generate a PHP script for searching and
+# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing
+# and searching needs to be provided by external tools. See the section
+# "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SERVER_BASED_SEARCH    = NO
+
+# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP
+# script for searching. Instead the search results are written to an XML file
+# which needs to be processed by an external indexer. Doxygen will invoke an
+# external search engine pointed to by the SEARCHENGINE_URL option to obtain the
+# search results.
+#
+# Doxygen ships with an example indexer (doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: https://xapian.org/).
+#
+# See the section "External Indexing and Searching" for details.
+# The default value is: NO.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH        = NO
+
+# The SEARCHENGINE_URL should point to a search engine hosted by a web server
+# which will return the search results when EXTERNAL_SEARCH is enabled.
+#
+# Doxygen ships with an example indexer (doxyindexer) and search engine
+# (doxysearch.cgi) which are based on the open source search engine library
+# Xapian (see: https://xapian.org/). See the section "External Indexing and
+# Searching" for details.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHENGINE_URL       =
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed
+# search data is written to a file for indexing by an external tool. With the
+# SEARCHDATA_FILE tag the name of this file can be specified.
+# The default file is: searchdata.xml.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+SEARCHDATA_FILE        = searchdata.xml
+
+# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the
+# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is
+# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple
+# projects and redirect the results back to the right project.
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTERNAL_SEARCH_ID     =
+
+# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen
+# projects other than the one defined by this configuration file, but that are
+# all added to the same external search index. Each project needs to have a
+# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of
+# to a relative location where the documentation can be found. The format is:
+# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...
+# This tag requires that the tag SEARCHENGINE is set to YES.
+
+EXTRA_SEARCH_MAPPINGS  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the LaTeX output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.
+# The default value is: YES.
+
+GENERATE_LATEX         = YES
+
+# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: latex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_OUTPUT           = latex
+
+# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be
+# invoked.
+#
+# Note that when not enabling USE_PDFLATEX the default is latex when enabling
+# USE_PDFLATEX the default is pdflatex and when in the later case latex is
+# chosen this is overwritten by pdflatex. For specific output languages the
+# default can have been set differently, this depends on the implementation of
+# the output language.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_CMD_NAME         =
+
+# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate
+# index for LaTeX.
+# Note: This tag is used in the Makefile / make.bat.
+# See also: LATEX_MAKEINDEX_CMD for the part in the generated output file
+# (.tex).
+# The default file is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+MAKEINDEX_CMD_NAME     = makeindex
+
+# The LATEX_MAKEINDEX_CMD tag can be used to specify the command name to
+# generate index for LaTeX. In case there is no backslash (\) as first character
+# it will be automatically added in the LaTeX code.
+# Note: This tag is used in the generated output file (.tex).
+# See also: MAKEINDEX_CMD_NAME for the part in the Makefile / make.bat.
+# The default value is: makeindex.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_MAKEINDEX_CMD    = makeindex
+
+# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+COMPACT_LATEX          = NO
+
+# The PAPER_TYPE tag can be used to set the paper type that is used by the
+# printer.
+# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x
+# 14 inches) and executive (7.25 x 10.5 inches).
+# The default value is: a4.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PAPER_TYPE             = a4
+
+# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names
+# that should be included in the LaTeX output. The package can be specified just
+# by its name or with the correct syntax as to be used with the LaTeX
+# \usepackage command. To get the times font for instance you can specify :
+# EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}
+# To use the option intlimits with the amsmath package you can specify:
+# EXTRA_PACKAGES=[intlimits]{amsmath}
+# If left blank no extra packages will be included.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+EXTRA_PACKAGES         =
+
+# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the
+# generated LaTeX document. The header should contain everything until the first
+# chapter. If it is left blank doxygen will generate a standard header. See
+# section "Doxygen usage" for information on how to let doxygen write the
+# default header to a separate file.
+#
+# Note: Only use a user-defined header if you know what you are doing! The
+# following commands have a special meaning inside the header: $title,
+# $datetime, $date, $doxygenversion, $projectname, $projectnumber,
+# $projectbrief, $projectlogo. Doxygen will replace $title with the empty
+# string, for the replacement values of the other commands the user is referred
+# to HTML_HEADER.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HEADER           =
+
+# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the
+# generated LaTeX document. The footer should contain everything after the last
+# chapter. If it is left blank doxygen will generate a standard footer. See
+# LATEX_HEADER for more information on how to generate a default footer and what
+# special commands can be used inside the footer.
+#
+# Note: Only use a user-defined footer if you know what you are doing!
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_FOOTER           =
+
+# The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined
+# LaTeX style sheets that are included after the standard style sheets created
+# by doxygen. Using this option one can overrule certain style aspects. Doxygen
+# will copy the style sheet files to the output directory.
+# Note: The order of the extra style sheet files is of importance (e.g. the last
+# style sheet in the list overrules the setting of the previous ones in the
+# list).
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_STYLESHEET =
+
+# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or
+# other source files which should be copied to the LATEX_OUTPUT output
+# directory. Note that the files will be copied as-is; there are no commands or
+# markers available.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EXTRA_FILES      =
+
+# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is
+# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will
+# contain links (just like the HTML output) instead of page references. This
+# makes the output suitable for online browsing using a PDF viewer.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+PDF_HYPERLINKS         = YES
+
+# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate
+# the PDF file directly from the LaTeX files. Set this option to YES, to get a
+# higher quality PDF documentation.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+USE_PDFLATEX           = YES
+
+# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode
+# command to the generated LaTeX files. This will instruct LaTeX to keep running
+# if errors occur, instead of asking the user for help. This option is also used
+# when generating formulas in HTML.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BATCHMODE        = NO
+
+# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the
+# index chapters (such as File Index, Compound Index, etc.) in the output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_HIDE_INDICES     = NO
+
+# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source
+# code with syntax highlighting in the LaTeX output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_SOURCE_CODE      = NO
+
+# The LATEX_BIB_STYLE tag can be used to specify the style to use for the
+# bibliography, e.g. plainnat, or ieeetr. See
+# https://en.wikipedia.org/wiki/BibTeX and \cite for more info.
+# The default value is: plain.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_BIB_STYLE        = plain
+
+# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated
+# page will contain the date and time when the page was generated. Setting this
+# to NO can help when comparing the output of multiple runs.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_TIMESTAMP        = NO
+
+# The LATEX_EMOJI_DIRECTORY tag is used to specify the (relative or absolute)
+# path from which the emoji images will be read. If a relative path is entered,
+# it will be relative to the LATEX_OUTPUT directory. If left blank the
+# LATEX_OUTPUT directory will be used.
+# This tag requires that the tag GENERATE_LATEX is set to YES.
+
+LATEX_EMOJI_DIRECTORY  =
+
+#---------------------------------------------------------------------------
+# Configuration options related to the RTF output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The
+# RTF output is optimized for Word 97 and may not look too pretty with other RTF
+# readers/editors.
+# The default value is: NO.
+
+GENERATE_RTF           = NO
+
+# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: rtf.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_OUTPUT             = rtf
+
+# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF
+# documents. This may be useful for small projects and may help to save some
+# trees in general.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+COMPACT_RTF            = NO
+
+# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will
+# contain hyperlink fields. The RTF file will contain links (just like the HTML
+# output) instead of page references. This makes the output suitable for online
+# browsing using Word or some other Word compatible readers that support those
+# fields.
+#
+# Note: WordPad (write) and others do not support links.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_HYPERLINKS         = NO
+
+# Load stylesheet definitions from file. Syntax is similar to doxygen's
+# configuration file, i.e. a series of assignments. You only have to provide
+# replacements, missing definitions are set to their default value.
+#
+# See also section "Doxygen usage" for information on how to generate the
+# default style sheet that doxygen normally uses.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_STYLESHEET_FILE    =
+
+# Set optional variables used in the generation of an RTF document. Syntax is
+# similar to doxygen's configuration file. A template extensions file can be
+# generated using doxygen -e rtf extensionFile.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_EXTENSIONS_FILE    =
+
+# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code
+# with syntax highlighting in the RTF output.
+#
+# Note that which sources are shown also depends on other settings such as
+# SOURCE_BROWSER.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_RTF is set to YES.
+
+RTF_SOURCE_CODE        = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the man page output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for
+# classes and files.
+# The default value is: NO.
+
+GENERATE_MAN           = NO
+
+# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it. A directory man3 will be created inside the directory specified by
+# MAN_OUTPUT.
+# The default directory is: man.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_OUTPUT             = man
+
+# The MAN_EXTENSION tag determines the extension that is added to the generated
+# man pages. In case the manual section does not start with a number, the number
+# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is
+# optional.
+# The default value is: .3.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_EXTENSION          = .3
+
+# The MAN_SUBDIR tag determines the name of the directory created within
+# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by
+# MAN_EXTENSION with the initial . removed.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_SUBDIR             =
+
+# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it
+# will generate one additional man file for each entity documented in the real
+# man page(s). These additional files only source the real man page, but without
+# them the man command would be unable to find the correct page.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_MAN is set to YES.
+
+MAN_LINKS              = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the XML output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that
+# captures the structure of the code including all documentation.
+# The default value is: NO.
+
+GENERATE_XML           = NO
+
+# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a
+# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of
+# it.
+# The default directory is: xml.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_OUTPUT             = xml
+
+# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program
+# listings (including syntax highlighting and cross-referencing information) to
+# the XML output. Note that enabling this will significantly increase the size
+# of the XML output.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_PROGRAMLISTING     = YES
+
+# If the XML_NS_MEMB_FILE_SCOPE tag is set to YES, doxygen will include
+# namespace members in file scope as well, matching the HTML output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_XML is set to YES.
+
+XML_NS_MEMB_FILE_SCOPE = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the DOCBOOK output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files
+# that can be used to generate PDF.
+# The default value is: NO.
+
+GENERATE_DOCBOOK       = NO
+
+# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.
+# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in
+# front of it.
+# The default directory is: docbook.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_OUTPUT         = docbook
+
+# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the
+# program listings (including syntax highlighting and cross-referencing
+# information) to the DOCBOOK output. Note that enabling this will significantly
+# increase the size of the DOCBOOK output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_DOCBOOK is set to YES.
+
+DOCBOOK_PROGRAMLISTING = NO
+
+#---------------------------------------------------------------------------
+# Configuration options for the AutoGen Definitions output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an
+# AutoGen Definitions (see http://autogen.sourceforge.net/) file that captures
+# the structure of the code including all documentation. Note that this feature
+# is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_AUTOGEN_DEF   = NO
+
+#---------------------------------------------------------------------------
+# Configuration options related to the Perl module output
+#---------------------------------------------------------------------------
+
+# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module
+# file that captures the structure of the code including all documentation.
+#
+# Note that this feature is still experimental and incomplete at the moment.
+# The default value is: NO.
+
+GENERATE_PERLMOD       = NO
+
+# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary
+# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI
+# output from the Perl module output.
+# The default value is: NO.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_LATEX          = NO
+
+# If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely
+# formatted so it can be parsed by a human reader. This is useful if you want to
+# understand what is going on. On the other hand, if this tag is set to NO, the
+# size of the Perl module output will be much smaller and Perl will parse it
+# just the same.
+# The default value is: YES.
+# This tag requires that the tag GENERATE_PERLMOD is set to YES.
+
+PERLMOD_PRETTY         = YES
+
+# The names of the make variables in the generated doxyrules.make file are
+# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful
+# so different doxyrules.make files included by the same Makefile don't
+# overwrite each other's variables.
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diff --git a/ggml/src/ggml-cann/acl_tensor.cpp b/ggml/src/ggml-cann/acl_tensor.cpp
new file mode 100644
index 00000000000000..960ce9a0368d7f
--- /dev/null
+++ b/ggml/src/ggml-cann/acl_tensor.cpp
@@ -0,0 +1,198 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#include "acl_tensor.h"
+
+#include <algorithm>
+#include <cstring>
+
+aclDataType ggml_cann_type_mapping(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_F32:
+            return ACL_FLOAT;
+        case GGML_TYPE_F16:
+            return ACL_FLOAT16;
+        case GGML_TYPE_I8:
+            return ACL_INT8;
+        case GGML_TYPE_I16:
+            return ACL_INT16;
+        case GGML_TYPE_I32:
+            return ACL_INT32;
+        default:
+            return ACL_DT_UNDEFINED;
+    }
+    return ACL_DT_UNDEFINED;
+}
+
+aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne,
+                                   size_t* nb, int64_t dims, aclFormat format,
+                                   size_t offset) {
+    // If tensor is bcasted, Up to GGML_MAX_DIMS additional dimensions will be
+    // added.
+    int64_t acl_ne[GGML_MAX_DIMS * 2], acl_stride[GGML_MAX_DIMS * 2];
+
+    int64_t acl_storage_len = 0;
+    if (ne == nullptr) {
+        acl_storage_len = ggml_nbytes(tensor);
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            acl_ne[i] = tensor->ne[i];
+            // The step size of acl is in elements.
+            acl_stride[i] = tensor->nb[i] / ggml_element_size(tensor);
+        }
+    } else {
+        // With bcast
+        for (int i = 0; i < dims; i++) {
+            acl_storage_len += (ne[i] - 1) * nb[i];
+            acl_ne[i] = ne[i];
+            acl_stride[i] = nb[i] / ggml_element_size(tensor);
+        }
+    }
+
+    // Reverse ne and stride.
+    int64_t final_dims = (dims == 0 ? GGML_MAX_DIMS : dims);
+    std::reverse(acl_ne, acl_ne + final_dims);
+    std::reverse(acl_stride, acl_stride + final_dims);
+
+    aclTensor* acl_tensor = aclCreateTensor(
+        acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride,
+        offset / ggml_element_size(tensor), format, &acl_storage_len, 1,
+        tensor->data);
+
+    return acl_tensor;
+}
+
+bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) {
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (t1->ne[i] != t0->ne[i] && t1->ne[i] != 1) {
+            return true;
+        }
+    }
+    return false;
+}
+
+aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype,
+                                   size_t type_size, int64_t* ne, size_t* nb,
+                                   int64_t dims, aclFormat format,
+                                   size_t offset) {
+    int64_t tmp_ne[GGML_MAX_DIMS * 2];
+    int64_t tmp_stride[GGML_MAX_DIMS * 2];
+
+    memcpy(tmp_ne, ne, dims * sizeof(int64_t));
+    for (int i = 0; i < dims; i++) {
+        tmp_stride[i] = nb[i] / type_size;
+    }
+
+    std::reverse(tmp_ne, tmp_ne + dims);
+    std::reverse(tmp_stride, tmp_stride + dims);
+
+    int64_t acl_storage_len = 0;
+    for (int i = 0; i < dims; i++) {
+        acl_storage_len += (ne[i] - 1) * nb[i];
+    }
+
+    aclTensor* acl_tensor =
+        aclCreateTensor(tmp_ne, dims, dtype, tmp_stride, offset / type_size,
+                        format, &acl_storage_len, 1, data_ptr);
+
+    return acl_tensor;
+}
+
+int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0,
+                                  const ggml_tensor* src1,
+                                  int64_t* bcast_src0_ne,
+                                  int64_t* bcast_src1_ne, size_t* bcast_src0_nb,
+                                  size_t* bcast_src1_nb) {
+    GGML_ASSERT(ggml_can_repeat(src1, src0));
+    int bcast_dim_cnt = 0;
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        int64_t nr = src0->ne[i] / src1->ne[i];
+        bcast_src0_ne[bcast_dim_cnt] = src0->ne[i] / nr;
+        bcast_src1_ne[bcast_dim_cnt] = src1->ne[i];
+        bcast_src0_nb[bcast_dim_cnt] = src0->nb[i];
+        bcast_src1_nb[bcast_dim_cnt] = src1->nb[i];
+        bcast_dim_cnt++;
+        if (nr != 1) {
+            // Need to add an extra dim.
+            bcast_src0_ne[bcast_dim_cnt] = nr;
+            bcast_src1_ne[bcast_dim_cnt] = 1;
+            bcast_src0_nb[bcast_dim_cnt] = bcast_src0_nb[bcast_dim_cnt - 1] *
+                                           bcast_src0_ne[bcast_dim_cnt - 1];
+            bcast_src1_nb[bcast_dim_cnt] = bcast_src1_nb[bcast_dim_cnt - 1] *
+                                           bcast_src1_ne[bcast_dim_cnt - 1];
+            bcast_dim_cnt++;
+        }
+    }
+    return bcast_dim_cnt;
+}
+
+int64_t ggml_cann_get_mulmat_bcast_shape(
+    const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne,
+    const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb,
+    int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne,
+    size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb) {
+    // input and dst shoule in same shape, except first two dims.
+    GGML_ASSERT(input_ne[2] == dst_ne[2]);
+    GGML_ASSERT(input_ne[3] == dst_ne[3]);
+
+    int bcast_dim_cnt = 0;
+
+    // For mul_mat, a dimension needs to be added before the dimension that
+    // weight needs to be expanded to satisfy the bcast rule of matrix
+    // multiplication.
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        int64_t nr = input_ne[i] / weight_ne[i];
+        // Do not use bcast in the first two dimensions because we only support
+        // the bcast batch dimension. Just copy them.
+        if (i < 2 || nr == 1) {
+            bcast_input_ne[bcast_dim_cnt] = input_ne[i];
+            bcast_weight_ne[bcast_dim_cnt] = weight_ne[i];
+            bcast_dst_ne[bcast_dim_cnt] = dst_ne[i];
+
+            bcast_input_nb[bcast_dim_cnt] = input_nb[i];
+            bcast_weight_nb[bcast_dim_cnt] = weight_nb[i];
+            bcast_dst_nb[bcast_dim_cnt] = dst_nb[i];
+            bcast_dim_cnt++;
+        } else {
+            // Need to add an extra dim.
+            bcast_input_ne[bcast_dim_cnt] = nr;
+            bcast_dst_ne[bcast_dim_cnt] = nr;
+            bcast_weight_ne[bcast_dim_cnt] = 1;
+            bcast_input_nb[bcast_dim_cnt] = input_nb[i];
+            bcast_dst_nb[bcast_dim_cnt] = dst_nb[i];
+            bcast_weight_nb[bcast_dim_cnt] = weight_nb[i];
+            bcast_dim_cnt++;
+
+            bcast_input_ne[bcast_dim_cnt] = input_ne[i] / nr;
+            bcast_dst_ne[bcast_dim_cnt] = dst_ne[i] / nr;
+            bcast_weight_ne[bcast_dim_cnt] = weight_ne[i];
+            bcast_input_nb[bcast_dim_cnt] = bcast_input_nb[bcast_dim_cnt - 1] *
+                                            bcast_input_ne[bcast_dim_cnt - 1];
+            bcast_dst_nb[bcast_dim_cnt] = bcast_dst_nb[bcast_dim_cnt - 1] *
+                                          bcast_dst_ne[bcast_dim_cnt - 1];
+            bcast_weight_nb[bcast_dim_cnt] =
+                bcast_weight_nb[bcast_dim_cnt - 1] *
+                bcast_weight_ne[bcast_dim_cnt - 1];
+            bcast_dim_cnt++;
+        }
+    }
+    return bcast_dim_cnt;
+}
diff --git a/ggml/src/ggml-cann/acl_tensor.h b/ggml/src/ggml-cann/acl_tensor.h
new file mode 100644
index 00000000000000..7d0bf04e072a31
--- /dev/null
+++ b/ggml/src/ggml-cann/acl_tensor.h
@@ -0,0 +1,230 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#ifndef CANN_ACL_TENSOR_H
+#define CANN_ACL_TENSOR_H
+
+#include <aclnn/aclnn_base.h>
+#include "common.h"
+
+/**
+ * @brief	Maps a ggml_type to its corresponding aclDataType.
+ *
+ * @details	This function takes a ggml_type as input and returns the corresponding
+ *			aclDataType. It supports mapping for various ggml_types. If the input type
+ *			does not match any of the predefined ggml_types, the function returns
+ *          ACL_DT_UNDEFINED.
+ *
+ * @param	type    The ggml_type to be mapped.
+ * @return	The corresponding aclDataType. If the input type is not recognized,
+ *			ACL_DT_UNDEFINED is returned.
+ */
+aclDataType ggml_cann_type_mapping(ggml_type type);
+
+/**
+ * @brief   Creates an ACL tensor from a ggml_tensor with optional shape.
+ *
+ * @details This function creates an ACL tensor based on the properties of the
+ *          provided ggml_tensor. It supports customer shape by adjusting dimensions
+ *          and strides accordingly. If customer shape is applied, additional
+ *          dimensions and strides are calculated based on the provided parameters.
+ *
+ * @param   tensor      Pointer to the ggml_tensor to be converted to ACL tensor.
+ * @param   ne          Pointer to an array containing dimensions. Defaults to nullptr
+ *                      if no customer shape is applied.
+ * @param   nb          Pointer to an array containing strides. Defaults to nullptr
+ *                      if no customer shape is applied.
+ * @param   dims        Number of dimensions in the tensor. Defaults to 0 if no customer
+ *                      shape is applied.
+ * @param   format      ACL tensor format. Defaults to ACL_FORMAT_ND.
+ * @param   offset      Offset in bytes for the ACL tensor data. Defaults to 0.
+ * @return  Pointer to the created ACL tensor.
+ */
+aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne = nullptr,
+                             size_t* nb = nullptr, int64_t dims = 0,
+                             aclFormat format = ACL_FORMAT_ND,
+                             size_t offset = 0);
+
+/**
+ * @brief   Creates an ACL tensor from provided parameters.
+ *
+ * @details This function creates an ACL tensor using the provided data pointer,
+ *          data type, dimensions, strides, format, offset, and additional parameters.
+ *          It calculates necessary dimensions and strides based on the provided ne and nb
+ *          arrays, adjusting them for the ACL tensor creation. The ACL storage length
+ *          is also calculated based on the provided dimensions and strides.
+ *
+ * @param   data_ptr    Pointer to the data buffer for the ACL tensor.
+ * @param   dtype       ACL data type of the tensor.
+ * @param   type_size   Size of each element in the tensor data buffer.
+ * @param   ne          Pointer to an array containing tensor dimensions.
+ * @param   nb          Pointer to an array containing tensor strides.
+ * @param   dims        Number of dimensions of the tensor.
+ * @param   format      ACL tensor format. Defaults to ACL_FORMAT_ND.
+ * @param   offset      Offset in bytes for the ACL tensor data. Defaults to 0.
+ * @return  Pointer to the created ACL tensor.
+ */
+aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype,
+                             size_t type_size, int64_t* ne, size_t* nb,
+                             int64_t dims, aclFormat format = ACL_FORMAT_ND,
+                             size_t offset = 0);
+
+/**
+ * @brief   Checks if tensors require broadcasting based on their shapes.
+ *
+ * @details This function determines if two ggml_tensors need to be broadcasted for
+ *          element-wise operations. Broadcasting is necessary if the shapes of the
+ *          tensors are not identical and no dimension in either tensor equals 1.
+ *
+ * @param   t0      Pointer to the first ggml_tensor.
+ * @param   t1      Pointer to the second ggml_tensor.
+ * @return  True if broadcasting is needed, False otherwise.
+ *
+ * @remarks This function iterates over the dimensions of t0 and t1. It checks if each
+ *          dimension in t1 differs from t0's corresponding dimension and is not equal
+ *          to 1. If such a dimension is found, broadcasting is required to align t1
+ *          with t0 for element-wise operations.
+ */
+bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1);
+
+/**
+ * @brief   Computes broadcast shapes and strides for two ggml_tensors.
+ *
+ * @details This function calculates the broadcast shapes and strides for two ggml_tensors,
+ *          following the broadcasting rules similar to numpy. It adjusts dimensions and
+ *          strides to ensure compatibility for element-wise operations where one tensor
+ *          can be broadcasted to match the shape of another tensor.
+ *
+ * @param   src0                Pointer to the first ggml_tensor.
+ * @param   src1                Pointer to the second ggml_tensor.
+ * @param   bcast_ne_src0       Output array to store broadcasted dimensions for src0.
+ * @param   bcast_ne_src1       Output array to store broadcasted dimensions for src1.
+ * @param   bcast_nb_src0       Output array to store broadcasted strides for src0.
+ * @param   bcast_nb_src1       Output array to store broadcasted strides for src1.
+ * @return  Number of dimensions in the broadcasted shape.
+ *
+ * @pre     ggml_can_repeat(src1, src0) must return true, indicating src1 can be broadcasted
+ *          to match src0.
+ *
+ * @remarks This function iterates over the dimensions of src0 and src1, calculating the
+ *          necessary broadcast dimensions and strides. If a dimension requires broadcasting
+ *          (i.e., its size in src1 is smaller than in src0), an additional dimension is
+ *          added with size calculated to match src0's dimension. This adjustment ensures
+ *          that src1 can be element-wise broadcasted to src0's shape.
+ *
+ *  How it works:
+ *
+ *  if dim0 has padding.
+ *  a -> (2, 2) padding = 2
+ *   a: [[1, 2, *, *]
+ *       [2, 3, *, *]]
+ *  nb = (8, 4, 2)
+ *
+ *  if a should bcast with b -> (2, 4)
+ *  b' -> (2, 2, 2)
+ *  b : [[1, 2, 3, 4, *, *]
+ *       [5, 6, 7, 8, *, *]]
+ *  nb = (12, 6, 1)
+ *
+ *  after bcast:
+ *  a' -> (2, 1, 2)
+ *  a': [[[1, 2], *, *]
+ *       [[2, 3], *, *]]
+ *  nb = (8, 4, 2, 1)
+ *
+ *  b' : [[[1, 2], [3, 4], *, *]
+ *        [[5, 6], [7, 8], *, *]]
+ *  nb = (12, 6, 2, 1)
+ *  \endcode
+ *
+ *  dim1 in a inserted dim, should add nb for dim1,
+ *  and all other nb moves to next in order.
+ */
+int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* src1,
+                        int64_t* bcast_ne_src0, int64_t* bcast_ne_src1,
+                        size_t* bcast_nb_src0, size_t* bcast_nb_src1);
+
+// Bcast macro to avoid duplicate code.
+#define BCAST_SHAPE(src0, src1)                                              \
+    int64_t bcast_##src0##_ne[GGML_MAX_DIMS * 2];                            \
+    int64_t bcast_##src1##_ne[GGML_MAX_DIMS * 2];                            \
+    size_t bcast_##src0##_nb[GGML_MAX_DIMS * 2];                             \
+    size_t bcast_##src1##_nb[GGML_MAX_DIMS * 2];                             \
+    int64_t bcast_dims = ggml_cann_get_bcast_shape(                          \
+        src0, src1, bcast_##src0##_ne, bcast_##src1##_ne, bcast_##src0##_nb, \
+        bcast_##src1##_nb);
+
+#define BCAST_PARAM(tensor) bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims
+
+/**
+ * @brief Calculates broadcast shapes for matrix multiplication.
+ *
+ * @details This function computes the broadcast shapes required for matrix multiplication
+ *          based on the input, weight, and destination tensor shapes. It ensures that the
+ *          dimensions of weight tensors are expanded appropriately to satisfy matrix
+ *          multiplication broadcast rules.
+ *
+ * @param input_ne      Array containing the dimensions of the input tensor.
+ * @param weight_ne     Array containing the dimensions of the weight tensor.
+ * @param dst_ne        Array containing the dimensions of the destination tensor.
+ * @param input_nb      Array containing the strides of the input tensor.
+ * @param weight_nb     Array containing the strides of the weight tensor.
+ * @param dst_nb        Array containing the strides of the destination tensor.
+ * @param bcast_input_ne    Output array for broadcasted input tensor dimensions.
+ * @param bcast_weight_ne   Output array for broadcasted weight tensor dimensions.
+ * @param bcast_dst_ne      Output array for broadcasted destination tensor dimensions.
+ * @param bcast_input_nb    Output array for broadcasted input tensor strides.
+ * @param bcast_weight_nb   Output array for broadcasted weight tensor strides.
+ * @param bcast_dst_nb      Output array for broadcasted destination tensor strides.
+ * @return The number of dimensions in the broadcasted tensors.
+ *
+ * @remarks This function iterates over the tensor dimensions and calculates the broadcast
+ *          shapes needed for matrix multiplication. It ensures that dimensions where
+ *          weight tensor requires expansion are appropriately handled to conform with
+ *          broadcasting rules.
+ * @note compare with ggml_cann_get_bcast_shape, mul_mat broadcast need add this new dim
+ *       before cast dim.
+ * @sa ggml_cann_get_bcast_shape
+ */
+int64_t ggml_cann_get_mulmat_bcast_shape(
+    const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne,
+    const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb,
+    int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne,
+    size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb);
+
+// Bcast macro to avoid duplicate code.
+#define BCAST_MUL_MAT_SHAPE(input, weight, dst)                         \
+    int64_t bcast_##input##_ne[GGML_MAX_DIMS * 2];                      \
+    int64_t bcast_##weight##_ne[GGML_MAX_DIMS * 2];                     \
+    int64_t bcast_##dst##_ne[GGML_MAX_DIMS * 2];                        \
+    size_t bcast_##input##_nb[GGML_MAX_DIMS * 2];                       \
+    size_t bcast_##weight##_nb[GGML_MAX_DIMS * 2];                      \
+    size_t bcast_##dst##_nb[GGML_MAX_DIMS * 2];                         \
+    int64_t bcast_dims = ggml_cann_get_mulmat_bcast_shape(              \
+        input->ne, weight->ne, dst->ne, input->nb, weight->nb, dst->nb, \
+        bcast_##input##_ne, bcast_##weight##_ne, bcast_##dst##_ne,      \
+        bcast_##input##_nb, bcast_##weight##_nb, bcast_##dst##_nb);
+
+#define BCAST_MUL_MAT_PARAM(tensor) \
+    bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims
+
+#endif  // CANN_ACL_TENSOR_H
diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
new file mode 100644
index 00000000000000..a02efc8281adca
--- /dev/null
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -0,0 +1,2944 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#include "aclnn_ops.h"
+
+#include <aclnnop/aclnn_avgpool2d.h>
+#include <aclnnop/aclnn_cast.h>
+#include <aclnnop/aclnn_constant_pad_nd.h>
+#include <aclnnop/aclnn_copy.h>
+#include <aclnnop/aclnn_cos.h>
+#include <aclnnop/aclnn_exp.h>
+#include <aclnnop/aclnn_fill_scalar.h>
+#include <aclnnop/aclnn_group_norm.h>
+#include <aclnnop/aclnn_index_fill_tensor.h>
+#include <aclnnop/aclnn_layer_norm.h>
+#include <aclnnop/aclnn_matmul.h>
+#include <aclnnop/aclnn_max_pool.h>
+#include <aclnnop/aclnn_permute.h>
+#include <aclnnop/aclnn_pow_tensor_tensor.h>
+#include <aclnnop/aclnn_reduce_sum.h>
+#include <aclnnop/aclnn_repeat.h>
+#include <aclnnop/aclnn_repeat_interleave.h>
+#include <aclnnop/aclnn_roll.h>
+#include <aclnnop/aclnn_sin.h>
+#include <aclnnop/aclnn_softmax.h>
+#include <aclnnop/aclnn_tril.h>
+#include <aclnnop/aclnn_triu.h>
+#include <aclnnop/aclnn_upsample_nearest_2d.h>
+#include <aclnnop/aclnn_weight_quant_batch_matmul_v2.h>
+#include <float.h>
+
+#include <cmath>
+#include <cstring>
+#include <exception>
+#include <vector>
+
+#include "kernels/ascendc_kernels.h"
+
+#define GGML_COMMON_DECL_C
+
+#include "../ggml-common.h"
+
+/**
+ * @brief Repeats elements of a tensor along each dimension according to the
+ * specified repeat array.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor to be repeated.
+ * @param acl_dst The destination tensor after repeating.
+ * @param repeat_array The array specifying the number of repetitions along each
+ * dimension.
+ */
+static void aclnn_repeat(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                         aclTensor* acl_dst, int64_t* repeat_array) {
+    // repeat tensor along each dim with repeat_array
+    aclIntArray* repeats = aclCreateIntArray(repeat_array, GGML_MAX_DIMS);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnRepeatGetWorkspaceSize(acl_src, repeats, acl_dst,
+                                          &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        // Memory from allocator will "free" immediately, and this memory
+        // will be alloced to other pointers, but it won't access before
+        // this async task end because all tasks in same stream will execute
+        // in queue.
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+    ACL_CHECK(
+        aclnnRepeat(workspaceAddr, workspaceSize, executor, ctx.stream()));
+    ACL_CHECK(aclDestroyIntArray(repeats));
+}
+
+void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    GGML_ASSERT(ggml_can_repeat(src, dst));
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t repeatsArray[] = {dst->ne[3] / src->ne[3], dst->ne[2] / src->ne[2],
+                              dst->ne[1] / src->ne[1], dst->ne[0] / src->ne[0]};
+
+    aclnn_repeat(ctx, acl_src, acl_dst, repeatsArray);
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Adds two tensors element-wise and stores the result in a destination
+ * tensor.
+ *
+ * This function performs the operation:
+ * \f[
+ *    dst = acl\_src0 + alpha \times acl\_src1
+ * \f]
+ * where alpha is a scalar value and defaults to 1.0f.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src0 The first source tensor.
+ * @param acl_src1 The second source tensor.
+ * @param acl_dst The destination tensor where the result will be stored.
+ */
+static void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0,
+                      aclTensor* acl_src1, aclTensor* acl_dst) {
+    aclScalar* alpha = nullptr;
+    float alphaValue = 1.0f;
+    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnAddGetWorkspaceSize(acl_src0, acl_src1, alpha, acl_dst,
+                                       &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnAdd(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(alpha));
+}
+
+void ggml_cann_add(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+    GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
+
+    aclTensor* acl_src0;
+    aclTensor* acl_src1;
+    aclTensor* acl_dst;
+
+    // Need bcast
+    if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) {
+        BCAST_SHAPE(src0, src1)
+        acl_src0 = ggml_cann_create_tensor(src0, BCAST_PARAM(src0));
+        acl_src1 = ggml_cann_create_tensor(src1, BCAST_PARAM(src1));
+        acl_dst = ggml_cann_create_tensor(dst, BCAST_PARAM(src0));
+    } else {
+        acl_src0 = ggml_cann_create_tensor(src0);
+        acl_src1 = ggml_cann_create_tensor(src1);
+        acl_dst = ggml_cann_create_tensor(dst);
+    }
+
+    aclnn_add(ctx, acl_src0, acl_src1, acl_dst);
+
+    ACL_CHECK(aclDestroyTensor(acl_src0));
+    ACL_CHECK(aclDestroyTensor(acl_src1));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    float negative_slope;
+    memcpy(&negative_slope, dst->op_params, sizeof(float));
+    aclScalar* acl_negative_slope =
+        aclCreateScalar(&negative_slope, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnLeakyReluGetWorkspaceSize(
+        acl_src, acl_negative_slope, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnLeakyRelu(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(acl_negative_slope));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Concatenates a list of tensors along a specified dimension and stores
+ * the result in a destination tensor.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param tensorList The list of tensors to be concatenated.
+ * @param acl_dst The destination tensor where the concatenated result will be
+ * stored.
+ * @param concat_dim The dimension along which the tensors will be concatenated.
+ */
+static void aclnn_concat(ggml_backend_cann_context& ctx,
+                         aclTensorList* tensorList, aclTensor* acl_dst,
+                         int64_t concat_dim) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnCatGetWorkspaceSize(tensorList, concat_dim, acl_dst,
+                                       &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnCat(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+    aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
+    aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t concat_dim = 1;
+    aclTensor* tensors[] = {acl_src0, acl_src1};
+    aclTensorList* tensorList = aclCreateTensorList(tensors, 2);
+    aclnn_concat(ctx, tensorList, acl_dst, concat_dim);
+
+    ACL_CHECK(aclDestroyTensorList(tensorList));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Creates a tensor with values starting from `start`, incremented by
+ * `step`, and ending before `stop`.
+ *
+ * This function performs the operation:
+ * \f[
+ *    \text {out }_{i+1}=\text {out }_i+\text {step}
+ * \f]
+ * the range is [start, stop).
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_dst The destination tensor where the values will be stored.
+ * @param start The starting value of the range.
+ * @param stop The ending value of the range (exclusive).
+ * @param step The step size between consecutive values.
+ * @param n_elements The number of elements in the destination tensor.
+ */
+static void aclnn_arange(ggml_backend_cann_context& ctx, aclTensor* acl_dst,
+                         float start, float stop, float step,
+                         int64_t n_elements) {
+    int64_t steps = (int64_t)std::ceil((stop - start) / step);
+    GGML_ASSERT(n_elements == steps);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    aclScalar* acl_start = aclCreateScalar(&start, aclDataType::ACL_FLOAT);
+    aclScalar* acl_end = aclCreateScalar(&stop, aclDataType::ACL_FLOAT);
+    aclScalar* acl_step = aclCreateScalar(&step, aclDataType::ACL_FLOAT);
+
+    ACL_CHECK(aclnnArangeGetWorkspaceSize(acl_start, acl_end, acl_step, acl_dst,
+                                          &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnArange(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(acl_start));
+    ACL_CHECK(aclDestroyScalar(acl_end));
+    ACL_CHECK(aclDestroyScalar(acl_step));
+}
+
+void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t n_elements = ggml_nelements(dst);
+    float start;
+    float stop;
+    float step;
+    memcpy(&start, (float*)dst->op_params + 0, sizeof(float));
+    memcpy(&stop, (float*)dst->op_params + 1, sizeof(float));
+    memcpy(&step, (float*)dst->op_params + 2, sizeof(float));
+
+    aclnn_arange(ctx, acl_dst, start, stop, step, n_elements);
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_sqr(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    dst->src[1] = dst->src[0];
+    ggml_cann_mul_div<aclnnMulGetWorkspaceSize, aclnnMul>(ctx, dst);
+}
+
+void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    float min;
+    float max;
+    memcpy(&min, dst->op_params, sizeof(float));
+    memcpy(&max, (float*)dst->op_params + 1, sizeof(float));
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    aclScalar* acl_min = aclCreateScalar(&min, aclDataType::ACL_FLOAT);
+    aclScalar* acl_max = aclCreateScalar(&max, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnClampGetWorkspaceSize(acl_src, acl_min, acl_max, acl_dst,
+                                         &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnClamp(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(acl_min));
+    ACL_CHECK(aclDestroyScalar(acl_max));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    // scale factor
+    float v;
+    memcpy(&v, dst->op_params, sizeof(float));
+
+    aclScalar* scale = aclCreateScalar(&v, aclDataType::ACL_FLOAT);
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnMulsGetWorkspaceSize(acl_src, scale, acl_dst, &workspaceSize,
+                                        &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnMuls(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(scale));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    enum ggml_sort_order order = (enum ggml_sort_order)dst->op_params[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+    ggml_cann_pool_alloc temp_buffer_allocator(
+        ctx.pool(), ggml_nelements(dst) * sizeof(int64_t));
+    void* buffer = temp_buffer_allocator.get();
+    aclTensor* tmp_tensor =
+        ggml_cann_create_tensor(buffer, ACL_INT64, ggml_type_size(dst->type),
+                                dst->ne, dst->nb, GGML_MAX_DIMS);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnArgsortGetWorkspaceSize(
+        acl_src, -1, (order == GGML_SORT_ORDER_DESC ? true : false), tmp_tensor,
+        &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnArgsort(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    workspaceSize = 0;
+    ACL_CHECK(aclnnCastGetWorkspaceSize(tmp_tensor,
+                                        ggml_cann_type_mapping(dst->type),
+                                        acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnCast(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(tmp_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    std::vector<int64_t> normData = {dst->ne[0]};
+    aclIntArray* norm = aclCreateIntArray(normData.data(), normData.size());
+    ACL_CHECK(aclnnLayerNormGetWorkspaceSize(acl_src, norm, nullptr, nullptr,
+                                             eps, acl_dst, nullptr, nullptr,
+                                             &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnLayerNorm(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(norm));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    const float eps = 1e-6f;  // TODO: make this a parameter
+    int n_groups = dst->op_params[0];
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    int64_t N = src->ne[3];
+    int64_t C = src->ne[2];
+    int64_t HxW = src->ne[1] * src->ne[0];
+
+    size_t type_size = ggml_type_size(src->type);
+    int64_t ne[] = {n_groups, N};
+    size_t nb[] = {type_size, type_size * n_groups};
+    size_t n_bytes = N * n_groups;
+
+    ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), n_bytes * 2);
+    void* buffer = temp_buffer_allocator.get();
+    aclTensor* acl_mean_out = ggml_cann_create_tensor(
+        buffer, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND);
+    aclTensor* acl_rstd_out = ggml_cann_create_tensor(
+        (char*)buffer + n_bytes, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND);
+
+    ACL_CHECK(aclnnGroupNormGetWorkspaceSize(
+        acl_src, nullptr, nullptr, N, C, HxW, n_groups, eps, acl_dst,
+        acl_mean_out, acl_rstd_out, &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnGroupNorm(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyTensor(acl_mean_out));
+    ACL_CHECK(aclDestroyTensor(acl_rstd_out));
+}
+
+void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+
+    size_t nb1 = ((int32_t*)dst->op_params)[0];
+    size_t nb2 = ((int32_t*)dst->op_params)[1];
+    size_t nb3 = ((int32_t*)dst->op_params)[2];
+    size_t offset = ((int32_t*)dst->op_params)[3];
+    bool inplace = (bool)((int32_t*)dst->op_params)[4];
+
+    size_t param_nb[] = {ggml_element_size(src0), nb1, nb2, nb3};
+
+    aclTensor* acl_dst = ggml_cann_create_tensor(
+        dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset);
+    aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
+
+    aclScalar* alpha = nullptr;
+    float alphaValue = 1.0f;
+    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    if (!inplace) {
+        size_t cpy_size = ggml_nbytes(dst);
+        ACL_CHECK(aclrtMemcpyAsync(dst->data, cpy_size, src0->data, cpy_size,
+                                   ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+        aclTensor* acl_src0 = ggml_cann_create_tensor(
+            src0, src1->ne, src0->nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset);
+        ACL_CHECK(aclnnAddGetWorkspaceSize(acl_src0, acl_src1, alpha, acl_dst,
+                                           &workspaceSize, &executor));
+        if (workspaceSize > 0) {
+            ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+            workspaceAddr = workspace_allocator.get();
+        }
+        ACL_CHECK(
+            aclnnAdd(workspaceAddr, workspaceSize, executor, ctx.stream()));
+        ACL_CHECK(aclDestroyTensor(acl_src0));
+    } else {
+        ACL_CHECK(aclnnInplaceAddGetWorkspaceSize(acl_dst, acl_src1, alpha,
+                                                  &workspaceSize, &executor));
+        if (workspaceSize > 0) {
+            ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+            workspaceAddr = workspace_allocator.get();
+        }
+        ACL_CHECK(aclnnInplaceAdd(workspaceAddr, workspaceSize, executor,
+                                  ctx.stream()));
+    }
+
+    ACL_CHECK(aclDestroyTensor(acl_src1));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+
+    GGML_ASSERT(dst->ne[0] == 1);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    int64_t reduce_dims_host[] = {3};
+    aclIntArray* reduce_dims = aclCreateIntArray(reduce_dims_host, 1);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnReduceSumGetWorkspaceSize(
+        acl_src, reduce_dims, true, ggml_cann_type_mapping(src->type), acl_dst,
+        &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnReduceSum(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx,
+                                  ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    aclTensor* acl_src =
+        ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+    aclTensor* acl_dst =
+        ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+
+    std::vector<int64_t> output_size{dst->ne[1], dst->ne[0]};
+    auto output_size_array = aclCreateIntArray(output_size.data(), 2);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnUpsampleNearest2dGetWorkspaceSize(
+        acl_src, output_size_array, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnUpsampleNearest2d(workspaceAddr, workspaceSize, executor,
+                                     ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(output_size_array));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Pads a tensor with a specified value along each dimension.
+ *
+ * This function performs padding of the source tensor `acl_src` and stores the
+ * result in the destination tensor `acl_dst`. The padding values for each
+ * dimension are specified in the `paddings` array.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor to be padded.
+ * @param acl_dst The destination tensor where the padded result will be stored.
+ * @param paddings An array specifying the padding values for each dimension.
+ * The size of the array should be twice the number of dimensions of the tensor.
+ * @param value The value to be used for padding. The default value is 0.0.
+ */
+static void aclnn_pad(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                      aclTensor* acl_dst, int64_t* paddings,
+                      float value = 0.0f) {
+    aclIntArray* acl_pad = aclCreateIntArray(paddings, GGML_MAX_DIMS * 2);
+    aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnConstantPadNdGetWorkspaceSize(
+        acl_src, acl_pad, acl_value, acl_dst, &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnConstantPadNd(workspaceAddr, workspaceSize, executor,
+                                 ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(acl_pad));
+    ACL_CHECK(aclDestroyScalar(acl_value));
+}
+
+void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    // padding: value in the array means how much distance will be padding.
+    // the position of elements in the array means which dirction to padding,
+    // each position means: [dim0.front, dim0.behind, dim1.front, dim1.behind,
+    //                       dim2.front, dim2.behind, dim3.front, dim3.behind]
+    int64_t paddings[] = {
+        0, dst->ne[0] - src->ne[0], 0, dst->ne[1] - src->ne[1],
+        0, dst->ne[2] - src->ne[2], 0, dst->ne[3] - src->ne[3]};
+    aclnn_pad(ctx, acl_src, acl_dst, paddings);
+
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+}
+
+/**
+ * @brief Performs 2D average pooling on the input tensor and stores the result
+ * in the destination tensor.
+ *
+ * This function performs average pooling on the source tensor and stores the
+ * result in the destination tensor. The pooling parameters (kernel size,
+ * strides, padding) are specified in the `op_params` of the destination tensor.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result will be stored. The source
+ * tensor is referenced by `dst->src[0]`.
+ */
+static void ggml_cann_avg_pool2d(ggml_backend_cann_context& ctx,
+                                 ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_src =
+        ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+    aclTensor* acl_dst =
+        ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+
+    const int32_t* opts = (const int32_t*)dst->op_params;
+    const int k0 = opts[1];
+    const int k1 = opts[2];
+    const int s0 = opts[3];
+    const int s1 = opts[4];
+    const int p0 = opts[5];
+    const int p1 = opts[6];
+
+    std::vector<int64_t> kernel_dims = {k1, k0};
+    std::vector<int64_t> stride_dims = {s1, s0};
+    std::vector<int64_t> padding_avg_dims = {p1, p0};  // (padH, padW)
+
+    auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2);
+    auto* strides = aclCreateIntArray(stride_dims.data(), 2);
+    auto* paddings_avg = aclCreateIntArray(padding_avg_dims.data(), 2);
+
+    bool ceil_mode = false;
+    bool count_include_pad = true;
+    int64_t divisor_override = 0;
+    int8_t cube_math_type = 0;
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnAvgPool2dGetWorkspaceSize(
+        acl_src, kernel_size, strides, paddings_avg, ceil_mode,
+        count_include_pad, divisor_override, cube_math_type, acl_dst,
+        &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+    ACL_CHECK(
+        aclnnAvgPool2d(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyIntArray(kernel_size));
+    ACL_CHECK(aclDestroyIntArray(strides));
+    ACL_CHECK(aclDestroyIntArray(paddings_avg));
+}
+
+/**
+ * @brief Performs 2D max pooling on the input tensor and stores the result in
+ * the destination tensor.
+ *
+ * This function performs max pooling on the source tensor and stores the result
+ * in the destination tensor. The pooling parameters (kernel size, strides,
+ * padding) are specified in the `op_params` of the destination tensor.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result will be stored. The source
+ * tensor is referenced by `dst->src[0]`.
+ */
+static void ggml_cann_max_pool2d(ggml_backend_cann_context& ctx,
+                                 ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_src =
+        ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+    aclTensor* acl_dst =
+        ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
+
+    const int32_t* opts = (const int32_t*)dst->op_params;
+    const int k0 = opts[1];
+    const int k1 = opts[2];
+    const int s0 = opts[3];
+    const int s1 = opts[4];
+    const int p0 = opts[5];
+    const int p1 = opts[6];
+
+    int64_t temp_ne[] = {src->ne[0] + p0 * 2, src->ne[1] + p1 * 2, src->ne[2],
+                         src->ne[3]};
+    size_t temp_nb[GGML_MAX_DIMS];
+
+    temp_nb[0] = ggml_element_size(src);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        temp_nb[i] = temp_nb[i - 1] * temp_ne[i - 1];
+    }
+
+    ggml_cann_pool_alloc temp_buffer_allocator(
+        ctx.pool(), ggml_nbytes(src) + p0 * 2 + p1 * 2 * src->nb[1]);
+    void* buffer = temp_buffer_allocator.get();
+    aclTensor* tmp_tensor = ggml_cann_create_tensor(
+        buffer, ACL_FLOAT, ggml_element_size(src), temp_ne, temp_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_NCHW);
+
+    // pad: see padding in ggml_cann_pad()
+    int64_t paddings[] = {p0, p0, p1, p1, 0, 0, 0, 0};
+    float value = -FLT_MAX;
+    aclnn_pad(ctx, acl_src, tmp_tensor, paddings, value);
+
+    // max_pool
+    std::vector<int64_t> kernel_dims = {k1, k0};
+    std::vector<int64_t> stride_dims = {s1, s0};
+    // padding_max_dims: [dim0_start, dim0_end, dim1_start, dim1_end]
+    std::vector<int64_t> padding_max_dims = {0, 0, 0, 0};
+    std::vector<int64_t> dilation_size = {1, 1};
+    auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2);
+    auto* strides = aclCreateIntArray(stride_dims.data(), 2);
+    auto* paddings_max = aclCreateIntArray(padding_max_dims.data(), 4);
+    auto* dilations = aclCreateIntArray(dilation_size.data(), 2);
+
+    bool ceil_mode = false;
+    int64_t auto_pads = 0;
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnMaxPoolGetWorkspaceSize(
+        tmp_tensor, kernel_size, strides, auto_pads, paddings_max, dilations,
+        ceil_mode, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnMaxPool(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyTensor(tmp_tensor));
+    ACL_CHECK(aclDestroyIntArray(kernel_size));
+    ACL_CHECK(aclDestroyIntArray(strides));
+    ACL_CHECK(aclDestroyIntArray(paddings_max));
+    ACL_CHECK(aclDestroyIntArray(dilations));
+}
+
+void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    const int32_t* opts = (const int32_t*)dst->op_params;
+    enum ggml_op_pool op = static_cast<ggml_op_pool>(opts[0]);
+    switch (op) {
+        case GGML_OP_POOL_AVG:
+            ggml_cann_avg_pool2d(ctx, dst);
+            break;
+        case GGML_OP_POOL_MAX:
+            ggml_cann_max_pool2d(ctx, dst);
+            break;
+        case GGML_OP_POOL_COUNT:
+            GGML_ASSERT(false);
+            break;
+    }
+}
+
+/**
+ * @brief Copies data from the source tensor to the destination tensor.
+ *
+ * This function copies data from the source tensor `acl_src` to the destination
+ * tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor from which data will be copied.
+ * @param acl_dst The destination tensor where the data will be copied to.
+ */
+static void cann_copy(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                      aclTensor* acl_dst) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceCopyGetWorkspaceSize(acl_dst, acl_src, &workspaceSize,
+                                               &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnInplaceCopy(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    ggml_cann_pool_alloc src_extra_allocator(ctx.pool(), sizeof(ggml_tensor));
+    ggml_cann_pool_alloc dst_extra_allocator(ctx.pool(), sizeof(ggml_tensor));
+    src->extra = src_extra_allocator.get();
+    dst->extra = dst_extra_allocator.get();
+    ACL_CHECK(aclrtMemcpyAsync(src->extra, sizeof(ggml_tensor), src,
+                               sizeof(ggml_tensor), ACL_MEMCPY_HOST_TO_DEVICE,
+                               ctx.stream()));
+    ACL_CHECK(aclrtMemcpyAsync(dst->extra, sizeof(ggml_tensor), dst,
+                               sizeof(ggml_tensor), ACL_MEMCPY_HOST_TO_DEVICE,
+                               ctx.stream()));
+
+    if ((dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32) &&
+        ggml_are_same_shape(src, dst)) {
+        cann_copy(ctx, acl_src, acl_dst);
+        ACL_CHECK(aclDestroyTensor(acl_src));
+        ACL_CHECK(aclDestroyTensor(acl_dst));
+        return;
+    }
+    // TODO: simplify
+    if (src->type == GGML_TYPE_F16) {
+        if (dst->type == GGML_TYPE_Q8_0) {
+            aclrtlaunch_ascendc_quantize_f16_q8_0(
+                24, ctx.stream(), src->data, dst->data,
+                ((ggml_tensor*)src->extra)->ne, ((ggml_tensor*)src->extra)->nb,
+                ((ggml_tensor*)dst->extra)->ne);
+            return;
+        }
+        if (dst->type == GGML_TYPE_F16) {
+            if (ggml_are_same_shape(src, dst)) {
+                cann_copy(ctx, acl_src, acl_dst);
+                ACL_CHECK(aclDestroyTensor(acl_src));
+                ACL_CHECK(aclDestroyTensor(acl_dst));
+                return;
+            }
+            if (ggml_is_contiguous(dst)) {
+                const size_t src_type_size = ggml_type_size(src->type);
+                if (src->nb[0] == src_type_size) {
+                    // src0 is contigous on first dimension, copy by rows
+                    int64_t rows_num = ggml_nrows(src);
+
+                    aclrtlaunch_ascendc_dup_by_rows_fp16(
+                        rows_num, ctx.stream(), src->data, dst->data,
+                        ((ggml_tensor*)src->extra)->ne,
+                        ((ggml_tensor*)src->extra)->nb,
+                        ((ggml_tensor*)dst->extra)->ne,
+                        ((ggml_tensor*)dst->extra)->nb);
+                    return;
+                }
+                GGML_ASSERT(false);
+            }
+            GGML_ASSERT(false);
+        }
+        if (dst->type == GGML_TYPE_F32) {
+            if (ggml_are_same_shape(src, dst)) {
+                cann_copy(ctx, acl_src, acl_dst);
+                ACL_CHECK(aclDestroyTensor(acl_src));
+                ACL_CHECK(aclDestroyTensor(acl_dst));
+                return;
+            }
+            if (ggml_is_contiguous(dst)) {
+                const size_t src_type_size = ggml_type_size(src->type);
+                if (src->nb[0] == src_type_size) {
+                    // src0 is contigous on first dimension, copy by rows
+                    int64_t rows_num = ggml_nrows(src);
+                    aclrtlaunch_ascendc_dup_by_rows_fp16_to_fp32(
+                        rows_num, ctx.stream(), src->data, dst->data,
+                        ((ggml_tensor*)src->extra)->ne,
+                        ((ggml_tensor*)src->extra)->nb,
+                        ((ggml_tensor*)dst->extra)->ne,
+                        ((ggml_tensor*)dst->extra)->nb);
+                    return;
+                }
+                GGML_ASSERT(false);
+            }
+            GGML_ASSERT(false);
+        }
+        // TODO
+        GGML_ASSERT(false);
+    } else if (src->type == GGML_TYPE_F32) {
+        // TODO: if (src0->type == dst->type && ne00 == ne0 && nb00 == type_size
+        //          && nb0 == type_size)
+        if (dst->type == GGML_TYPE_Q8_0) {
+            aclrtlaunch_ascendc_quantize_f32_q8_0(
+                24, ctx.stream(), src->data, dst->data,
+                ((ggml_tensor*)src->extra)->ne, ((ggml_tensor*)src->extra)->nb,
+                ((ggml_tensor*)dst->extra)->ne);
+            return;
+        }
+        if (dst->type == GGML_TYPE_F32) {
+            if (ggml_are_same_shape(src, dst)) {
+                cann_copy(ctx, acl_src, acl_dst);
+                ACL_CHECK(aclDestroyTensor(acl_src));
+                ACL_CHECK(aclDestroyTensor(acl_dst));
+                return;
+            }
+            if (ggml_is_contiguous(dst)) {
+                const size_t src_type_size = ggml_type_size(src->type);
+                if (src->nb[0] == src_type_size) {
+                    // src0 is contigous on first dimension, copy by rows
+                    int64_t rows_num = ggml_nrows(src);
+                    aclrtlaunch_ascendc_dup_by_rows_fp32(
+                        rows_num, ctx.stream(), src->data, dst->data,
+                        ((ggml_tensor*)src->extra)->ne,
+                        ((ggml_tensor*)src->extra)->nb,
+                        ((ggml_tensor*)dst->extra)->ne,
+                        ((ggml_tensor*)dst->extra)->nb);
+                    return;
+                }
+                GGML_ASSERT(false);
+            } else {
+                // TODO: dst not contiguous
+                GGML_ASSERT(false);
+            }
+        }
+        if (dst->type == GGML_TYPE_F16) {
+            if (ggml_are_same_shape(src, dst)) {
+                cann_copy(ctx, acl_src, acl_dst);
+                ACL_CHECK(aclDestroyTensor(acl_src));
+                ACL_CHECK(aclDestroyTensor(acl_dst));
+                return;
+            }
+            if (ggml_is_contiguous(dst)) {
+                const size_t src_type_size = ggml_type_size(src->type);
+                if (src->nb[0] == src_type_size) {
+                    // src0 is contigous on first dimension, copy by rows
+                    int64_t rows_num = ggml_nrows(src);
+                    aclrtlaunch_ascendc_dup_by_rows_fp32_to_fp16(
+                        rows_num, ctx.stream(), src->data, dst->data,
+                        ((ggml_tensor*)src->extra)->ne,
+                        ((ggml_tensor*)src->extra)->nb,
+                        ((ggml_tensor*)dst->extra)->ne,
+                        ((ggml_tensor*)dst->extra)->nb);
+                    return;
+                }
+                GGML_ASSERT(false);
+            }
+        }
+        // TODO
+        GGML_ASSERT(false);
+    } else {
+        if (ggml_are_same_shape(src, dst)) {
+            cann_copy(ctx, acl_src, acl_dst);
+            ACL_CHECK(aclDestroyTensor(acl_src));
+            ACL_CHECK(aclDestroyTensor(acl_dst));
+            return;
+        }
+        GGML_ASSERT(false);
+    }
+}
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+aclnnStatus aclnnRmsNormGetWorkspaceSize(const aclTensor* x,
+                                         const aclTensor* gamma, double epsilon,
+                                         const aclTensor* yOut,
+                                         const aclTensor* rstdOout,
+                                         uint64_t* workspaceSize,
+                                         aclOpExecutor** executor);
+aclnnStatus aclnnRmsNorm(void* workspace, uint64_t workspaceSize,
+                         aclOpExecutor* executor, aclrtStream stream);
+#ifdef __cplusplus
+}
+#endif
+
+/**
+ * @brief Creates an ACL tensor initialized with zeros using a provided buffer.
+ *
+ * This function initializes a tensor with zeros using the specified buffer and
+ * tensor parameters.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param buffer The buffer to be used for the tensor data.
+ * @param n_bytes The size of the buffer in bytes.
+ * @param ne An array specifying the extents (sizes) of each dimension of the
+ * tensor.
+ * @param dims The number of dimensions of the tensor.
+ * @param type The data type of the tensor.
+ * @param type_size The size of each element in the tensor data type.
+ * @return An ACL tensor initialized with zeros.
+ */
+static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer,
+                             size_t n_bytes, int64_t* ne, int64_t dims,
+                             aclDataType type, size_t type_size) {
+    size_t nb[GGML_MAX_DIMS];
+    nb[0] = type_size;
+    for (int i = 1; i < dims; i++) {
+        nb[i] = nb[i - 1] * ne[i - 1];
+    }
+
+    ACL_CHECK(aclrtMemsetAsync(buffer, n_bytes, 0, n_bytes, ctx.stream()));
+    aclTensor* zero =
+        ggml_cann_create_tensor(buffer, type, type_size, ne, nb, dims);
+    return zero;
+}
+
+/**
+ * @brief Creates an ACL tensor initialized with ones using a provided buffer.
+ *
+ * This function initializes a tensor with ones using the specified buffer and
+ * tensor parameters.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param buffer The buffer to be used for the tensor data.
+ * @param n_bytes The size of the buffer in bytes.
+ * @param ne An array specifying the extents (sizes) of each dimension of the
+ * tensor.
+ * @param dims The number of dimensions of the tensor.
+ * @param type The data type of the tensor.
+ * @param type_size The size of each element in the tensor data type.
+ * @param value The value to be used for initializing the tensor (default
+ * is 1.0).
+ * @return An ACL tensor initialized with ones.
+ */
+static aclTensor* aclnn_ones(ggml_backend_cann_context& ctx, void* buffer,
+                             size_t n_bytes, int64_t* ne, int64_t dims,
+                             aclDataType type, size_t type_size,
+                             float value = 1.0f) {
+    aclTensor* acl_tensor =
+        aclnn_zero(ctx, buffer, n_bytes, ne, dims, type, type_size);
+    float alpha_host = 1.0f;
+    aclScalar* alpha = aclCreateScalar(&alpha_host, aclDataType::ACL_FLOAT);
+    aclScalar* other = aclCreateScalar(&value, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceAddsGetWorkspaceSize(acl_tensor, other, alpha,
+                                               &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+    ACL_CHECK(
+        aclnnInplaceAdds(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    return acl_tensor;
+}
+
+void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    GGML_ASSERT(eps > 0.0f);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    size_t one_tensor_n_bytes = src->ne[0] * ggml_element_size(src);
+    ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes);
+
+    aclTensor* acl_gamma = aclnn_ones(
+        ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, 1,
+        ggml_cann_type_mapping(src->type), ggml_element_size(src));
+
+    size_t zero_tensor_n_bytes =
+        src->ne[1] * src->ne[2] * src->ne[3] * ggml_element_size(src);
+    ggml_cann_pool_alloc zero_tensor_allocator(ctx.pool(), zero_tensor_n_bytes);
+    aclTensor* acl_rstd =
+        aclnn_zero(ctx, zero_tensor_allocator.get(), zero_tensor_n_bytes,
+                   src->ne, GGML_MAX_DIMS, ggml_cann_type_mapping(src->type),
+                   ggml_element_size(src));
+
+    ACL_CHECK(aclnnRmsNormGetWorkspaceSize(
+        acl_src, acl_gamma, eps, acl_dst, acl_rstd, &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnRmsNorm(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyTensor(acl_gamma));
+    ACL_CHECK(aclDestroyTensor(acl_rstd));
+}
+
+// TODO: performace is low.
+void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst,
+                         float value) {
+    ggml_tensor* src = dst->src[0];
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    const int n_past = ((int32_t*)dst->op_params)[0];
+
+    size_t one_tensor_n_bytes = src->ne[0] * src->ne[1] * src->ne[2] *
+                                src->ne[3] * ggml_element_size(src);
+    ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes);
+
+    aclTensor* mask_tensor =
+        aclnn_ones(ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne,
+                   GGML_MAX_DIMS, ggml_cann_type_mapping(src->type),
+                   ggml_element_size(src), value);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceTriuGetWorkspaceSize(mask_tensor, n_past + 1,
+                                               &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnInplaceTriu(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclnnTrilGetWorkspaceSize(acl_src, n_past + 1, acl_dst,
+                                        &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnTril(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    aclScalar* alpha = nullptr;
+    float alphaValue = 1.0f;
+    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    ACL_CHECK(aclnnInplaceAddGetWorkspaceSize(acl_dst, mask_tensor, alpha,
+                                              &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+    ACL_CHECK(
+        aclnnInplaceAdd(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(alpha));
+    ACL_CHECK(aclDestroyTensor(mask_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Casts the data type of a source tensor to a destination tensor.
+ *
+ * This function casts the data type of the source tensor `acl_src` to the
+ * specified data type `cast_data_type` and stores the result in the destination
+ * tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose data type will be casted.
+ * @param acl_dst The destination tensor where the casted result will be stored.
+ * @param cast_data_type The target data type to which the source tensor will be
+ * casted.
+ */
+static void aclnn_cast(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                       aclTensor* acl_dst, aclDataType cast_data_type) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnCastGetWorkspaceSize(acl_src, cast_data_type, acl_dst,
+                                        &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnCast(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Permutes the dimensions of a tensor according to a specified order.
+ *
+ * This function permutes the dimensions of the source tensor `acl_src`
+ * according to the order specified in the `new_dim` array and stores the result
+ * in the destination tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose dimensions will be permuted.
+ * @param acl_dst The destination tensor where the permuted result will be
+ * stored.
+ * @param new_dim An array specifying the new order of dimensions for the
+ * tensor.
+ * @param dims The number of dimensions in the tensor.
+ */
+static void aclnn_permute(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                          aclTensor* acl_dst, int64_t* new_dim, uint64_t dims) {
+    aclIntArray* acl_dims = aclCreateIntArray(new_dim, dims);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnPermuteGetWorkspaceSize(acl_src, acl_dims, acl_dst,
+                                           &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnPermute(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(acl_dims));
+}
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+aclnnStatus aclnnIm2colGetWorkspaceSize(const aclTensor* self,
+                                        const aclIntArray* kernelSize,
+                                        const aclIntArray* dilation,
+                                        const aclIntArray* padding,
+                                        const aclIntArray* stride,
+                                        aclTensor* out, uint64_t* workspaceSize,
+                                        aclOpExecutor** executor);
+aclnnStatus aclnnIm2col(void* workspace, uint64_t workspaceSize,
+                        aclOpExecutor* executor, aclrtStream stream);
+#ifdef __cplusplus
+}
+#endif
+void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];  // kernel
+    ggml_tensor* src1 = dst->src[1];  // input
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
+
+    const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
+    const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
+    const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
+    const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
+    const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
+    const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    const int64_t N = is_2D ? ne13 : ne12;
+    const int64_t IC = is_2D ? ne12 : ne11;
+
+    const int64_t KH = is_2D ? ne01 : 1;
+    const int64_t KW = ne00;
+
+    const int64_t OH = is_2D ? ne2 : 1;
+    const int64_t OW = ne1;
+
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    // im2col: [N,C,H,W] -> [N, IC * KH * KW, OW * OH]
+    aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
+    int64_t tmp_im2col_ne[] = {OW * OH, IC * KH * KW, N};
+    size_t tmp_im2col_nb[GGML_MAX_DIMS - 1];
+
+    tmp_im2col_nb[0] = ggml_type_size(src1->type);
+    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+        tmp_im2col_nb[i] = tmp_im2col_nb[i - 1] * tmp_im2col_ne[i - 1];
+    }
+
+    // Calculate im2col.
+    // If dst is f16, tmp_buffer is f32, we need alloc src.typesize *
+    // dst.elemcount.
+    ggml_cann_pool_alloc im2col_allocator(
+        ctx.pool(), ggml_nelements(dst) * ggml_element_size(src1));
+    void* tmp_im2col_buffer = im2col_allocator.get();
+    aclTensor* tmp_im2col_tensor = ggml_cann_create_tensor(
+        tmp_im2col_buffer, ggml_cann_type_mapping(src1->type),
+        ggml_type_size(src1->type), tmp_im2col_ne, tmp_im2col_nb,
+        GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
+
+    std::vector<int64_t> kernel_dims = {KH, KW};
+    std::vector<int64_t> dilation_size = {d1, d0};
+    std::vector<int64_t> padding_dims = {p1, p0};
+    std::vector<int64_t> stride_dims = {s1, s0};
+    auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2);
+    auto* dilations = aclCreateIntArray(dilation_size.data(), 2);
+    auto* paddings = aclCreateIntArray(padding_dims.data(), 2);
+    auto* strides = aclCreateIntArray(stride_dims.data(), 2);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnIm2colGetWorkspaceSize(acl_src1, kernel_size, dilations,
+                                          paddings, strides, tmp_im2col_tensor,
+                                          &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnIm2col(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    // Cast if dst is f16.
+    aclTensor* tmp_cast_tensor = nullptr;
+    ggml_cann_pool_alloc tmp_cast_allocator(ctx.pool());
+    if (src1->type != dst->type) {
+        tmp_cast_allocator.alloc(ggml_nbytes(dst));
+        void* tmp_cast_buffer = tmp_cast_allocator.get();
+        size_t temp_cast_nb[GGML_MAX_DIMS - 1];
+        temp_cast_nb[0] = ggml_type_size(dst->type);
+        for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
+            temp_cast_nb[i] = temp_cast_nb[i - 1] * tmp_im2col_ne[i - 1];
+        }
+
+        tmp_cast_tensor = ggml_cann_create_tensor(
+            tmp_cast_buffer, ggml_cann_type_mapping(dst->type),
+            ggml_type_size(dst->type), tmp_im2col_ne, temp_cast_nb,
+            GGML_MAX_DIMS - 1, ACL_FORMAT_ND);
+        aclnn_cast(ctx, tmp_im2col_tensor, tmp_cast_tensor,
+                   ggml_cann_type_mapping(dst->type));
+    }
+
+    // Permute: [N, IC * KH * KW, OW * OH] -> [N, OW * OH, IC * KH * KW]
+    int64_t dst_ne[] = {dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3]};
+    size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[3]};
+    aclTensor* acl_dst =
+        ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1);
+
+    int64_t permute_dim[] = {0, 2, 1};
+    if (src1->type != dst->type) {
+        aclnn_permute(ctx, tmp_cast_tensor, acl_dst, permute_dim, 3);
+    } else {
+        aclnn_permute(ctx, tmp_im2col_tensor, acl_dst, permute_dim, 3);
+    }
+
+    // release
+    ACL_CHECK(aclDestroyTensor(acl_src1));
+    ACL_CHECK(aclDestroyTensor(tmp_im2col_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_cast_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyIntArray(kernel_size));
+    ACL_CHECK(aclDestroyIntArray(dilations));
+    ACL_CHECK(aclDestroyIntArray(paddings));
+    ACL_CHECK(aclDestroyIntArray(strides));
+}
+
+/**
+ * @brief Applies element-wise exponential function to the elements of a tensor.
+ *
+ * This function computes the exponential of each element in the source tensor
+ * `acl_src` and stores the result back into the same tensor.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_src }_i=e^{acl\_src_i}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The tensor on which the exponential function will be applied.
+ */
+static void aclnn_exp(ggml_backend_cann_context& ctx, aclTensor* acl_src) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(
+        aclnnInplaceExpGetWorkspaceSize(acl_src, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnInplaceExp(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Multiplies elements of a tensor by a scalar value, optionally
+ * in-place.
+ *
+ * This function multiplies each element of the source tensor `acl_src` by the
+ * scalar `scale` and stores the result in the destination tensor `acl_dst`. If
+ * `inplace` is true, `acl_dst` will not be used and the operation is performed
+ *  in-place on `acl_src`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst }_i=\text {acl_src }_i \times \text {scale}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose elements will be multiplied.
+ * @param scale The scalar value by which each element of `acl_src` will be
+ * multiplied.
+ * @param acl_dst The destination tensor where the result will be stored if
+ * `inplace` is false.
+ * @param inplace Flag indicating whether to perform the operation in-place on
+ * `acl_src`.
+ */
+static void aclnn_muls(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                       float scale, aclTensor* acl_dst, bool inplace) {
+    aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    if (inplace) {
+        ACL_CHECK(aclnnInplaceMulsGetWorkspaceSize(acl_src, acl_scale,
+                                                   &workspaceSize, &executor));
+        if (workspaceSize > 0) {
+            ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+            workspaceAddr = workspace_allocator.get();
+        }
+
+        ACL_CHECK(aclnnInplaceMuls(workspaceAddr, workspaceSize, executor,
+                                   ctx.stream()));
+    } else {
+        ACL_CHECK(aclnnMulsGetWorkspaceSize(acl_src, acl_scale, acl_dst,
+                                            &workspaceSize, &executor));
+        if (workspaceSize > 0) {
+            ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+            workspaceAddr = workspace_allocator.get();
+        }
+
+        ACL_CHECK(
+            aclnnMuls(workspaceAddr, workspaceSize, executor, ctx.stream()));
+    }
+
+    ACL_CHECK(aclDestroyScalar(acl_scale));
+}
+
+/**
+ * @brief Performs an in-place element-wise multiplication of two tensors.
+ *
+ * This function performs an element-wise multiplication of the tensors
+ * `acl_src` and `acl_other` and stores the result in `acl_src`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_src }_i=\text {acl_src }_i \times \text {acl_other }_i
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor where the multiplication result will be
+ * stored.
+ * @param acl_other The tensor whose elements will be multiplied with `acl_src`.
+ */
+static void aclnn_inplace_mul(ggml_backend_cann_context& ctx,
+                              aclTensor* acl_src, aclTensor* acl_other) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceMulGetWorkspaceSize(acl_src, acl_other,
+                                              &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnInplaceMul(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Performs element-wise multiplication of two tensors and stores the
+ * result in a destination tensor.
+ *
+ * This function performs element-wise multiplication of the tensors `acl_src`
+ * and `acl_other` and stores the result in the destination tensor `acl_dst`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst }_i=\text {acl_src }_i \times \text {acl_other }_i
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The first tensor for element-wise multiplication.
+ * @param acl_other The second tensor for element-wise multiplication.
+ * @param acl_dst The destination tensor where the result will be stored.
+ */
+static void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                      aclTensor* acl_other, aclTensor* acl_dst) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnMulGetWorkspaceSize(acl_src, acl_other, acl_dst,
+                                       &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnMul(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Applies element-wise cosine function to the elements of a tensor.
+ *
+ * This function computes the cosine of each element in the source tensor
+ * `acl_src` and stores the result in the destination tensor `acl_dst`. The
+ * operation is defined as: \f[ \text {acl_dst }_i=\cos \left(\text {acl_src
+ * }_i\right) \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor on which the cosine function will be
+ * applied.
+ * @param acl_dst The destination tensor where the cosine results will be
+ * stored.
+ */
+static void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                      aclTensor* acl_dst) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(
+        aclnnCosGetWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnCos(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Applies element-wise sine function to the elements of a tensor.
+ *
+ * This function computes the sine of each element in the source tensor
+ `acl_src`
+ * and stores the result in the destination tensor `acl_dst`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst }_i=\sin \left(\text {acl_src }_i\right)
+ * \f]
+
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor on which the sine function will be applied.
+ * @param acl_dst The destination tensor where the sine results will be stored.
+ */
+static void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                      aclTensor* acl_dst) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(
+        aclnnSinGetWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnSin(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx,
+                                  ggml_tensor* dst) {
+    const ggml_tensor* src = dst->src[0];
+
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int dim = dst->op_params[0];
+    const int max_period = dst->op_params[1];
+    int half = dim / 2;
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+
+    // arange: [0, ..., half)
+    float start = 0;
+    float stop = half;
+    float step = 1;
+    int64_t n_elements_arange = half;
+    int64_t tmp_arange_ne[] = {half};
+    size_t tmp_arange_nb[] = {sizeof(dst->type)};
+
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(), half * sizeof(dst->type));
+    void* tmp_arange_buffer = arange_allocator.get();
+    aclTensor* tmp_arange_tensor = ggml_cann_create_tensor(
+        tmp_arange_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_arange_ne, tmp_arange_nb,
+        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+    aclnn_arange(ctx, tmp_arange_tensor, start, stop, step, n_elements_arange);
+
+    // freq
+    float freq_param = -logf(max_period) / half;
+    bool inplace = true;
+    aclnn_muls(ctx, tmp_arange_tensor, freq_param, nullptr, inplace);
+    aclnn_exp(ctx, tmp_arange_tensor);
+
+    // permute: src [0,1,2,3]->[0,1,3,2]
+    int64_t tmp_permute_ne[] = {src->ne[1], src->ne[0], src->ne[2], src->ne[3]};
+    size_t tmp_permute_nb[GGML_MAX_DIMS];
+    tmp_permute_nb[0] = ggml_type_size(src->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1];
+    }
+
+    ggml_cann_pool_alloc permute_allocator(ctx.pool(), ggml_nbytes(src));
+    void* tmp_permute_buffer = permute_allocator.get();
+    aclTensor* tmp_permute_tenosr = ggml_cann_create_tensor(
+        tmp_permute_buffer, ggml_cann_type_mapping(src->type),
+        ggml_type_size(src->type), tmp_permute_ne, tmp_permute_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_ND);
+    int64_t permute_dim[] = {0, 1, 3, 2};
+    int64_t num_dims = 4;
+    aclnn_permute(ctx, acl_src, tmp_permute_tenosr, permute_dim, num_dims);
+
+    // timestep * freq
+    int64_t tmp_mul_ne[] = {src->ne[1] * half, src->ne[0], src->ne[2],
+                            src->ne[3]};
+    size_t tmp_mul_nb[GGML_MAX_DIMS];
+    tmp_mul_nb[0] = ggml_type_size(src->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        tmp_mul_nb[i] = tmp_mul_nb[i - 1] * tmp_mul_ne[i - 1];
+    }
+
+    int mul_nelements =
+        src->ne[1] * half * src->ne[0] * src->ne[2] * src->ne[3];
+
+    ggml_cann_pool_alloc mul_allocator(
+        ctx.pool(), mul_nelements * ggml_type_size(src->type));
+    void* tmp_mul_buffer = mul_allocator.get();
+    aclTensor* tmp_mul_tensor = ggml_cann_create_tensor(
+        tmp_mul_buffer, ggml_cann_type_mapping(src->type),
+        ggml_type_size(src->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS,
+        ACL_FORMAT_ND);
+    aclnn_mul(ctx, tmp_permute_tenosr, tmp_arange_tensor, tmp_mul_tensor);
+
+    // cos
+    ggml_cann_pool_alloc cos_allocator(
+        ctx.pool(), mul_nelements * ggml_type_size(src->type));
+    void* tmp_cos_buffer = cos_allocator.get();
+    aclTensor* tmp_cos_tensor = ggml_cann_create_tensor(
+        tmp_cos_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS,
+        ACL_FORMAT_ND);
+
+    aclnn_cos(ctx, tmp_mul_tensor, tmp_cos_tensor);
+
+    // sin
+    ggml_cann_pool_alloc sin_allocator(
+        ctx.pool(), mul_nelements * ggml_type_size(src->type));
+    void* tmp_sin_buffer = sin_allocator.get();
+    aclTensor* tmp_sin_tensor = ggml_cann_create_tensor(
+        tmp_sin_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS,
+        ACL_FORMAT_ND);
+
+    aclnn_sin(ctx, tmp_mul_tensor, tmp_sin_tensor);
+
+    // concat
+    int64_t concat_dim = 3;
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+    aclTensor* tensors[] = {tmp_cos_tensor, tmp_sin_tensor};
+    aclTensorList* tensorList = aclCreateTensorList(tensors, 2);
+    aclnn_concat(ctx, tensorList, acl_dst, concat_dim);
+
+    // release
+    // segmentation fault when delete both tensorList and his elements.
+    ACL_CHECK(aclDestroyTensorList(tensorList));
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(tmp_arange_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_permute_tenosr));
+    ACL_CHECK(aclDestroyTensor(tmp_mul_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Fills a tensor with a scalar value.
+ *
+ * This function fills the destination tensor `acl_dst` with the scalar value
+ * `scalar`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param scalar The scalar value used to fill the tensor.
+ * @param acl_dst The destination tensor to be filled with the scalar value.
+ */
+static void aclnn_fill_scalar(ggml_backend_cann_context& ctx, float scalar,
+                              aclTensor* acl_dst) {
+    auto acl_scalar = aclCreateScalar(&scalar, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceFillScalarGetWorkspaceSize(
+        acl_dst, acl_scalar, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnInplaceFillScalar(workspaceAddr, workspaceSize, executor,
+                                     ctx.stream()));
+    ACL_CHECK(aclDestroyScalar(acl_scalar));
+}
+
+/**
+ * @brief Raises each element of a tensor to the power of the corresponding
+ * element in another tensor.
+ *
+ * This function computes the element-wise power of the destination tensor
+ * `acl_dst` raised to the power of the exponent tensor `acl_exp`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst }_i=acl\_dst_i^{\text {acl_exp }_i}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_dst The destination tensor, which also serves as the base tensor.
+ * @param acl_exp The exponent tensor, each element of which is used to raise
+ * the corresponding element in the destination tensor.
+ */
+static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx,
+                                    aclTensor* acl_dst, aclTensor* acl_exp) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplacePowTensorTensorGetWorkspaceSize(
+        acl_dst, acl_exp, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnInplacePowTensorTensor(workspaceAddr, workspaceSize,
+                                          executor, ctx.stream()));
+}
+
+/**
+ * @brief   Applies the Alibi (Attention with Linear Biases) mechanism to the
+ * @details This function implements the Alibi mechanism, which introduces
+ *          learnable biases into the attention scores to simulate relative
+ *          position encoding without the need for explicit positional
+ *          embeddings.
+ *
+ * @param ctx          The backend CANN context for executing operations.
+ * @param acl_src      The source tensor representing the query or key.
+ * @param acl_position The position tensor containing relative positions.
+ * @param acl_dst      The destination tensor where the result will be stored.
+ * @param n_head       The number of attention heads.
+ * @param src_ne       The dimensions of the source tensor.
+ * @param src_nb0      The byte size of the first dimension of the source
+ tensor.
+ * @param max_bias     The maximum bias value used in the Alibi mechanism.
+ * @param dst          The destination tensor object for additional metadata.
+ *
+ * The function performs the following steps:
+ * 1. Calculates the logarithm floor of the number of heads to determine the
+      base for bias calculation.
+ * 2. Initializes arrays with arithmetic sequences and fills them with bias
+      values.
+ * 3. Computes the bias tensor based on the calculated biases and arithmetic
+      sequences.
+ * 4. Reshapes the bias tensor to match the dimensions of the input tensors.
+ * 5. Multiplies the position tensor by the bias tensor.
+ * 6. Adds the result of the multiplication to the source tensor to produce the
+      final output.
+ */
+static void aclnn_alibi(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                        aclTensor* acl_position, aclTensor* acl_dst,
+                        const int n_head, int64_t* src_ne, const size_t src_nb0,
+                        float max_bias, ggml_tensor* dst) {
+    const int64_t ne2_ne3 = src_ne[2] * src_ne[3];
+    GGML_ASSERT(src_nb0 == sizeof(float));
+    GGML_ASSERT(n_head == src_ne[2]);
+
+    const int n_heads_log2_floor = 1u << (uint32_t)floor(log2(n_head));
+
+    float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+    float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+
+    // init arange
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(),
+                                          ne2_ne3 * ggml_type_size(dst->type));
+    void* tmp_arange_buffer = arange_allocator.get();
+
+    // arange1: [1, ..., n_heads_log2_floor+1)
+    float start = 1;
+    float stop = n_heads_log2_floor + 1;
+    float step = 1;
+    int64_t n_elements_arange = n_heads_log2_floor;
+
+    int64_t tmp_arange1_ne[] = {n_heads_log2_floor};
+    size_t tmp_arange1_nb[] = {sizeof(dst->type)};
+    aclTensor* tmp_arange1_tensor = ggml_cann_create_tensor(
+        tmp_arange_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_arange1_ne, tmp_arange1_nb,
+        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+    aclnn_arange(ctx, tmp_arange1_tensor, start, stop, step, n_elements_arange);
+
+    aclTensor* tmp_arange2_tensor = nullptr;
+    if (n_heads_log2_floor < ne2_ne3) {
+        // arange2: [1, ..., 2 * (k - n_heads_log2_floor) + 1)
+        start = 1;
+        stop = 2 * (ne2_ne3 - n_heads_log2_floor) + 1;
+        step = 2;
+        n_elements_arange = ne2_ne3 - n_heads_log2_floor;
+        int64_t tmp_arange2_ne[] = {ne2_ne3 - n_heads_log2_floor};
+        size_t tmp_arange2_nb[] = {sizeof(dst->type)};
+
+        aclTensor* tmp_arange2_tensor = ggml_cann_create_tensor(
+            (char*)tmp_arange_buffer +
+                n_heads_log2_floor * ggml_type_size(dst->type),
+            ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+            tmp_arange2_ne, tmp_arange2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+        aclnn_arange(ctx, tmp_arange2_tensor, start, stop, step,
+                     n_elements_arange);
+    }
+
+    // init mk_base
+    ggml_cann_pool_alloc mk_base_allocator(ctx.pool(),
+                                           ne2_ne3 * ggml_type_size(dst->type));
+    void* tmp_mk_base_buffer = mk_base_allocator.get();
+    int64_t tmp_mk_base1_ne[] = {n_heads_log2_floor};
+    size_t tmp_mk_base1_nb[] = {sizeof(dst->type)};
+    aclTensor* tmp_mk_base1_tensor = ggml_cann_create_tensor(
+        tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mk_base1_ne, tmp_mk_base1_nb,
+        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+    aclnn_fill_scalar(ctx, m0, tmp_mk_base1_tensor);
+
+    aclTensor* tmp_mk_base2_tensor = nullptr;
+    if (n_heads_log2_floor < ne2_ne3) {
+        int64_t tmp_mk_base2_ne[] = {ne2_ne3 - n_heads_log2_floor};
+        size_t tmp_mk_base2_nb[] = {sizeof(dst->type)};
+        aclTensor* tmp_mk_base2_tensor = ggml_cann_create_tensor(
+            (char*)tmp_mk_base_buffer +
+                n_heads_log2_floor * ggml_type_size(dst->type),
+            ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type),
+            tmp_mk_base2_ne, tmp_mk_base2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+        aclnn_fill_scalar(ctx, m1, tmp_mk_base2_tensor);
+    }
+
+    // init mk
+    int64_t tmp_mk_base_ne[] = {ne2_ne3};
+    size_t tmp_mk_base_nb[] = {sizeof(dst->type)};
+    aclTensor* tmp_mk_base_tensor = ggml_cann_create_tensor(
+        tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mk_base_ne, tmp_mk_base_nb,
+        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+    aclTensor* tmp_arange_tensor = ggml_cann_create_tensor(
+        tmp_arange_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mk_base_ne, tmp_mk_base_nb,
+        GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+    aclnn_pow_tensor_tensor(ctx, tmp_mk_base_tensor, tmp_arange_tensor);
+
+    // reshape mk
+    int64_t tmp_mk_ne[] = {1, 1, src_ne[2], src_ne[3]};
+    size_t tmp_mk_nb[GGML_MAX_DIMS];
+    tmp_mk_nb[0] = ggml_type_size(dst->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        tmp_mk_nb[i] = tmp_mk_nb[i - 1] * tmp_mk_ne[i - 1];
+    }
+    aclTensor* tmp_mk_tensor = ggml_cann_create_tensor(
+        tmp_mk_base_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_mk_ne, tmp_mk_nb, GGML_MAX_DIMS,
+        ACL_FORMAT_ND);
+
+    // acl_position * mk
+    int64_t tmp_output_ne[] = {src_ne[0], src_ne[1], src_ne[2], src_ne[3]};
+    size_t tmp_output_nb[GGML_MAX_DIMS];
+    tmp_output_nb[0] = ggml_type_size(dst->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        tmp_output_nb[i] = tmp_output_nb[i - 1] * tmp_output_ne[i - 1];
+    }
+    ggml_cann_pool_alloc output_allocator(ctx.pool(), ggml_nbytes(dst));
+    void* tmp_output_buffer = output_allocator.get();
+    aclTensor* tmp_output_tensor = ggml_cann_create_tensor(
+        tmp_output_buffer, ggml_cann_type_mapping(dst->type),
+        ggml_type_size(dst->type), tmp_output_ne, tmp_output_nb, GGML_MAX_DIMS,
+        ACL_FORMAT_ND);
+    aclnn_mul(ctx, acl_position, tmp_mk_tensor, tmp_output_tensor);
+
+    // add
+    aclnn_add(ctx, tmp_output_tensor, acl_src, acl_dst);
+
+    ACL_CHECK(aclDestroyTensor(tmp_arange1_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_arange2_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_mk_base1_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_mk_base2_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_mk_base_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_arange_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_mk_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_output_tensor));
+}
+
+void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_cann_dup(ctx, dst);
+}
+
+/**
+ * @brief Performs element-wise addition of two tensors in place.
+ *
+ * This function adds the source tensor `acl_src` to the destination tensor
+ * `acl_dst` element-wise and stores the result in the destination tensor
+ * `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor to be added.
+ * @param acl_dst The destination tensor which will hold the result of the
+ * addition.
+ */
+static void aclnn_inplace_add(ggml_backend_cann_context& ctx,
+                              aclTensor* acl_src, aclTensor* acl_dst) {
+    aclScalar* alpha = nullptr;
+    float alphaValue = 1.0f;
+    alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceAddGetWorkspaceSize(acl_dst, acl_src, alpha,
+                                              &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnInplaceAdd(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyScalar(alpha));
+}
+
+/**
+ * @brief Applies the softmax function to a tensor along a specified dimension.
+ *
+ * This function computes the softmax of the source tensor `acl_src` along the
+ * specified dimension `dim` and stores the result in the destination tensor
+ * `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor on which the softmax function will be
+ * applied.
+ * @param dim The dimension along which the softmax function will be computed.
+ * @param acl_dst The destination tensor where the softmax results will be
+ * stored.
+ */
+static void aclnn_softmax(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                          int64_t dim, aclTensor* acl_dst) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnSoftmaxGetWorkspaceSize(acl_src, dim, acl_dst,
+                                           &workspaceSize, &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    aclrtStream stream = ctx.stream();
+    ACL_CHECK(aclnnSoftmax(workspaceAddr, workspaceSize, executor, stream));
+}
+
+void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];  // mask
+
+    aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    float scale = 1.0f;
+    float max_bias = 0.0f;
+
+    memcpy(&scale, (float*)dst->op_params + 0, sizeof(float));
+    memcpy(&max_bias, (float*)dst->op_params + 1, sizeof(float));
+
+    // input mul scale
+    aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT);
+
+    size_t n_bytes = ggml_nbytes(src0);
+    ggml_cann_pool_alloc mul_scale_allocator(ctx.pool(), n_bytes);
+    void* input_mul_scale_buffer = mul_scale_allocator.get();
+    aclTensor* acl_input_mul_scale_tensor = ggml_cann_create_tensor(
+        input_mul_scale_buffer, ACL_FLOAT, ggml_type_size(src0->type), src0->ne,
+        src0->nb, GGML_MAX_DIMS);
+
+    bool inplace = false;
+    aclnn_muls(ctx, acl_src0, scale, acl_input_mul_scale_tensor, inplace);
+
+    // mask
+    aclTensor* acl_src1_fp32_tensor = nullptr;
+    aclTensor* tmp_mask_tensor = nullptr;
+    ggml_cann_pool_alloc src1_fp32_allocator(ctx.pool());
+    if (src1) {
+        const bool use_f16 = src1->type == GGML_TYPE_F16;
+        if (use_f16) {
+            // cast to fp32
+            size_t n_bytes = ggml_nelements(src1) * sizeof(float_t);
+            size_t src1_fp32_nb[GGML_MAX_DIMS];
+            src1_fp32_nb[0] = sizeof(float_t);
+            for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                src1_fp32_nb[i] = src1_fp32_nb[i - 1] * src1->ne[i - 1];
+            }
+            src1_fp32_allocator.alloc(n_bytes);
+            void* src1_fp32_buffer = src1_fp32_allocator.get();
+            acl_src1_fp32_tensor = ggml_cann_create_tensor(
+                src1_fp32_buffer, ACL_FLOAT, sizeof(float), src1->ne,
+                src1_fp32_nb, GGML_MAX_DIMS);
+            aclTensor* acl_src1 = ggml_cann_create_tensor(src1);
+            aclnn_cast(ctx, acl_src1, acl_src1_fp32_tensor, ACL_FLOAT);
+
+            ACL_CHECK(aclDestroyTensor(acl_src1));
+        } else {
+            acl_src1_fp32_tensor = ggml_cann_create_tensor(src1);
+        }
+
+        // broadcast the mask across rows, only use ne11 of ne01 in mask
+        if (src1->ne[1] != src0->ne[1]) {
+            // mask shape: [1,1,ne11,ne10]
+            int64_t tmp_mask_ne[] = {src0->ne[0], src0->ne[1], 1, 1};
+            size_t tmp_mask_nb[GGML_MAX_DIMS];
+            tmp_mask_nb[0] = sizeof(float_t);
+            for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                tmp_mask_nb[i] = tmp_mask_nb[i - 1] * tmp_mask_ne[i - 1];
+            }
+            tmp_mask_tensor = ggml_cann_create_tensor(
+                src1->data, ACL_FLOAT, sizeof(float), tmp_mask_ne, tmp_mask_nb,
+                GGML_MAX_DIMS, ACL_FORMAT_ND);
+        }
+
+        // alibi
+        const int n_head = src0->ne[2];
+        const size_t src_nb0 = src0->nb[0];
+
+        n_bytes = ggml_nbytes(dst);
+        ggml_cann_pool_alloc output_allocator(ctx.pool(), n_bytes);
+        void* output_buffer = output_allocator.get();
+        aclTensor* alibi_output_tensor = ggml_cann_create_tensor(
+            output_buffer, ACL_FLOAT, ggml_type_size(dst->type), dst->ne,
+            dst->nb, GGML_MAX_DIMS);
+        if (max_bias <= 0.0f) {
+            // slope = 1.0
+            if (tmp_mask_tensor) {
+                aclnn_add(ctx, tmp_mask_tensor, acl_input_mul_scale_tensor,
+                          alibi_output_tensor);
+            } else {
+                aclnn_add(ctx, acl_src1_fp32_tensor, acl_input_mul_scale_tensor,
+                          alibi_output_tensor);
+            }
+        } else {
+            // slope != 1.0
+            if (tmp_mask_tensor) {
+                aclnn_alibi(ctx, acl_input_mul_scale_tensor, tmp_mask_tensor,
+                            alibi_output_tensor, n_head, src0->ne, src_nb0,
+                            max_bias, dst);
+            } else {
+                aclnn_alibi(ctx, acl_input_mul_scale_tensor,
+                            acl_src1_fp32_tensor, alibi_output_tensor, n_head,
+                            src0->ne, src_nb0, max_bias, dst);
+            }
+        }
+
+        // softmax
+        aclnn_softmax(ctx, alibi_output_tensor, 3, acl_dst);
+        ACL_CHECK(aclDestroyTensor(alibi_output_tensor));
+    } else {
+        aclnn_softmax(ctx, acl_input_mul_scale_tensor, 3, acl_dst);
+    }
+
+    ACL_CHECK(aclDestroyTensor(acl_src0));
+    ACL_CHECK(aclDestroyTensor(acl_src1_fp32_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+    ACL_CHECK(aclDestroyScalar(acl_scale));
+    ACL_CHECK(aclDestroyTensor(acl_input_mul_scale_tensor));
+    ACL_CHECK(aclDestroyTensor(tmp_mask_tensor));
+}
+
+void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+
+    ggml_cann_pool_alloc src0_extra_allocator(ctx.pool(), sizeof(ggml_tensor));
+    ggml_cann_pool_alloc src1_extra_allocator(ctx.pool(), sizeof(ggml_tensor));
+    ggml_cann_pool_alloc dst_extra_allocator(ctx.pool(), sizeof(ggml_tensor));
+    src0->extra = src0_extra_allocator.get();
+    src1->extra = src1_extra_allocator.get();
+    dst->extra = dst_extra_allocator.get();
+    ACL_CHECK(aclrtMemcpyAsync(src0->extra, sizeof(ggml_tensor), src0,
+                               sizeof(ggml_tensor), ACL_MEMCPY_HOST_TO_DEVICE,
+                               ctx.stream()));
+    ACL_CHECK(aclrtMemcpyAsync(src1->extra, sizeof(ggml_tensor), src1,
+                               sizeof(ggml_tensor), ACL_MEMCPY_HOST_TO_DEVICE,
+                               ctx.stream()));
+    ACL_CHECK(aclrtMemcpyAsync(dst->extra, sizeof(ggml_tensor), dst,
+                               sizeof(ggml_tensor), ACL_MEMCPY_HOST_TO_DEVICE,
+                               ctx.stream()));
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            aclrtlaunch_ascendc_get_row_f32(
+                24, ctx.stream(), src0->data, src1->data, dst->data,
+                ((ggml_tensor*)src0->extra)->ne,
+                ((ggml_tensor*)src0->extra)->nb,
+                ((ggml_tensor*)src1->extra)->ne,
+                ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne,
+                ((ggml_tensor*)dst->extra)->nb);
+            break;
+        case GGML_TYPE_F16:
+            aclrtlaunch_ascendc_get_row_f16(
+                24, ctx.stream(), src0->data, src1->data, dst->data,
+                ((ggml_tensor*)src0->extra)->ne,
+                ((ggml_tensor*)src0->extra)->nb,
+                ((ggml_tensor*)src1->extra)->ne,
+                ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne,
+                ((ggml_tensor*)dst->extra)->nb);
+            break;
+        case GGML_TYPE_Q4_0:
+            aclrtlaunch_ascendc_get_row_q4_0(
+                24, ctx.stream(), src0->data, src1->data, dst->data,
+                ((ggml_tensor*)src0->extra)->ne,
+                ((ggml_tensor*)src1->extra)->ne,
+                ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne,
+                ((ggml_tensor*)dst->extra)->nb);
+            break;
+        case GGML_TYPE_Q8_0:
+            aclrtlaunch_ascendc_get_row_q8_0(
+                24, ctx.stream(), src0->data, src1->data, dst->data,
+                ((ggml_tensor*)src0->extra)->ne,
+                ((ggml_tensor*)src1->extra)->ne,
+                ((ggml_tensor*)src1->extra)->nb, ((ggml_tensor*)dst->extra)->ne,
+                ((ggml_tensor*)dst->extra)->nb);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+}
+
+/**
+ * @brief Repeats elements of a tensor along a specified dimension.
+ *
+ * This function repeats each element of the source tensor `acl_src` a specified
+ * number of times (`repeats`) along the specified dimension `dim` and stores
+ * the result in the destination tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose elements will be repeated.
+ * @param acl_dst The destination tensor where the repeated elements will be
+ * stored.
+ * @param dim The dimension along which the elements will be repeated.
+ * @param repeats The number of times each element will be repeated.
+ * @param output_size The size of the output tensor.
+ */
+static void aclnn_repeat_interleave(ggml_backend_cann_context& ctx,
+                                    aclTensor* acl_src, aclTensor* acl_dst,
+                                    int64_t dim, int64_t repeats,
+                                    int64_t output_size) {
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnRepeatInterleaveIntWithDimGetWorkspaceSize(
+        acl_src, repeats, dim, output_size, acl_dst, &workspaceSize,
+        &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnRepeatInterleaveIntWithDim(workspaceAddr, workspaceSize,
+                                              executor, ctx.stream()));
+}
+
+/**
+ * @brief Performs matrix multiplication of two tensors.
+ *
+ * This function computes the matrix multiplication of the input tensor
+ * `acl_input` and the weight tensor `acl_weight`, and stores the result in the
+ * destination tensor `acl_dst`.
+ * The operation is defined as:
+ * \f[
+ *     \text {acl_dst}=\text {acl_input@acl_weight}
+ * \f]
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_input The input tensor for the matrix multiplication.
+ * @param acl_weight The weight tensor for the matrix multiplication.
+ * @param acl_dst The destination tensor where the result of the matrix
+ * multiplication will be stored.
+ */
+static void aclnn_mat_mul(ggml_backend_cann_context& ctx, aclTensor* acl_input,
+                          aclTensor* acl_weight, aclTensor* acl_dst) {
+    int8_t cube_math_type = 1;  // ALLOW_FP32_DOWN_PRECISION, when input is
+                                // fp32, atlas a2 will transpose it to HFLOAT32.
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnMatmulGetWorkspaceSize(acl_input, acl_weight, acl_dst,
+                                          cube_math_type, &workspaceSize,
+                                          &executor));
+
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(
+        aclnnMatmul(workspaceAddr, workspaceSize, executor, ctx.stream()));
+}
+
+/**
+ * @brief Performs matrix multiplication with floating-point precision on
+ * tensors using the CANN backend.
+ *
+ * This function performs matrix multiplication of the input tensor and the
+ * weight tensor, handling broadcasting and transposing as needed, and stores
+ * the result in the destination tensor `dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result of the matrix
+ * multiplication will be stored.
+ */
+static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
+                                 ggml_tensor* dst) {
+    ggml_tensor* weight = dst->src[0];  // weight
+    ggml_tensor* input = dst->src[1];   // input
+
+    // when weight ne2 or ne3 is 1, aclnnMatmulGetWorkspaceSize will auto
+    // broadcast, when weight ne2 or ne3 is not 1, weight need repeat.
+    BCAST_MUL_MAT_SHAPE(input, weight, dst);
+
+    // transpose weight: [1,2,3,4] -> [1,2,4,3]
+    int64_t transpose_ne[] = {bcast_weight_ne[1], bcast_weight_ne[0],
+                              bcast_weight_ne[2], bcast_weight_ne[3],
+                              bcast_weight_ne[4], bcast_weight_ne[5]};
+    size_t transpose_nb[] = {bcast_weight_nb[1], bcast_weight_nb[0],
+                             bcast_weight_nb[2], bcast_weight_nb[3],
+                             bcast_weight_nb[4], bcast_weight_nb[5]};
+
+    aclTensor* acl_weight_tensor =
+        ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, bcast_dims);
+    aclTensor* acl_input_tensor =
+        ggml_cann_create_tensor(input, BCAST_MUL_MAT_PARAM(input));
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst, BCAST_MUL_MAT_PARAM(dst));
+    aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst);
+
+    ACL_CHECK(aclDestroyTensor(acl_weight_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_input_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+/**
+ * @brief Performs matrix multiplication with quantized weights and
+ * floating-point inputs using the CANN backend.
+ *
+ * This function performs matrix multiplication of the input tensor `src1` and
+ * the weight tensor `src0`, handling broadcasting, transposing, and
+ * quantization as needed, and stores the result in the destination tensor
+ * `dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param dst The destination tensor where the result of the matrix
+ * multiplication will be stored.
+ */
+static void ggml_cann_mul_mat_q8_0(ggml_backend_cann_context& ctx,
+                                   ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];  // weight
+    ggml_tensor* src1 = dst->src[1];  // input
+
+    // The shape of the weight is NCHW. Matrix multiplication uses HW dims. HC
+    // is regarded as batch. weight need transpose.
+    int64_t weight_ne[] = {src0->ne[1], src0->ne[0]};
+    size_t weight_elem_size = sizeof(uint8_t);
+    size_t weight_nb[] = {weight_elem_size * src0->ne[0], weight_elem_size};
+    // size of one matrix is element_size * height * width.
+    size_t weight_stride = weight_elem_size * src0->ne[0] * src0->ne[1];
+    size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3];
+
+    // scale stored at the end of weight. Also need transpose.
+    int64_t scale_ne[] = {src0->ne[1], src0->ne[0] / QK8_0};
+    size_t scale_elem_size = sizeof(uint16_t);
+    size_t scale_nb[] = {src0->ne[0] / QK8_0 * scale_elem_size,
+                         scale_elem_size};
+    size_t scale_stride = scale_elem_size * src0->ne[0] * src0->ne[1] / QK8_0;
+    char* scale_offset = (char*)src0->data + weight_size;
+
+    // input
+    void* input_buffer;
+    size_t input_elem_size = sizeof(uint16_t);
+    int64_t input_ne[] = {src1->ne[0], src1->ne[1]};
+    size_t input_nb[] = {input_elem_size, input_elem_size * src1->ne[0]};
+    size_t input_stride = input_elem_size * src1->ne[0] * src1->ne[1];
+
+    if (src1->type != GGML_TYPE_F16) {
+        aclTensor* acl_src1_tensor = ggml_cann_create_tensor(src1);
+        ggml_cann_pool_alloc input_alloctor(
+            ctx.pool(), ggml_nelements(src1) * input_elem_size);
+        input_buffer = input_alloctor.get();
+
+        int64_t* input_cast_ne = src1->ne;
+        size_t input_cast_nb[GGML_MAX_DIMS];
+        input_cast_nb[0] = sizeof(uint16_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            input_cast_nb[i] = input_cast_nb[i - 1] * input_cast_ne[i - 1];
+        }
+
+        aclTensor* acl_input_tensor = ggml_cann_create_tensor(
+            input_buffer, ACL_FLOAT16, input_elem_size, input_cast_ne,
+            input_cast_nb, GGML_MAX_DIMS);
+        aclnn_cast(ctx, acl_src1_tensor, acl_input_tensor, ACL_FLOAT16);
+        ACL_CHECK(aclDestroyTensor(acl_input_tensor));
+        ACL_CHECK(aclDestroyTensor(acl_src1_tensor));
+    } else {
+        input_buffer = src1->data;
+    }
+
+    // output
+    size_t output_elem_size = sizeof(uint16_t);
+    int64_t output_ne[] = {dst->ne[0], dst->ne[1]};
+    size_t output_nb[] = {output_elem_size, output_elem_size * dst->ne[0]};
+    ggml_cann_pool_alloc output_alloctor(
+        ctx.pool(), ggml_nelements(dst) * output_elem_size);
+    void* output_buffer = output_alloctor.get();
+    size_t output_stride = output_elem_size * dst->ne[0] * dst->ne[1];
+
+    // aclnn
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    for (int64_t n1 = 0; n1 < src1->ne[3]; n1++) {
+        for (int64_t c1 = 0; c1 < src1->ne[2]; c1++) {
+            int64_t n0 = n1 / (src1->ne[3] / src0->ne[3]);
+            int64_t c0 = c1 / (src1->ne[2] / src0->ne[2]);
+
+            int64_t batch1 = n1 * src1->ne[2] + c1;
+            int64_t batch0 = n0 * src0->ne[2] + c0;
+
+            aclTensor* acl_input_tensor = ggml_cann_create_tensor(
+                (char*)input_buffer + batch1 * input_stride, ACL_FLOAT16,
+                input_elem_size, input_ne, input_nb, 2);
+            aclTensor* acl_weight_tensor = ggml_cann_create_tensor(
+                (char*)src0->data + batch0 * weight_stride, ACL_INT8,
+                weight_elem_size, weight_ne, weight_nb, 2);
+            aclTensor* acl_scale_tensor = ggml_cann_create_tensor(
+                scale_offset + batch0 * scale_stride, ACL_FLOAT16,
+                scale_elem_size, scale_ne, scale_nb, 2);
+            aclTensor* acl_output_tensor = ggml_cann_create_tensor(
+                (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16,
+                output_elem_size, output_ne, output_nb, 2);
+
+            ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize(
+                acl_input_tensor, acl_weight_tensor, acl_scale_tensor, nullptr,
+                nullptr, nullptr, nullptr, QK8_0, acl_output_tensor,
+                &workspaceSize, &executor));
+
+            if (workspaceSize > 0 && workspaceAddr == nullptr) {
+                ggml_cann_pool_alloc workspace_allocator(ctx.pool(),
+                                                         workspaceSize);
+                workspaceAddr = workspace_allocator.get();
+            }
+
+            ACL_CHECK(aclnnWeightQuantBatchMatmulV2(
+                workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+            ACL_CHECK(aclDestroyTensor(acl_input_tensor));
+            ACL_CHECK(aclDestroyTensor(acl_weight_tensor));
+            ACL_CHECK(aclDestroyTensor(acl_scale_tensor));
+            ACL_CHECK(aclDestroyTensor(acl_output_tensor));
+        }
+    }
+
+    // cast out
+    int64_t* output_cast_ne = dst->ne;
+    size_t output_cast_nb[GGML_MAX_DIMS];
+    output_cast_nb[0] = sizeof(uint16_t);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        output_cast_nb[i] = output_cast_nb[i - 1] * output_cast_ne[i - 1];
+    }
+
+    aclTensor* acl_output_tensor =
+        ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, output_elem_size,
+                                output_cast_ne, output_cast_nb, GGML_MAX_DIMS);
+    aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst);
+    aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, ACL_FLOAT);
+
+    ACL_CHECK(aclDestroyTensor(acl_output_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_dst_tensor));
+}
+
+void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    const enum ggml_type type = dst->src[0]->type;
+    switch (type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_F16:
+            ggml_cann_mat_mul_fp(ctx, dst);
+            break;
+        // case GGML_TYPE_Q4_0:
+        //     ggml_cann_mul_mat_q4_0(ctx, dst);
+        //     break;
+        case GGML_TYPE_Q8_0:
+            ggml_cann_mul_mat_q8_0(ctx, dst);
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+}
+
+/**
+ * @brief Rolls the elements of a tensor along a specified dimension.
+ *
+ * This function rolls the elements of the source tensor `acl_src` by the
+ * specified shifts `shifts` along the specified dimensions `dims`, and stores
+ * the result in the destination tensor `acl_dst`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor whose elements will be rolled.
+ * @param acl_dst The destination tensor where the rolled elements will be
+ * stored.
+ * @param shifts An array specifying the number of positions by which elements
+ * are shifted.
+ * @param dims An array specifying the dimensions along which elements are
+ * shifted.
+ */
+static void aclnn_roll(ggml_backend_cann_context& ctx, aclTensor* acl_src,
+                       aclTensor* acl_dst, int64_t* shifts, int64_t* dims) {
+    aclIntArray* acl_shifts = aclCreateIntArray(shifts, 1);
+    aclIntArray* acl_dims = aclCreateIntArray(dims, 1);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnRollGetWorkspaceSize(acl_src, acl_shifts, acl_dims, acl_dst,
+                                        &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnRoll(workspaceAddr, workspaceSize, executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(acl_shifts));
+    ACL_CHECK(aclDestroyIntArray(acl_dims));
+}
+
+/**
+ * @brief Fills specified positions of a tensor with a scalar value.
+ *
+ * This function fills the positions in the source tensor `acl_src` specified by
+ * `index` along the dimension `dim` with the scalar value `value`.
+ *
+ * @param ctx The context for the CANN backend operations.
+ * @param acl_src The source tensor where the positions will be filled.
+ * @param dim The dimension along which the positions are specified.
+ * @param index An array specifying the positions to be filled.
+ * @param index_num The number of positions specified in the index array.
+ * @param value The scalar value used to fill the specified positions.
+ */
+static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx,
+                                    aclTensor* acl_src, int64_t dim,
+                                    int64_t* index, int64_t index_num,
+                                    float value) {
+    aclIntArray* acl_index = aclCreateIntArray(index, index_num);
+    aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(aclnnInplaceIndexFillTensorGetWorkspaceSize(
+        acl_src, dim, acl_index, acl_value, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    ACL_CHECK(aclnnInplaceIndexFillTensor(workspaceAddr, workspaceSize,
+                                          executor, ctx.stream()));
+
+    ACL_CHECK(aclDestroyIntArray(acl_index));
+    ACL_CHECK(aclDestroyScalar(acl_value));
+}
+
+static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
+                             aclTensor* acl_cos_repeat_tensor,
+                             aclTensor* acl_sin_repeat_tensor,
+                             float theta_scale, bool is_neox) {
+    // int sin/cos cache, cache has different repeat method depond on
+    // @param.is_neox
+
+    ggml_tensor* src0 = dst->src[0];  // input
+    ggml_tensor* src1 = dst->src[1];  // position
+
+    // arange, [0,1,...,ne0/2]
+    int64_t arange_length = src0->ne[0] / 2;
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(),
+                                          arange_length * sizeof(float_t));
+    void* arange_buffer = arange_allocator.get();
+    int64_t arange_ne[] = {arange_length, 1, 1, 1};
+    size_t arange_nb[] = {sizeof(float_t), sizeof(float_t), sizeof(float_t),
+                          arange_length * sizeof(float_t)};
+
+    aclTensor* acl_arange_tensor =
+        ggml_cann_create_tensor(arange_buffer, ACL_FLOAT, sizeof(float_t),
+                                arange_ne, arange_nb, GGML_MAX_DIMS);
+    float start = 0;
+    float step = 1;
+    float stop = src0->ne[0] / 2;
+    float n_elements = src0->ne[0] / 2;
+    aclnn_arange(ctx, acl_arange_tensor, start, stop, step, n_elements);
+
+    // power
+    // aclnnPowScalarTensor(): @param self is tensor which should be scalar, so
+    // use aclnn_pow_tensor_tensor() until fixed. aclScalar* acl_theta_scale =
+    // aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT);
+    // aclnn_power_scalar_tensor(ctx, acl_theta_scale, acl_arange_tensor,
+    // acl_power_tensor);
+    ggml_cann_pool_alloc theta_scale_allocator(ctx.pool(),
+                                               arange_length * sizeof(float_t));
+    void* theta_scale_buffer = theta_scale_allocator.get();
+    aclTensor* acl_theta_scale_tensor = aclnn_ones(
+        ctx, theta_scale_buffer, arange_length * sizeof(float_t), arange_ne,
+        GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), theta_scale);
+    aclnn_pow_tensor_tensor(ctx, acl_theta_scale_tensor, acl_arange_tensor);
+
+    // position
+    GGML_ASSERT(src1->type == GGML_TYPE_I32);
+    int64_t position_length = src1->ne[0];
+    int64_t position_ne[] = {1, position_length, 1, 1};
+    size_t position_nb[] = {sizeof(int32_t), sizeof(int32_t),
+                            sizeof(int32_t) * position_length,
+                            sizeof(int32_t) * position_length};
+    aclTensor* acl_position_tensor = ggml_cann_create_tensor(
+        src1->data, ggml_cann_type_mapping(src1->type),
+        ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS);
+
+    // power * position
+    int64_t theta_length = arange_length * position_length;
+    ggml_cann_pool_alloc theta_allocator(ctx.pool(),
+                                         theta_length * sizeof(float_t));
+    void* theta_buffer = theta_allocator.get();
+    int64_t theta_ne[] = {arange_length, position_length, 1, 1};
+    size_t theta_nb[GGML_MAX_DIMS];
+    theta_nb[0] = sizeof(float_t);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1];
+    }
+    aclTensor* acl_theta_tensor =
+        ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t),
+                                theta_ne, theta_nb, GGML_MAX_DIMS);
+    aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
+              acl_theta_tensor);
+
+    // permute: [0,1,2,3]->[0,2,1,3]
+    int64_t permute_ne[] = {arange_length, 1, position_length, 1};
+    size_t permute_nb[GGML_MAX_DIMS];
+    permute_nb[0] = sizeof(float_t);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        permute_nb[i] = permute_nb[i - 1] * permute_ne[i - 1];
+    }
+    ggml_cann_pool_alloc permute_allocator(ctx.pool(),
+                                           theta_length * sizeof(float_t));
+    void* permute_buffer = permute_allocator.get();
+    aclTensor* acl_permute_tensor = ggml_cann_create_tensor(
+        permute_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_ND);
+    int64_t permute_dim[] = {0, 2, 1, 3};
+    int64_t num_dims = 4;
+    aclnn_permute(ctx, acl_theta_tensor, acl_permute_tensor, permute_dim,
+                  num_dims);
+
+    // sin/cos
+    ggml_cann_pool_alloc sin_allocator(ctx.pool(),
+                                       theta_length * sizeof(float_t));
+    void* sin_buffer = sin_allocator.get();
+    aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
+        sin_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_ND);
+    aclnn_sin(ctx, acl_permute_tensor, acl_sin_tensor);
+
+    ggml_cann_pool_alloc cos_allocator(ctx.pool(),
+                                       theta_length * sizeof(float_t));
+    void* cos_buffer = cos_allocator.get();
+    aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
+        cos_buffer, ACL_FLOAT, sizeof(float_t), permute_ne, permute_nb,
+        GGML_MAX_DIMS, ACL_FORMAT_ND);
+    aclnn_cos(ctx, acl_permute_tensor, acl_cos_tensor);
+
+    // repeat
+    if (is_neox) {
+        int64_t repeatsArray[] = {1, 1, 1, 2};
+        aclnn_repeat(ctx, acl_sin_tensor, acl_sin_repeat_tensor, repeatsArray);
+        aclnn_repeat(ctx, acl_cos_tensor, acl_cos_repeat_tensor, repeatsArray);
+    } else {
+        int64_t num_repeats = 2;
+        int64_t dim = 3;
+        int64_t output_size = arange_length * num_repeats;
+        aclnn_repeat_interleave(ctx, acl_sin_tensor, acl_sin_repeat_tensor, dim,
+                                num_repeats, output_size);
+        aclnn_repeat_interleave(ctx, acl_cos_tensor, acl_cos_repeat_tensor, dim,
+                                num_repeats, output_size);
+    }
+
+    // release
+    ACL_CHECK(aclDestroyTensor(acl_arange_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_theta_scale_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_position_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_theta_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_permute_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_sin_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_cos_tensor));
+}
+
+void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    // TODO: use ascendc
+    // Only test with LLAMA model.
+    ggml_tensor* src0 = dst->src[0];  // input
+    ggml_tensor* src2 = dst->src[2];  // freq_factors
+
+    // TODO: with freq_factors
+    GGML_ASSERT(src2 == NULL);
+
+    // param
+    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
+    // const int n_past     = ((int32_t *) dst->op_params)[0];
+    const int n_dims = ((int32_t*)dst->op_params)[1];
+    const int mode = ((int32_t*)dst->op_params)[2];
+    // const int n_ctx      = ((int32_t *) dst->op_params)[3];
+    const int n_ctx_orig = ((int32_t*)dst->op_params)[4];
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    memcpy(&freq_base, (int32_t*)dst->op_params + 5, sizeof(float));
+    memcpy(&freq_scale, (int32_t*)dst->op_params + 6, sizeof(float));
+    memcpy(&ext_factor, (int32_t*)dst->op_params + 7, sizeof(float));
+    memcpy(&attn_factor, (int32_t*)dst->op_params + 8, sizeof(float));
+    memcpy(&beta_fast, (int32_t*)dst->op_params + 9, sizeof(float));
+    memcpy(&beta_slow, (int32_t*)dst->op_params + 10, sizeof(float));
+
+    GGML_ASSERT(n_dims <= ne0);
+    GGML_ASSERT(n_dims % 2 == 0);
+
+    // TODO: ext_factor != 0
+    GGML_ASSERT(ext_factor == 0);
+    // TODO: freq_scale != 1
+    GGML_ASSERT(freq_scale == 1);
+
+    const float theta_scale = powf(freq_base, -2.0f / n_dims);
+
+    float corr_dims[2];
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast,
+                             beta_slow, corr_dims);
+
+    const bool is_neox = mode & 2;
+
+    // init cos/sin cache
+    ggml_cann_pool_alloc sin_allocator(
+        ctx.pool(), src0->ne[0] * src0->ne[2] * sizeof(float_t));
+    ggml_cann_pool_alloc cos_allocator(
+        ctx.pool(), src0->ne[0] * src0->ne[2] * sizeof(float_t));
+    void* sin_buffer = sin_allocator.get();
+    void* cos_buffer = cos_allocator.get();
+
+    int64_t sin_reshape_ne[4] = {src0->ne[0], 1, src0->ne[2], 1};
+    size_t sin_reshape_nb[GGML_MAX_DIMS];
+    sin_reshape_nb[0] = sizeof(float_t);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
+    }
+    aclTensor* acl_sin_reshape_tensor =
+        ggml_cann_create_tensor(sin_buffer, ACL_FLOAT, sizeof(float_t),
+                                sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
+    aclTensor* acl_cos_reshape_tensor =
+        ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t),
+                                sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
+    aclnn_cache_init(ctx, dst, acl_cos_reshape_tensor, acl_sin_reshape_tensor,
+                     theta_scale, is_neox);
+
+    // roll input
+    void* input_roll_buffer;
+    aclTensor* acl_minus_one_tensor;
+    void* minus_one_scale_buffer = nullptr;
+    ggml_cann_pool_alloc roll_allocator(ctx.pool(), ggml_nbytes(src0));
+    ggml_cann_pool_alloc minus_one_scale_allocator(
+        ctx.pool(), sizeof(float_t) * src0->ne[0]);
+    if (!is_neox) {
+        // roll input: [q0,q1,q2,q3,...] -> [q1,q0,q3,q2,...]
+        input_roll_buffer = roll_allocator.get();
+        int64_t input_roll_ne[4] = {2, src0->ne[1] * (src0->ne[0] / 2),
+                                    src0->ne[2], src0->ne[3]};
+        size_t input_roll_nb[GGML_MAX_DIMS];
+        input_roll_nb[0] = ggml_type_size(src0->type);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            input_roll_nb[i] = input_roll_nb[i - 1] * input_roll_ne[i - 1];
+        }
+        aclTensor* acl_input_roll_tensor = ggml_cann_create_tensor(
+            input_roll_buffer, ggml_cann_type_mapping(src0->type),
+            ggml_type_size(src0->type), input_roll_ne, input_roll_nb,
+            GGML_MAX_DIMS);
+        aclTensor* acl_input_tensor = ggml_cann_create_tensor(
+            src0->data, ggml_cann_type_mapping(src0->type),
+            ggml_type_size(src0->type), input_roll_ne, input_roll_nb,
+            GGML_MAX_DIMS);
+
+        int64_t shifts[] = {1};
+        int64_t dims[] = {3};
+        aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims);
+        ACL_CHECK(aclDestroyTensor(acl_input_roll_tensor));
+        ACL_CHECK(aclDestroyTensor(acl_input_tensor));
+
+        // init [-1, 1, -1, 1, ...]
+        minus_one_scale_buffer = minus_one_scale_allocator.get();
+
+        int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1};
+        size_t minus_one_nb[GGML_MAX_DIMS];
+        minus_one_nb[0] = sizeof(float_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1];
+        }
+        acl_minus_one_tensor = aclnn_ones(
+            ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0],
+            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1);
+        int64_t dim = 3;
+        int64_t* index = new int64_t[src0->ne[0]];
+        for (int i = 0; i < src0->ne[0]; i++) {
+            index[i] = i / 2 * 2;
+        }
+        int64_t index_num = src0->ne[0];
+        float value = -1;
+        aclnn_index_fill_tensor(ctx, acl_minus_one_tensor, dim, index,
+                                index_num, value);
+    } else {
+        // roll input: [q0,q1,q2,...] ->
+        // [q_half,q_half+1,...,q_end,q0,q1,...q_half-1]
+        input_roll_buffer = roll_allocator.get();
+        aclTensor* acl_input_roll_tensor = ggml_cann_create_tensor(
+            input_roll_buffer, ggml_cann_type_mapping(src0->type),
+            ggml_type_size(src0->type), src0->ne, src0->nb, GGML_MAX_DIMS);
+        aclTensor* acl_input_tensor = ggml_cann_create_tensor(src0);
+
+        int64_t shifts[] = {src0->ne[0] / 2};
+        int64_t dims[] = {3};
+        aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims);
+
+        ACL_CHECK(aclDestroyTensor(acl_input_roll_tensor));
+        ACL_CHECK(aclDestroyTensor(acl_input_tensor));
+
+        // init [-1, -1, -1, 1, 1，1，...]
+        minus_one_scale_buffer = minus_one_scale_allocator.get();
+
+        int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1};
+        size_t minus_one_nb[GGML_MAX_DIMS];
+        minus_one_nb[0] = sizeof(float_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1];
+        }
+        acl_minus_one_tensor = aclnn_ones(
+            ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0],
+            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1);
+        // -1 * first half
+        int64_t first_half_ne[4] = {src0->ne[0] / 2, 1, 1, 1};
+        size_t first_half_nb[GGML_MAX_DIMS];
+        first_half_nb[0] = sizeof(float_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            first_half_nb[i] = first_half_nb[i - 1] * first_half_ne[i - 1];
+        }
+        aclTensor* acl_first_half_tensor = ggml_cann_create_tensor(
+            minus_one_scale_buffer, ACL_FLOAT, sizeof(float_t), first_half_ne,
+            first_half_nb, GGML_MAX_DIMS);
+        bool inplace = true;
+        float scale = -1;
+        aclnn_muls(ctx, acl_first_half_tensor, scale, nullptr, inplace);
+        ACL_CHECK(aclDestroyTensor(acl_first_half_tensor));
+    }
+
+    // TODO: n_dims < ne0
+    GGML_ASSERT(n_dims == src0->ne[0]);
+
+    // input * scale
+    ggml_cann_pool_alloc roll_mul_scale_allocator(ctx.pool(),
+                                                  ggml_nbytes(src0));
+    void* input_roll_mul_scale_buffer = roll_mul_scale_allocator.get();
+    size_t input_nb[GGML_MAX_DIMS];
+    input_nb[0] = ggml_type_size(src0->type);
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        input_nb[i] = input_nb[i - 1] * src0->ne[i - 1];
+    }
+    aclTensor* acl_input_roll_mul_scale_tensor = ggml_cann_create_tensor(
+        input_roll_mul_scale_buffer, ggml_cann_type_mapping(src0->type),
+        ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS);
+    aclTensor* acl_input_roll_reshape_tensor = ggml_cann_create_tensor(
+        input_roll_buffer, ggml_cann_type_mapping(src0->type),
+        ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS);
+
+    aclnn_mul(ctx, acl_input_roll_reshape_tensor, acl_minus_one_tensor,
+              acl_input_roll_mul_scale_tensor);
+
+    // output
+    aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+    void* output_fp32_buffer;
+    if (src0->type == GGML_TYPE_F32) {
+        aclnn_inplace_mul(ctx, acl_src0, acl_cos_reshape_tensor);
+        aclnn_inplace_mul(ctx, acl_input_roll_mul_scale_tensor,
+                          acl_sin_reshape_tensor);
+        aclnn_add(ctx, acl_src0, acl_input_roll_mul_scale_tensor, acl_dst);
+        // TODO: ne0 != n_dims in mode2
+    } else if (src0->type == GGML_TYPE_F16) {
+        size_t input_fp32_nb[GGML_MAX_DIMS];
+        input_fp32_nb[0] = sizeof(float_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            input_fp32_nb[i] = input_fp32_nb[i - 1] * dst->ne[i - 1];
+        }
+        ggml_cann_pool_alloc fp32_allocator1(
+            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
+        void* input_fp32_buffer1 = fp32_allocator1.get();
+        aclTensor* input_fp32_tensor1 = ggml_cann_create_tensor(
+            input_fp32_buffer1, ACL_FLOAT, sizeof(float_t), dst->ne,
+            input_fp32_nb, GGML_MAX_DIMS);
+        ggml_cann_pool_alloc fp32_allocator2(
+            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
+        void* input_fp32_buffer2 = fp32_allocator2.get();
+        aclTensor* input_fp32_tensor2 = ggml_cann_create_tensor(
+            input_fp32_buffer2, ACL_FLOAT, sizeof(float_t), dst->ne,
+            input_fp32_nb, GGML_MAX_DIMS);
+
+        ggml_cann_pool_alloc fp32_allocator(
+            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
+        output_fp32_buffer = fp32_allocator.get();
+        aclTensor* output_fp32_tensor = ggml_cann_create_tensor(
+            output_fp32_buffer, ACL_FLOAT, sizeof(float_t), dst->ne,
+            input_fp32_nb, GGML_MAX_DIMS);
+        aclnn_mul(ctx, acl_src0, acl_cos_reshape_tensor, input_fp32_tensor1);
+        aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor,
+                  input_fp32_tensor2);
+        aclnn_add(ctx, input_fp32_tensor1, input_fp32_tensor2,
+                  output_fp32_tensor);
+        aclnn_cast(ctx, output_fp32_tensor, acl_dst, ACL_FLOAT16);
+
+        ACL_CHECK(aclDestroyTensor(input_fp32_tensor1));
+        ACL_CHECK(aclDestroyTensor(input_fp32_tensor2));
+        ACL_CHECK(aclDestroyTensor(output_fp32_tensor));
+    }
+
+    ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_minus_one_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_input_roll_mul_scale_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_input_roll_reshape_tensor));
+    ACL_CHECK(aclDestroyTensor(acl_src0));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
diff --git a/ggml/src/ggml-cann/aclnn_ops.h b/ggml/src/ggml-cann/aclnn_ops.h
new file mode 100644
index 00000000000000..680129c76de689
--- /dev/null
+++ b/ggml/src/ggml-cann/aclnn_ops.h
@@ -0,0 +1,592 @@
+#ifndef CANN_ACLNN_OPS
+#define CANN_ACLNN_OPS
+
+/**
+ * @file    acl_tensor
+ * @brief   This file contains related functions of ggml_tensor and acl_tensor.
+ *          Contains conversion from ggml_tensor to acl_tensor, broadcast and other
+ *          functions.
+ * @author  hipudding <huafengchun@gmail.com>
+ * @author  wangshuai09 <391746016@qq.com>
+ * @date    July 15, 2024
+ *
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#include <aclnnop/aclnn_add.h>
+#include <aclnnop/aclnn_arange.h>
+#include <aclnnop/aclnn_argsort.h>
+#include <aclnnop/aclnn_cat.h>
+#include <aclnnop/aclnn_clamp.h>
+#include <aclnnop/aclnn_div.h>
+#include <aclnnop/aclnn_gelu.h>
+#include <aclnnop/aclnn_hardsigmoid.h>
+#include <aclnnop/aclnn_hardswish.h>
+#include <aclnnop/aclnn_leaky_relu.h>
+#include <aclnnop/aclnn_mul.h>
+#include <aclnnop/aclnn_relu.h>
+#include <aclnnop/aclnn_silu.h>
+#include <aclnnop/aclnn_tanh.h>
+#include "acl_tensor.h"
+#include "common.h"
+
+/**
+ * @brief   Repeats a ggml tensor along each dimension to match the dimensions
+ *          of another tensor.
+ *
+ * @details This function repeats the elements of a source ggml tensor along
+ *          each dimension to create a destination tensor with the specified
+ *          dimensions. The operation is performed using the ACL backend and
+ *          executed asynchronously on the device.
+ *
+ * @param   ctx The CANN context used for operations.
+ * @param   dst The ggml tensor representing the destination, which op is
+ *              GGML_OP_REPEAT and specifies the desired dimensions.
+ */
+void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Adds two ggml tensors using the CANN backend.
+ *
+ * @details This function performs an element-wise addition of two tensors. In
+ *          case the tensors do not have the same shape, one or both tensors
+ *          will be broadcasted to match the shape of the other before the
+ *          addition is performed.The formula for the operation is given by:
+ *          \f[
+ *              \text{dst} = \text{acl_src0} + \alpha \cdot \text{acl_src1}
+ *          \f]
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The ggml tensor representing the destination, result of the
+ *            addition is stored at dst->data, and dst->op is `GGML_OP_ADD`
+ */
+void ggml_cann_add(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Applies the Leaky ReLU activation function to a tensor using the CANN
+ *          backend.
+ *
+ * @details This function computes the Leaky ReLU activation for each element of
+ *          the input tensor. The Leaky ReLU function allows a small gradient
+ *          when the unit is not active (i.e., when the input is negative). The
+ *          Leaky ReLU function is defined as:
+ *          \f[
+ *              \text{dst} = \max(0, src) + \text{negativeSlope} \cdot \min(0,
+ *               src)
+ *          \f]
+ *          `negativeSlope` is in dst->params.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the result of the Leaky ReLU
+ *            activation is stored, which op is `GGML_OP_LEAKY_RELU`
+ */
+void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief    Concatenates multiple tensors along a specified dimension using the
+ *           CANN backend.
+ *
+ * @param ctx        The CANN context used for operations.
+ * @param tensorList A pointer to the list of tensors to be concatenated.
+ * @param dst        The destination tensor where the result of the
+ *                   concatenation is stored. dst->op is `GGML_OP_CONCAT`.
+ * @param concat_dim The dimension along which the tensors are concatenated.
+ *
+ * @attention tensorList length should be 2 and the dimension using for concat
+ *            default to 1.
+ */
+void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Generates a sequence of evenly spaced values within a specified
+ *          interval for a ggml tensor using the CANN backend.
+ *
+ * @details This function creates a sequence of numbers over a specified i
+ *          nterval, starting from `start`, ending before `stop`, and
+ *          incrementing by `step`. The sequence is stored in the destination
+ *          tensor `dst`.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the generated sequence will be stored.
+ *            `start`, 'stop' and 'step' are in dst->op_params and dst->op is
+ *            `GGML_OP_ARANGE`.
+ */
+void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the square of the elements of a ggml tensor using the CANN
+ *          backend.
+ * @details The function sets the second source tensor of the destination
+ *          tensor `dst` to be equal to the first source tensor. This is
+ *          effectively squaring the elements since the multiplication becomes
+ *          `element * element`.
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the squared values will be stored，
+ *            which dst->op is `GGML_OP_SQR`.
+ */
+void ggml_cann_sqr(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Applies a clamp operation to the elements of a ggml tensor using the
+ *          CANN backend.
+ *
+ * @details This function clamps the elements of the input tensor `src` to a
+ *          specified range defined by `min` and `max` values. The result is
+ *          stored in the destination tensor `dst`. The operation is defined as:
+ *          \f[
+ *              y = \max(\min(x, max\_value), min\_value)
+ *           \f]
+ *          where `x` is an element of the input tensor, and `y` is the
+ *          corresponding element in the output tensor.
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the clamped values will be stored.
+ *            dst->op is `GGML_OP_CLAMP`, `min` and `max` value is in dst->params.
+ */
+void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Scales the elements of a ggml tensor by a constant factor using the
+ *          CANN backend.
+ *
+ * @details This function multiplies each element of the input tensor `src` by
+ *          a scaling factor `scale`, storing the result in the destination
+ *          tensor `dst`. The operation is defined as:
+ *          \f[
+ *             dst = src \times scale
+ *          \f]
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the scaled values will be stored.
+ *            dst->op is `GGML_OP_SCALE` and `scale` value is in dst->params.
+ */
+void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Sorts the elements of a ggml tensor and returns the indices that
+ *          would sort the tensor using the CANN backend.
+ *
+ * @details This function performs an argsort operation on the input tensor
+ *          `src`. It sorts the elements of `src` in either ascending or
+ *          descending order, depending on the `GGML_SORT_ORDER_DESC`,
+ *          and returns the indices that would sort the original tensor.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the sorted indices will be stored.
+ *            dst->op is `GGML_OP_ARGSORT`.
+ */
+void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the Layer Normalization for a ggml tensor using the CANN
+ *          backend.
+ *
+ * @details This function applies the Layer Normalization operation on the
+ *          input tensor `src` and stores the result in the destination tensor
+ *          `dst`. Layer Normalization normalizes the features at each sample in
+ *          a mini-batch independently. It is commonly used in neural networks
+ *          to normalize the activations of a layer by adjusting and scaling
+ *          the outputs.
+ *          The operation is defined as:
+ *          \f[
+ *              \text { out }=\frac{x-\mathrm{E}[x]}{\sqrt{\text{Var}[x]+eps}}
+ *          \f]
+ *          `Var` defaults dst->ne[0]. `eps` is in dst->params.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the normalized values will be stored.
+ * @attention `Var` defaults to dst->ne[0].
+ */
+void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief  Computes the Group Normalization for a ggml tensor using the CANN
+ *         backend.
+ *
+ * @brief  This function applies the Group Normalization operation on the input
+ *         tensor `src` and stores the result in the destination tensor `dst`.
+ *         Group Normalization divides the channels into groups and normalizes
+ *         the features within each group across spatial locations.
+ *         It is commonly used in convolutional neural networks to improve
+ *         training stability and performance.
+ *         The operation is defined as:
+ *         \f[
+ *             \text { out }=\frac{x-\mathrm{E}[x]}{\sqrt{\text{Var}[x]+eps}}
+ *         \f]
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the normalized values will be stored.
+ *            `n_groups` is in dst->params, which split C channel to `n_groups`.
+ *            dst->op is `GGML_OP_GROUP_NORM`.
+ *
+ * @attention eps defaults to 1e-6f.
+ */
+void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the accumulation of tensors using the CANN backend.
+ *
+ * @details This function performs an accumulation operation on two tensors.
+ *          Depending on the `inplace` flag, it either updates the destination
+ *          tensor `dst` in place by adding `alpha * src1` to it, or it creates
+ *          a new tensor as the result of `src0 + alpha * src1` and stores it in
+ *          `dst`.
+ *          The operation is defined as:
+ *          \f[
+ *               dst = src0 + alpha \times src1
+ *          \f]
+ *          if `inplace` is `true`, `src0` is equal to 'dst'.
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the accumulated values will be stored.
+ *            `inplace` is in dst->params, and dst->op is `GGML_OP_ACC`.
+ */
+void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the sum of elements along the last dimension of a ggml tensor
+ *          using the CANN backend.
+ *
+ * @details This function performs a reduction sum operation along the last
+ *          dimension of the input tensor `src`. The result of the sum is stored
+ *          in the destination tensor `dst`.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the reduced values will be stored。
+ *            dst->op is `GGML_OP_SUM_ROWS`.
+ *
+ * @attention `reduce_dims` defaults to 3, which means the last dimension.
+ */
+void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Upsamples a ggml tensor using nearest neighbor interpolation using
+ *          the CANN backend.
+ *
+ * @details This function performs upsampling of the input tensor `src` using
+ *          nearest neighbor interpolation. The upsampling is applied to the
+ *          height and width dimensions (last two dimensions) of the tensor. The
+ *          result is stored in the destination tensor `dst`, which must have
+ *          the appropriate dimensions for the upsampled output.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the upsampled values will be stored.
+ *            dst->op is `GGML_OP_UPSCALE`.
+ */
+void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx,
+                                  ggml_tensor* dst);
+
+/**
+ * @brief   Pads a ggml tensor to match the dimensions of the destination tensor
+ *          using the CANN backend.
+ *
+ * @details This function pads the input tensor `src` so that it matches the
+ *          dimensions of the destination tensor `dst`. The amount of padding
+ *          is calculated based on the difference in sizes between `src` and
+ *          `dst` along each dimension. The padded tensor is stored in `dst`.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor, which specifies the target dimensions for
+ *            padding. dst->op is `GGML_OP_PAD`.
+ */
+void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Executes a 2D pooling operation on a ggml tensor using the CANN
+ *          backend.
+ *
+ * @details This function dispatches the execution of a 2D pooling operation on
+ *          the input tensor `dst`. The type of pooling (average or max) is
+ *          determined by the `op` parameter, which is read from the operation
+ *          parameters of `dst`. The function supports average pooling
+ *          (`GGML_OP_POOL_AVG`) and max pooling (`GGML_OP_POOL_MAX`). If an
+ *          invalid operation is encountered, the function asserts a failure.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor on which the pooling operation is to be
+ *            performed. dst->op is `GGML_OP_POOL_2D`.
+ */
+void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Duplicates a ggml tensor using the CANN backend.
+ *
+ * @details This function duplicates the contents of the source tensor `src` to
+ *          the destination tensor `dst`. The function supports various tensor
+ *          types and configurations, including handling of extra data, type
+ *          conversions, and special cases for contiguous and non-contiguous
+ *          tensors.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the duplicated data will be stored.
+ *            dst->op is `GGML_OP_DUP`
+ *
+ * @attention Only support Fp16/FP32. Not support when src and dst have
+ *            different shape and dst is no-contiguous.
+ * @note:     This func need to simplify.
+ */
+void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the Root Mean Square (RMS) normalization of a ggml tensor
+ *          using the CANN backend.
+ *
+ * @details This function applies RMS normalization to the input tensor `src`
+ *          and stores the result in the destination tensor `dst`. RMS
+ *          normalization involves computing the root mean square of the input
+ *          tensor along a specified dimension and then dividing each element of
+ *          the tensor by this value, adjusted by a small epsilon value to
+ *          prevent division by zero.
+ *          The operation is defined as:
+ *          \f[
+ *               \text{RmsNorm}\left(x_i\right)=\frac{x_i}{\text{Rms}(\mathbf{x})} g_i,
+ *               \quad \text { where } \text{Rms}(\mathbf{x})=\sqrt{\frac{1}{n} \sum_{i=1}^n x_i^2+e p s}
+ *          \f]
+ *          `eps` is in dst->op_params.
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the normalized values will be stored.
+ *            dst->op is `GGML_OP_RMS_NORM`.
+ */
+void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Applies a diagonal mask to the tensor with a specified value.
+ *
+ * @details This function creates a mask tensor filled with ones, then applies
+ *          an upper triangular and lower triangular operation to it based on
+ *          the number of past elements specified. Afterward, it adds the masked
+ *          tensor to the destination tensor in-place.
+ *
+ * @param ctx The backend CANN context used for operations.
+ * @param dst The destination tensor where the result will be stored. dst->op is
+ *            `GGML_OP_DIAG_MASK`
+ * @param value The value to use for masking.
+ */
+void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, float value);
+
+/**
+ * @brief   Performs an image-to-column transformation on the input tensor.
+ *
+ * @details This function takes an input tensor and applies an image-to-column
+ *          operation, converting spatial dimensions into column-like
+ *          structures suitable for convolutional operations. It supports both
+ *          half-precision (F16) and single-precision (F32) floating-point data
+ *          types.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor that stores the result of the operation.
+ *            dst->op is `GGML_OP_IM2COL`.
+ */
+void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes time step embeddings using sine and cosine functions.
+ *
+ * @details This function calculates time step embeddings by applying sine and
+ *          cosine transformations to a given input tensor, which is typically
+ *          used in temporal models like diffusion models or transformers to
+ *          encode time information effectively.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor where the result of the embedding operation
+ *            will be stored. dst->op is `GGML_OP_TIMESTEP_EMBEDDING`.
+ */
+void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+// @see ggml_cann_dup.
+void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Computes the softmax activation with optional masking.
+ *
+ * @details This function computes the softmax activation over the input tensor,
+ *          optionally applying a mask and scaling factor. It supports both FP16
+ *          and FP32 data types and can handle masking by broadcasting the mask
+ *          across rows if necessary.
+ *          The function performs the following steps:
+ *          1. Multiplies the input tensor by a scale factor.
+ *          2. Optionally casts the mask tensor to FP32 if it is in FP16 format.
+ *          3. Broadcasts the mask tensor if its dimensions do not match the
+ *             input tensor's dimensions.
+ *          4. Adds the mask to the scaled input tensor.
+ *          5. Applies the softmax activation function along the specified
+ *             dimension.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor where the result will be stored. dst->op is
+ *            `GGML_OP_SOFTMAX`.
+ */
+void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Extracts specific rows from a tensor based on indices.
+ *
+ * @details This function retrieves rows from a source tensor src0 according to
+ *          the indices provided in another tensor src1 and stores the result in
+ *          a destination tensor (\p dst). It supports different data types
+ *          including F32, F16, Q4_0, and Q8_0.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor where the extracted rows will be stored.
+ *            dst->op is `GGML_OP_GET_ROWS`.
+ */
+void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief   Executes matrix multiplication for the given tensor.
+ *
+ * @details This function performs matrix multiplication on the source tensors
+ *          associated with the destination tensor. It supports matrix
+ *          multiplication F32, F16, and Q8_0.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor for storing the result of the matrix
+ *            multiplication. dst->op is `GGML_OP_MUL_MAT`.
+ */
+void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+/**
+ * @brief Applies Rotary Positional Embedding (RoPE) to the input tensor.
+ *
+ * @details This function implements the RoPE mechanism, which is a method to
+ *          encode positional information into sequence data, particularly
+ *          useful in transformer models. It supports both F32 and F16 data
+ *          types.
+ *
+ * @param ctx The backend CANN context for executing operations.
+ * @param dst The destination tensor where the RoPE-transformed data will be
+ *            stored. dst->op is `GGML_OP_ROPE`.
+ *
+ * @note The function currently does not support cases where the n_dims is less
+ *       than the input tensor's first dimension.
+ * @note The function currently does not support cases where the freq_factors is
+ *       not NULL.
+ * @note The function currently does not support cases where the ext_factor is
+ *       not equal 0.
+ * @note The function currently does not support cases where the freq_scale is
+ *       not equal 1.
+ */
+void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
+template <aclnnStatus getWorkspaceSize(const aclTensor*, const aclTensor*,
+                                       aclTensor*, uint64_t*, aclOpExecutor**),
+          aclnnStatus execute(void*, uint64_t, aclOpExecutor*, aclrtStream)>
+void ggml_cann_mul_div(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src0 = dst->src[0];
+    ggml_tensor* src1 = dst->src[1];
+    GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
+
+    aclTensor* acl_src0;
+    aclTensor* acl_src1;
+    aclTensor* acl_dst;
+
+    // Need bcast
+    if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) {
+        BCAST_SHAPE(src0, src1)
+        acl_src0 = ggml_cann_create_tensor(src0, BCAST_PARAM(src0));
+        acl_src1 = ggml_cann_create_tensor(src1, BCAST_PARAM(src1));
+        acl_dst = ggml_cann_create_tensor(dst, BCAST_PARAM(src0));
+    } else {
+        acl_src0 = ggml_cann_create_tensor(src0);
+        acl_src1 = ggml_cann_create_tensor(src1);
+        acl_dst = ggml_cann_create_tensor(dst);
+    }
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(getWorkspaceSize(acl_src0, acl_src1, acl_dst, &workspaceSize,
+                               &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    aclrtStream main_stream = ctx.stream();
+    ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
+
+    ACL_CHECK(aclDestroyTensor(acl_src0));
+    ACL_CHECK(aclDestroyTensor(acl_src1));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+// Activation functions template.
+template <aclnnStatus getWorkspaceSize(const aclTensor*, aclTensor*, uint64_t*,
+                                       aclOpExecutor**),
+          aclnnStatus execute(void*, uint64_t, aclOpExecutor*,
+                              const aclrtStream)>
+void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(getWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    aclrtStream main_stream = ctx.stream();
+    ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+// Activation functions template for const aclTensors.
+template <aclnnStatus getWorkspaceSize(const aclTensor*, const aclTensor*,
+                                       uint64_t*, aclOpExecutor**),
+          aclnnStatus execute(void*, uint64_t, aclOpExecutor*,
+                              const aclrtStream)>
+void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    ggml_tensor* src = dst->src[0];
+
+    GGML_ASSERT(src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    aclTensor* acl_src = ggml_cann_create_tensor(src);
+    aclTensor* acl_dst = ggml_cann_create_tensor(dst);
+
+    uint64_t workspaceSize = 0;
+    aclOpExecutor* executor;
+    void* workspaceAddr = nullptr;
+
+    ACL_CHECK(getWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
+    if (workspaceSize > 0) {
+        ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
+        workspaceAddr = workspace_allocator.get();
+    }
+
+    aclrtStream main_stream = ctx.stream();
+    ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
+
+    ACL_CHECK(aclDestroyTensor(acl_src));
+    ACL_CHECK(aclDestroyTensor(acl_dst));
+}
+
+#endif  // CANN_ACLNN_OPS
diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
new file mode 100644
index 00000000000000..e6a5701075f02c
--- /dev/null
+++ b/ggml/src/ggml-cann/common.h
@@ -0,0 +1,282 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#ifndef CANN_COMMON_H
+#define CANN_COMMON_H
+
+#include <acl/acl.h>
+
+#include <cstdio>
+#include <iostream>
+#include <map>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "../include/ggml-cann.h"
+#include "../include/ggml.h"
+
+#define MATRIX_ROW_PADDING 512
+#define GGML_CANN_MAX_STREAMS 8
+
+/**
+ * @brief Handles CANN-related errors by printing an error message and
+ *        terminating the program.
+ * @param stmt The statement that caused the error.
+ * @param func The function in which the error occurred.
+ * @param file The file in which the error occurred.
+ * @param line The line number at which the error occurred.
+ * @param msg The error message.
+ */
+[[noreturn]] void ggml_cann_error(const char* stmt, const char* func,
+                                  const char* file, int line, const char* msg);
+
+/**
+ * @brief Checks the result of a CANN function call and invokes the error
+ *        handler if the call fails.
+ * @param stmt The CANN function call to check.
+ * @param success The success code that indicates the call was successful.
+ * @param error_fn The function to call to retrieve the error message.
+ */
+#define ACL_CHECK_GEN(stmt, success, error_fn)                                \
+    do {                                                                      \
+        int err_code = (stmt);                                                \
+        if (err_code != (success)) {                                          \
+            ggml_cann_error(#stmt, __func__, __FILE__, __LINE__, error_fn()); \
+        }                                                                     \
+    } while (0);
+
+#define ACL_CHECK(stmt) ACL_CHECK_GEN(stmt, 0, aclGetRecentErrMsg)
+
+/**
+ * @brief Contains information about CANN devices.
+ */
+struct ggml_cann_device_info {
+    /**
+     * @brief Number of CANN devices available.
+     */
+    int32_t device_count;
+
+    /**
+     * @brief Information about a single CANN device.
+     */
+    struct cann_device_info {
+        int cc;                 /**< Compute capability.                   */
+        size_t smpb;            /**< Maximum shared memory per block.      */
+        bool vmm;               /**< Virtual memory support.               */
+        size_t vmm_granularity; /**< Granularity of virtual memory.        */
+        size_t total_vram;      /**< Total video RAM available on the device. */
+    };
+
+    cann_device_info devices[GGML_CANN_MAX_DEVICES] =
+        {}; /**< Array of CANN device information. */
+};
+
+const ggml_cann_device_info& ggml_cann_info();
+
+void ggml_cann_set_device(int32_t device);
+int32_t ggml_cann_get_device();
+
+/**
+ * @brief Abstract base class for memory pools used by CANN.
+ */
+struct ggml_cann_pool {
+    /**
+     * @brief Virtual destructor for the memory pool.
+     */
+    virtual ~ggml_cann_pool() = default;
+
+    /**
+     * @brief Allocates memory from the pool.
+     *
+     * @param size         The size of the memory block to allocate.
+     * @param actual_size  Pointer to a variable where the actual allocated size
+     *                     will be stored.
+     * @return             Pointer to the allocated memory block.
+     */
+    virtual void* alloc(size_t size, size_t* actual_size) = 0;
+
+    /**
+     * @brief Frees a previously allocated memory block.
+     *
+     * @param ptr   Pointer to the memory block to free.
+     * @param size  Size of the memory block to free.
+     * @note Note that all CANN opertors are running async. Make sure memory is
+     *       still avaiable before this operator finished.
+     */
+    virtual void free(void* ptr, size_t size) = 0;
+};
+
+/**
+ * @brief RAII wrapper for managing memory allocations from a CANN memory pool.
+ */
+struct ggml_cann_pool_alloc {
+    ggml_cann_pool* pool = nullptr; /**< Pointer to the memory pool. */
+    void* ptr = nullptr;    /**< Pointer to the allocated memory block. */
+    size_t actual_size = 0; /**< Actual size of the allocated memory block. */
+
+    /**
+     * @brief Default constructor.
+     */
+    ggml_cann_pool_alloc() = default;
+
+    /**
+     * @brief Constructor that initializes the memory pool.
+     * @param pool Reference to the memory pool.
+     */
+    explicit ggml_cann_pool_alloc(ggml_cann_pool& pool) : pool(&pool) {}
+
+    /**
+     * @brief Constructor that initializes the memory pool and allocates memory.
+     * @param pool Reference to the memory pool.
+     * @param size Size of the memory block to allocate.
+     */
+    ggml_cann_pool_alloc(ggml_cann_pool& pool, size_t size) : pool(&pool) {
+        alloc(size);
+    }
+
+    /**
+     * @brief Destructor that frees the allocated memory block.
+     */
+    ~ggml_cann_pool_alloc() {
+        if (ptr != nullptr) {
+            pool->free(ptr, actual_size);
+        }
+    }
+
+    /**
+     * @brief Allocates memory from the pool.
+     * @param size Size of the memory block to allocate.
+     * @return Pointer to the allocated memory block.
+     */
+    void* alloc(size_t size) {
+        GGML_ASSERT(pool != nullptr);
+        GGML_ASSERT(ptr == nullptr);
+        ptr = pool->alloc(size, &this->actual_size);
+        return ptr;
+    }
+
+    /**
+     * @brief Allocates memory from a specific memory pool.
+     * @param pool Reference to the memory pool.
+     * @param size Size of the memory block to allocate.
+     * @return Pointer to the allocated memory block.
+     */
+    void* alloc(ggml_cann_pool& pool, size_t size) {
+        this->pool = &pool;
+        return alloc(size);
+    }
+
+    /**
+     * @brief Gets the pointer to the allocated memory block.
+     * @return Pointer to the allocated memory block.
+     */
+    void* get() { return ptr; }
+
+    // Deleted copy constructor
+    ggml_cann_pool_alloc(const ggml_cann_pool_alloc&) = delete;
+
+    // Deleted move constructor
+    ggml_cann_pool_alloc(ggml_cann_pool_alloc&&) = delete;
+
+    // Deleted copy assignment operator
+    ggml_cann_pool_alloc& operator=(const ggml_cann_pool_alloc&) = delete;
+
+    // Deleted move assignment operator
+    ggml_cann_pool_alloc& operator=(ggml_cann_pool_alloc&&) = delete;
+};
+
+/**
+ * @brief Context for managing CANN backend operations.
+ */
+struct ggml_backend_cann_context {
+    int32_t device;                  /**< Device ID. */
+    std::string name;                /**< Name of the device. */
+    aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
+
+    aclrtStream streams[GGML_CANN_MAX_STREAMS] = {
+        {nullptr}}; /**< Array of streams for the device. */
+
+    /**
+     * @brief Constructor for initializing the context with a given device.
+     * @param device Device ID.
+     */
+    explicit ggml_backend_cann_context(int device)
+        : device(device), name("CANN" + std::to_string(device)) {}
+
+    /**
+     * @brief Destructor for cleaning up resources.
+     */
+    ~ggml_backend_cann_context() {
+        if (copy_event != nullptr) {
+            ACL_CHECK(aclrtDestroyEvent(copy_event));
+        }
+        for (int i = 0; i < GGML_CANN_MAX_STREAMS; ++i) {
+            if (streams[i] != nullptr) {
+                ACL_CHECK(aclrtDestroyStream(streams[i]));
+            }
+        }
+    }
+
+    /**
+     * @brief Get or create a stream for a given index.
+     * @param stream Index of the stream.
+     * @return The stream corresponding to the given index.
+     */
+    aclrtStream stream(int stream) {
+        if (streams[stream] == nullptr) {
+            ggml_cann_set_device(device);
+            ACL_CHECK(aclrtCreateStream(&streams[stream]));
+        }
+        return streams[stream];
+    }
+
+    /**
+     * @brief Get or create the default stream (index 0).
+     * @return The default stream.
+     */
+    aclrtStream stream() { return stream(0); }
+
+    // TODO: each stream should have a memory pool.
+    std::unique_ptr<ggml_cann_pool>
+        mem_pool; /**< Memory pool for the device. */
+
+    /**
+     * @brief Create a new memory pool for a given device.
+     * @param device Device ID.
+     * @return A unique pointer to the new memory pool.
+     */
+    static std::unique_ptr<ggml_cann_pool> new_pool_for_device(int device);
+
+    /**
+     * @brief Get or create the memory pool for the context.
+     * @return Reference to the memory pool.
+     */
+    ggml_cann_pool& pool() {
+        if (mem_pool == nullptr) {
+            mem_pool = new_pool_for_device(device);
+        }
+        return *mem_pool;
+    }
+};
+
+#endif  // CANN_COMMON_H
diff --git a/ggml/src/ggml-cann/kernels/CMakeLists.txt b/ggml/src/ggml-cann/kernels/CMakeLists.txt
new file mode 100644
index 00000000000000..f12a4d43f2df6f
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/CMakeLists.txt
@@ -0,0 +1,32 @@
+if (NOT SOC_TYPE)
+    set (SOC_TYPE "Ascend910B3")
+endif()
+
+file(GLOB SRC_FILES
+    get_row_f32.cpp
+    get_row_f16.cpp
+    get_row_q4_0.cpp
+    get_row_q8_0.cpp
+    quantize_f32_q8_0.cpp
+    quantize_f16_q8_0.cpp
+    dup.cpp
+)
+
+string(TOLOWER ${SOC_TYPE} SOC_VERSION)
+set(ASCEND_CANN_PACKAGE_PATH ${CANN_INSTALL_DIR})
+set(RUN_MODE "npu" CACHE STRING "run mode: npu/sim")
+
+if(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/compiler/tikcpp/ascendc_kernel_cmake)
+    set(ASCENDC_CMAKE_DIR ${ASCEND_CANN_PACKAGE_PATH}/compiler/tikcpp/ascendc_kernel_cmake)
+elseif(EXISTS ${ASCEND_CANN_PACKAGE_PATH}/ascendc_devkit/tikcpp/samples/cmake)
+    set(ASCENDC_CMAKE_DIR ${ASCEND_CANN_PACKAGE_PATH}/ascendc_devkit/tikcpp/samples/cmake)
+else()
+    message(FATAL_ERROR "ascendc_kernel_cmake does not exist, please check whether the compiler package is installed.")
+endif()
+include(${ASCENDC_CMAKE_DIR}/ascendc.cmake)
+
+ascendc_library(ascendc_kernels STATIC
+    ${SRC_FILES}
+)
+
+#ascendc_compile_definitions(ascendc_kernels PRIVATE -DASCENDC_DUMP)
diff --git a/ggml/src/ggml-cann/kernels/ascendc_kernels.h b/ggml/src/ggml-cann/kernels/ascendc_kernels.h
new file mode 100644
index 00000000000000..bf891475148351
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/ascendc_kernels.h
@@ -0,0 +1,17 @@
+#ifndef ASCENDC_KERNELS_H
+#define ASCENDC_KERNELS_H
+
+#include "aclrtlaunch_ascendc_get_row_f32.h"
+#include "aclrtlaunch_ascendc_get_row_f16.h"
+#include "aclrtlaunch_ascendc_get_row_q8_0.h"
+#include "aclrtlaunch_ascendc_get_row_q4_0.h"
+
+#include "aclrtlaunch_ascendc_quantize_f32_q8_0.h"
+#include "aclrtlaunch_ascendc_quantize_f16_q8_0.h"
+
+#include "aclrtlaunch_ascendc_dup_by_rows_fp16.h"
+#include "aclrtlaunch_ascendc_dup_by_rows_fp32.h"
+#include "aclrtlaunch_ascendc_dup_by_rows_fp32_to_fp16.h"
+#include "aclrtlaunch_ascendc_dup_by_rows_fp16_to_fp32.h"
+
+#endif  // ASCENDC_KERNELS_H
diff --git a/ggml/src/ggml-cann/kernels/dup.cpp b/ggml/src/ggml-cann/kernels/dup.cpp
new file mode 100644
index 00000000000000..e2c651152f4865
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/dup.cpp
@@ -0,0 +1,223 @@
+#include "kernel_operator.h"
+
+#include <cmath>
+
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+
+template <typename SRC_T, typename DST_T>
+class DupByRows {
+   public:
+    __aicore__ inline DupByRows() {}
+    __aicore__ inline void init(GM_ADDR src, GM_ADDR dst, int64_t *input_ne_ub,
+                                size_t *input_nb_ub) {
+        /* Dup by rows when src is contigous on first dimension and dst is
+        contiguous, each kernel process one row.
+        */
+
+        // Input has four dims.
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        // param
+        num_rows = input_ne_ub[1] * input_ne_ub[2] * input_ne_ub[3];
+        num_elem = input_ne_ub[0];
+
+        // index for (ne[1], ne[2], ne[3]): (idx_ne1, idx_ne2, idx_ne3)
+        idx_ne3 = op_block_idx / (input_ne_ub[1] * input_ne_ub[2]);
+        idx_ne2 = (op_block_idx - idx_ne3 * (input_ne_ub[1] * input_ne_ub[2]))
+                  / (input_ne_ub[1]);
+        idx_ne1 = op_block_idx - idx_ne3 * (input_ne_ub[1] * input_ne_ub[2])
+                - idx_ne2 * input_ne_ub[1];
+
+        // src may not contiguous in dim [1,2,3], so stride decited by ne&nb
+        src_stride = input_nb_ub[3] * idx_ne3 + input_nb_ub[2] * idx_ne2
+                     + input_nb_ub[1] * idx_ne1;
+
+        // dst is contiguous
+        dst_stride = op_block_idx * (input_ne_ub[0] * sizeof(DST_T));
+
+        src_gm.SetGlobalBuffer(reinterpret_cast<__gm__ SRC_T *>(src +
+                                                                src_stride));
+        dst_gm.SetGlobalBuffer(reinterpret_cast<__gm__ DST_T *>(dst +
+                                                                dst_stride));
+
+        pipe.InitBuffer(src_queue, BUFFER_NUM, (sizeof(SRC_T) * num_elem +
+                                                32 - 1) / 32 * 32);
+        pipe.InitBuffer(dst_queue, BUFFER_NUM, (sizeof(DST_T) * num_elem +
+                                                32 - 1) / 32 * 32);
+    }
+
+    __aicore__ inline void copy_in() {
+        LocalTensor<SRC_T> src_local = src_queue.AllocTensor<SRC_T>();
+
+        DataCopyExtParams dataCopyParams;
+        dataCopyParams.blockCount = 1;
+        dataCopyParams.blockLen = num_elem * sizeof(SRC_T);
+        DataCopyPadExtParams<SRC_T> padParams;
+        DataCopyPad(src_local, src_gm, dataCopyParams, padParams);
+
+        src_queue.EnQue(src_local);
+    }
+
+    __aicore__ inline void copy_out() {
+        LocalTensor<DST_T> dst_local = dst_queue.DeQue<DST_T>();
+
+        DataCopyExtParams dataCopyParams;
+        dataCopyParams.blockCount = 1;
+        dataCopyParams.blockLen = num_elem * sizeof(DST_T);
+        DataCopyPad(dst_gm, dst_local, dataCopyParams);
+
+        dst_queue.FreeTensor(dst_local);
+    }
+
+    __aicore__ inline void dup() {
+        // main process, copy one row data from src to dst.
+        copy_in();
+
+        LocalTensor<SRC_T> src_local = src_queue.DeQue<SRC_T>();
+        LocalTensor<DST_T> dst_local = dst_queue.AllocTensor<DST_T>();
+
+        int32_t BLOCK_NUM = 32 / sizeof(DST_T);
+        DataCopy(dst_local, src_local, (num_elem + BLOCK_NUM - 1)
+                                        / BLOCK_NUM * BLOCK_NUM);
+        dst_queue.EnQue<DST_T>(dst_local);
+
+        src_queue.FreeTensor(src_local);
+        copy_out();
+    }
+
+    __aicore__ inline void dup_with_cast() {
+        // main process, copy one row data from src to dst.
+        // cast dtype from src to dst.
+        copy_in();
+
+        LocalTensor<SRC_T> src_local = src_queue.DeQue<SRC_T>();
+        LocalTensor<DST_T> dst_local = dst_queue.AllocTensor<DST_T>();
+
+        Cast(dst_local, src_local, RoundMode::CAST_NONE, num_elem);
+        dst_queue.EnQue<DST_T>(dst_local);
+
+        src_queue.FreeTensor(src_local);
+        copy_out();
+    }
+
+   private:
+
+    TPipe pipe;
+    GlobalTensor<SRC_T> src_gm;
+    GlobalTensor<DST_T> dst_gm;
+
+    int64_t num_rows;
+    int64_t num_elem;
+    int64_t idx_ne3;
+    int64_t idx_ne2;
+    int64_t idx_ne1;
+    int64_t src_stride;
+    int64_t dst_stride;
+
+    TQue<QuePosition::VECIN, BUFFER_NUM> src_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> dst_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp16(
+                                                        GM_ADDR src_gm,
+                                                        GM_ADDR dst_gm,
+                                                        GM_ADDR input_ne_gm,
+                                                        GM_ADDR input_nb_gm,
+                                                        GM_ADDR output_ne_gm,
+                                                        GM_ADDR output_nb_gm) {
+
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    DupByRows<half, half> op;
+    op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
+    op.dup();
+}
+
+extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp32(
+                                                        GM_ADDR src_gm,
+                                                        GM_ADDR dst_gm,
+                                                        GM_ADDR input_ne_gm,
+                                                        GM_ADDR input_nb_gm,
+                                                        GM_ADDR output_ne_gm,
+                                                        GM_ADDR output_nb_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    DupByRows<float_t, float_t> op;
+    op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
+    op.dup();
+}
+
+extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp32_to_fp16(
+                                                        GM_ADDR src_gm,
+                                                        GM_ADDR dst_gm,
+                                                        GM_ADDR input_ne_gm,
+                                                        GM_ADDR input_nb_gm,
+                                                        GM_ADDR output_ne_gm,
+                                                        GM_ADDR output_nb_gm) {
+
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    DupByRows<float_t, half> op;
+    op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
+    op.dup_with_cast();
+}
+
+extern "C" __global__ __aicore__ void ascendc_dup_by_rows_fp16_to_fp32(
+                                                        GM_ADDR src_gm,
+                                                        GM_ADDR dst_gm,
+                                                        GM_ADDR input_ne_gm,
+                                                        GM_ADDR input_nb_gm,
+                                                        GM_ADDR output_ne_gm,
+                                                        GM_ADDR output_nb_gm) {
+
+    // copy params from gm to ub.
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    DupByRows<half, float_t> op;
+    op.init(src_gm, dst_gm, input_ne_ub, input_nb_ub);
+    op.dup_with_cast();
+}
diff --git a/ggml/src/ggml-cann/kernels/get_row_f16.cpp b/ggml/src/ggml-cann/kernels/get_row_f16.cpp
new file mode 100644
index 00000000000000..c704b5b2ec0f39
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/get_row_f16.cpp
@@ -0,0 +1,186 @@
+#include "kernel_operator.h"
+
+// optimize me. Use template to avoid copy code.
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+
+class GET_ROW_F16 {
+   public:
+    __aicore__ inline GET_ROW_F16() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
+                                int64_t *input_ne_ub, size_t *input_nb_ub,
+                                int64_t *indices_ne_ub, size_t *indices_nb_ub,
+                                int64_t *output_ne_ub, size_t *output_nb_ub) {
+        // TODO, use template for F16/f32
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
+
+            indices_ne[i] = indices_ne_ub[i];
+            indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
+
+            output_ne[i] = output_ne_ub[i];
+            output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
+        }
+
+        // Indices has two dims. n_elements = all rows should get.
+        // dr, all rows should this thread get.
+        uint64_t n_elements =
+            indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
+        dr = n_elements / op_block_num;
+
+        uint64_t tails = n_elements % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        input_gm.SetGlobalBuffer((__gm__ half *)input);
+        indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
+        output_gm.SetGlobalBuffer((__gm__ float *)output);
+
+        uint64_t input_local_buffer_size = ((input_ne[0] * sizeof(half) + 31)
+                                             & ~31);
+        uint64_t output_local_buffer_size = ((input_ne[0] * sizeof(float) + 31)
+                                              & ~31);
+
+        local_buffer_elems = input_local_buffer_size / sizeof(half);
+
+        // TODO, consider long row that can't put in UB.
+        // All data should asign to 32. It's ok because all data is align to 32.
+        pipe.InitBuffer(input_queue, BUFFER_NUM, input_local_buffer_size);
+        pipe.InitBuffer(output_queue, BUFFER_NUM, output_local_buffer_size);
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset, size_t len) {
+        LocalTensor<half> input_local = input_queue.AllocTensor<half>();
+        size_t tail = len % 32;
+        len = len & ~31;
+        DataCopy(input_local, input_gm[offset], len);
+        if(tail != 0) {
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = tail * sizeof(half);
+            DataCopyPadExtParams<half> padParams;
+            DataCopyPad(input_local[len], input_gm[offset + len],
+                        dataCopyParams, padParams);
+        }
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset, size_t len) {
+        LocalTensor<float> output_local = output_queue.DeQue<float>();
+        size_t tail = len % 32;
+        len = len & ~31;
+        DataCopy(output_gm[offset], output_local, len);
+        if(tail != 0) {
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = tail * sizeof(float);
+            DataCopyPad(output_gm[offset + len], output_local[len],
+                        dataCopyParams);
+        }
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline void calculate_row(int64_t idx) {
+        const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
+        const int64_t indices_ne1_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
+            indices_ne[0];
+        const int64_t indices_ne0_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
+             indices_ne1_idx * indices_ne[0]);
+
+        const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
+                                       indices_ne1_idx * indices_stride[1] +
+                                       indices_ne2_idx * indices_stride[2];
+        const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
+
+        const int64_t input_offset = selected_row_idx * input_stride[1] +
+                                     indices_ne1_idx * input_stride[2] +
+                                     indices_ne2_idx * input_stride[3];
+
+        const int64_t output_offset = indices_ne0_idx * output_stride[1] +
+                                      indices_ne1_idx * output_stride[2] +
+                                      indices_ne2_idx * output_stride[3];
+
+        copy_in(input_offset, input_ne[0]);
+        LocalTensor<half> input_local = input_queue.DeQue<half>();
+        LocalTensor<float> output_local = output_queue.AllocTensor<float>();
+
+        Cast(output_local, input_local, RoundMode::CAST_NONE,
+             local_buffer_elems);
+        output_queue.EnQue(output_local);
+        copy_out(output_offset, input_ne[0]);
+
+        input_queue.FreeTensor(input_local);
+    }
+
+    __aicore__ inline void calculate() {
+        for (int64_t i = ir; i < ir + dr; i++) {
+            calculate_row(i);
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t indices_ne[4];
+    size_t indices_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    size_t local_buffer_elems;
+
+    int64_t ir;
+    int64_t dr;
+
+    TPipe pipe;
+    GlobalTensor<half> input_gm;
+    GlobalTensor<int32_t> indices_gm;
+    GlobalTensor<float> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_get_row_f16(
+    GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
+    GM_ADDR input_ne_gm, GM_ADDR input_nb_gm, GM_ADDR indices_ne_gm,
+    GM_ADDR indices_nb_gm, GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t indices_ne_ub[4];
+    size_t indices_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
+    copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    GET_ROW_F16 op;
+    op.init(input_gm, indices_gm, output_gm, input_ne_ub, input_nb_ub,
+            indices_ne_ub, indices_nb_ub, output_ne_ub, output_nb_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/get_row_f32.cpp b/ggml/src/ggml-cann/kernels/get_row_f32.cpp
new file mode 100644
index 00000000000000..9db080af369987
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/get_row_f32.cpp
@@ -0,0 +1,180 @@
+#include "kernel_operator.h"
+
+// optimize me. Use template to avoid copy code.
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+
+class GET_ROW_F32 {
+   public:
+    __aicore__ inline GET_ROW_F32() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
+                                int64_t *input_ne_ub, size_t *input_nb_ub,
+                                int64_t *indices_ne_ub, size_t *indices_nb_ub,
+                                int64_t *output_ne_ub, size_t *output_nb_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
+
+            indices_ne[i] = indices_ne_ub[i];
+            indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
+
+            output_ne[i] = output_ne_ub[i];
+            output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
+        }
+
+        // Indices has two dims. n_elements = all rows should get.
+        // dr, all rows should this thread get.
+        uint64_t n_elements =
+            indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
+        dr = n_elements / op_block_num;
+
+        uint64_t tails = n_elements % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        input_gm.SetGlobalBuffer((__gm__ float *)input);
+        indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
+        output_gm.SetGlobalBuffer((__gm__ float *)output);
+
+        uint64_t local_buffer_size = ((input_ne[0] * sizeof(float) + 31) & ~31);
+        local_buffer_elems = local_buffer_size / sizeof(float);
+
+        // TODO, consider long row that can't put in UB.
+        // All data should asign to 32. It's ok because all data is align to 32.
+        pipe.InitBuffer(input_queue, BUFFER_NUM, local_buffer_size);
+        pipe.InitBuffer(output_queue, BUFFER_NUM, local_buffer_size);
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset, size_t len) {
+        LocalTensor<float> input_local = input_queue.AllocTensor<float>();
+        size_t tail = len % 32;
+        len = len & ~31;
+        DataCopy(input_local, input_gm[offset], len);
+        if(tail != 0) {
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = tail * sizeof(float);
+            DataCopyPadExtParams<float> padParams;
+            DataCopyPad(input_local[len], input_gm[offset + len],
+                        dataCopyParams, padParams);
+        }
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset, size_t len) {
+        LocalTensor<float> output_local = output_queue.DeQue<float>();
+        size_t tail = len % 32;
+        len = len & ~31;
+        DataCopy(output_gm[offset], output_local, len);
+        if(tail != 0) {
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = tail * sizeof(float);
+            DataCopyPad(output_gm[offset + len], output_local[len],
+                        dataCopyParams);
+        }
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline void calculate_row(int64_t idx) {
+        const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
+        const int64_t indices_ne1_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
+            indices_ne[0];
+        const int64_t indices_ne0_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
+             indices_ne1_idx * indices_ne[0]);
+
+        const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
+                                       indices_ne1_idx * indices_stride[1] +
+                                       indices_ne2_idx * indices_stride[2];
+        const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
+
+        const int64_t input_offset = selected_row_idx * input_stride[1] +
+                                     indices_ne1_idx * input_stride[2] +
+                                     indices_ne2_idx * input_stride[3];
+
+        const int64_t output_offset = indices_ne0_idx * output_stride[1] +
+                                      indices_ne1_idx * output_stride[2] +
+                                      indices_ne2_idx * output_stride[3];
+
+        copy_in(input_offset, input_ne[0]);
+        LocalTensor<float> input_local = input_queue.DeQue<float>();
+        LocalTensor<float> output_local = output_queue.AllocTensor<float>();
+
+        DataCopy(output_local, input_local, local_buffer_elems);
+        output_queue.EnQue(output_local);
+        copy_out(output_offset, input_ne[0]);
+
+        input_queue.FreeTensor(input_local);
+    }
+
+    __aicore__ inline void calculate() {
+        for (int64_t i = ir; i < ir + dr; i++) {
+            calculate_row(i);
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t indices_ne[4];
+    size_t indices_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    size_t local_buffer_elems;
+
+    int64_t ir;
+    int64_t dr;
+
+    TPipe pipe;
+    GlobalTensor<float> input_gm;
+    GlobalTensor<int32_t> indices_gm;
+    GlobalTensor<float> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_get_row_f32(
+    GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
+    GM_ADDR input_ne_gm, GM_ADDR input_nb_gm, GM_ADDR indices_ne_gm,
+    GM_ADDR indices_nb_gm, GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t indices_ne_ub[4];
+    size_t indices_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
+    copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    GET_ROW_F32 op;
+    op.init(input_gm, indices_gm, output_gm, input_ne_ub, input_nb_ub,
+            indices_ne_ub, indices_nb_ub, output_ne_ub, output_nb_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp b/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp
new file mode 100644
index 00000000000000..a80bfeec2417d9
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp
@@ -0,0 +1,193 @@
+#include "kernel_operator.h"
+
+// optimize me. Use template to avoid copy code.
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+
+#define QK4_0 32
+
+class GET_ROW_Q4_0 {
+   public:
+    __aicore__ inline GET_ROW_Q4_0() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
+                                int64_t *input_ne_ub, int64_t *indices_ne_ub,
+                                size_t *indices_nb_ub, int64_t *output_ne_ub,
+                                size_t *output_nb_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            indices_ne[i] = indices_ne_ub[i];
+            indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
+            scale_ne[i] = input_ne_ub[i];
+            output_ne[i] = output_ne_ub[i];
+            output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
+        }
+
+        // one scale for a group.
+        scale_ne[0] /= QK4_0;
+
+        input_stride[0] = 1;
+        scale_stride[0] = 1;
+        output_stride[0] = 1;
+        for (int i = 1; i < 4; i++) {
+            input_stride[i] = input_stride[i - 1] * input_ne[i - 1];
+            scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
+        }
+
+        group_size_in_row = input_ne[0] / QK4_0;
+        int64_t scale_offset = input_ne[0] * input_ne[1] * input_ne[2] *
+                               input_ne[3] / 2;
+
+        // Indices has two dims. n_elements = all rows should get.
+        // dr, all rows should this thread get.
+        uint64_t n_elements =
+            indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
+        dr = n_elements / op_block_num;
+
+        uint64_t tails = n_elements % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        input_gm.SetGlobalBuffer((__gm__ int4b_t *)input);
+        scale_gm.SetGlobalBuffer((__gm__ half *)(input + scale_offset));
+        indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
+        output_gm.SetGlobalBuffer((__gm__ float *)output);
+
+        pipe.InitBuffer(input_queue, BUFFER_NUM, QK4_0 * sizeof(int4b_t));
+        pipe.InitBuffer(cast_queue, BUFFER_NUM, QK4_0 * sizeof(half));
+        pipe.InitBuffer(output_queue, BUFFER_NUM, QK4_0 * sizeof(float));
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset) {
+        LocalTensor<int4b_t> input_local = input_queue.AllocTensor<int4b_t>();
+        // 32 * sizeof(int4b_t) = 16, which is not aligned to 32, why no error?
+        DataCopy(input_local, input_gm[offset], QK4_0);
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset) {
+        LocalTensor<float> output_local = output_queue.DeQue<float>();
+        DataCopy(output_gm[offset], output_local, QK4_0);
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline void calculate_group(int64_t idx, int64_t group) {
+        const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
+        const int64_t indices_ne1_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
+            indices_ne[0];
+        const int64_t indices_ne0_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
+             indices_ne1_idx * indices_ne[0]);
+
+        const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
+                                       indices_ne1_idx * indices_stride[1] +
+                                       indices_ne2_idx * indices_stride[2];
+        const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
+
+        const int64_t input_offset = selected_row_idx * input_stride[1] +
+                                     indices_ne1_idx * input_stride[2] +
+                                     indices_ne2_idx * input_stride[3] +
+                                     group * QK4_0;
+        const int64_t scale_offset = selected_row_idx * scale_stride[1] +
+                                     indices_ne1_idx * scale_stride[2] +
+                                     indices_ne2_idx * scale_stride[3] + group;
+        const int64_t output_offset = indices_ne0_idx * output_stride[1] +
+                                      indices_ne1_idx * output_stride[2] +
+                                      indices_ne2_idx * output_stride[3] +
+                                      group * QK4_0;
+
+        copy_in(input_offset);
+        LocalTensor<int4b_t> input_local = input_queue.DeQue<int4b_t>();
+        LocalTensor<half> cast_local = cast_queue.AllocTensor<half>();
+        LocalTensor<float> output_local = output_queue.AllocTensor<float>();
+
+        // TODO: cast more data to speed up.
+        Cast(cast_local, input_local, RoundMode::CAST_NONE, QK4_0);
+        Cast(output_local, cast_local, RoundMode::CAST_NONE, QK4_0);
+
+        // Only mul need compile by group.
+        half scale = scale_gm.GetValue(scale_offset);
+
+        Muls(output_local, output_local, (float)scale, QK4_0);
+
+        input_queue.FreeTensor(input_local);
+        cast_queue.FreeTensor(cast_local);
+        output_queue.EnQue(output_local);
+
+        copy_out(output_offset);
+    }
+
+    __aicore__ inline void calculate() {
+        for (int64_t i = ir; i < ir + dr; i++) {
+            for (int64_t j = 0; j < group_size_in_row; j++) {
+                calculate_group(i, j);
+            }
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t scale_ne[4];
+    size_t scale_stride[4];
+
+    int64_t indices_ne[4];
+    size_t indices_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    int64_t ir;
+    int64_t dr;
+
+    int64_t group_size_in_row;
+
+    TPipe pipe;
+    GlobalTensor<int4b_t> input_gm;
+    GlobalTensor<half> scale_gm;
+    GlobalTensor<int32_t> indices_gm;
+    GlobalTensor<float> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+    TQue<QuePosition::VECIN, BUFFER_NUM> cast_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_get_row_q4_0(
+    GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
+    GM_ADDR input_ne_gm, GM_ADDR indices_ne_gm, GM_ADDR indices_nb_gm,
+    GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
+    int64_t input_ne_ub[4];
+    int64_t indices_ne_ub[4];
+    size_t indices_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
+    copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    GET_ROW_Q4_0 op;
+    op.init(input_gm, indices_gm, output_gm, input_ne_ub, indices_ne_ub,
+            indices_nb_ub, output_ne_ub, output_nb_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/get_row_q8_0.cpp b/ggml/src/ggml-cann/kernels/get_row_q8_0.cpp
new file mode 100644
index 00000000000000..ba9ab3c04832f4
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/get_row_q8_0.cpp
@@ -0,0 +1,191 @@
+#include "kernel_operator.h"
+
+// optimize me. Use template to avoid copy code.
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+
+#define QK8_0 32
+
+class GET_ROW_Q8_0 {
+   public:
+    __aicore__ inline GET_ROW_Q8_0() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR indices, GM_ADDR output,
+                                int64_t *input_ne_ub, int64_t *indices_ne_ub,
+                                size_t *indices_nb_ub, int64_t *output_ne_ub,
+                                size_t *output_nb_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            indices_ne[i] = indices_ne_ub[i];
+            indices_stride[i] = indices_nb_ub[i] / indices_nb_ub[0];
+            scale_ne[i] = input_ne_ub[i];
+            output_ne[i] = output_ne_ub[i];
+            output_stride[i] = output_nb_ub[i] / output_nb_ub[0];
+        }
+
+        // one scale for a group.
+        scale_ne[0] /= QK8_0;
+
+        input_stride[0] = 1;
+        scale_stride[0] = 1;
+        output_stride[0] = 1;
+        for (int i = 1; i < 4; i++) {
+            input_stride[i] = input_stride[i - 1] * input_ne[i - 1];
+            scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
+        }
+
+        group_size_in_row = input_ne[0] / QK8_0;
+        int64_t scale_offset = input_ne[0] * input_ne[1] * input_ne[2] *
+                               input_ne[3] * sizeof(int8_t);
+
+        // Indices has two dims. n_elements = all rows should get.
+        // dr, all rows should this thread get.
+        uint64_t n_elements =
+            indices_ne[0] * indices_ne[1] * indices_ne[2] * indices_ne[3];
+        dr = n_elements / op_block_num;
+
+        uint64_t tails = n_elements % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        input_gm.SetGlobalBuffer((__gm__ int8_t *)input);
+        scale_gm.SetGlobalBuffer((__gm__ half *)(input + scale_offset));
+        indices_gm.SetGlobalBuffer((__gm__ int32_t *)indices);
+        output_gm.SetGlobalBuffer((__gm__ float *)output);
+
+        pipe.InitBuffer(input_queue, BUFFER_NUM, QK8_0 * sizeof(int8_t));
+        pipe.InitBuffer(cast_queue, BUFFER_NUM, QK8_0 * sizeof(half));
+        pipe.InitBuffer(output_queue, BUFFER_NUM, QK8_0 * sizeof(float));
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset) {
+        LocalTensor<int8_t> input_local = input_queue.AllocTensor<int8_t>();
+        DataCopy(input_local, input_gm[offset], QK8_0);
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset) {
+        LocalTensor<float> output_local = output_queue.DeQue<float>();
+        DataCopy(output_gm[offset], output_local, QK8_0);
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline void calculate_group(int64_t idx, int64_t group) {
+        const int64_t indices_ne2_idx = idx / (indices_ne[0] * indices_ne[1]);
+        const int64_t indices_ne1_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1]) /
+            indices_ne[0];
+        const int64_t indices_ne0_idx =
+            (idx - indices_ne2_idx * indices_ne[0] * indices_ne[1] -
+             indices_ne1_idx * indices_ne[0]);
+
+        const int64_t indices_offset = indices_ne0_idx * indices_stride[0] +
+                                       indices_ne1_idx * indices_stride[1] +
+                                       indices_ne2_idx * indices_stride[2];
+        const int32_t selected_row_idx = indices_gm.GetValue(indices_offset);
+
+        const int64_t input_offset = selected_row_idx * input_stride[1] +
+                                     indices_ne1_idx * input_stride[2] +
+                                     indices_ne2_idx * input_stride[3] +
+                                     group * QK8_0;
+        const int64_t scale_offset = selected_row_idx * scale_stride[1] +
+                                     indices_ne1_idx * scale_stride[2] +
+                                     indices_ne2_idx * scale_stride[3] + group;
+        const int64_t output_offset = indices_ne0_idx * output_stride[1] +
+                                      indices_ne1_idx * output_stride[2] +
+                                      indices_ne2_idx * output_stride[3] +
+                                      group * QK8_0;
+
+        copy_in(input_offset);
+        LocalTensor<int8_t> input_local = input_queue.DeQue<int8_t>();
+        LocalTensor<half> cast_local = cast_queue.AllocTensor<half>();
+        LocalTensor<float> output_local = output_queue.AllocTensor<float>();
+
+        // TODO: cast more data to speed up.
+        Cast(cast_local, input_local, RoundMode::CAST_NONE, QK8_0);
+        Cast(output_local, cast_local, RoundMode::CAST_NONE, QK8_0);
+
+        // Only mul need compile by group.
+        half scale = scale_gm.GetValue(scale_offset);
+        Muls(output_local, output_local, (float)scale, QK8_0);
+
+        input_queue.FreeTensor(input_local);
+        cast_queue.FreeTensor(cast_local);
+        output_queue.EnQue(output_local);
+
+        copy_out(output_offset);
+    }
+
+    __aicore__ inline void calculate() {
+        for (int64_t i = ir; i < ir + dr; i++) {
+            for (int64_t j = 0; j < group_size_in_row; j++) {
+                calculate_group(i, j);
+            }
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t scale_ne[4];
+    size_t scale_stride[4];
+
+    int64_t indices_ne[4];
+    size_t indices_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    int64_t ir;
+    int64_t dr;
+
+    int64_t group_size_in_row;
+
+    TPipe pipe;
+    GlobalTensor<int8_t> input_gm;
+    GlobalTensor<half> scale_gm;
+    GlobalTensor<int32_t> indices_gm;
+    GlobalTensor<float> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+    TQue<QuePosition::VECIN, BUFFER_NUM> cast_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_get_row_q8_0(
+    GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm,
+    GM_ADDR input_ne_gm, GM_ADDR indices_ne_gm, GM_ADDR indices_nb_gm,
+    GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) {
+    int64_t input_ne_ub[4];
+    int64_t indices_ne_ub[4];
+    size_t indices_nb_ub[4];
+    int64_t output_ne_ub[4];
+    size_t output_nb_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(indices_ne_gm, indices_ne_ub, 32);
+    copy_to_ub(indices_nb_gm, indices_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+    copy_to_ub(output_nb_gm, output_nb_ub, 32);
+
+    GET_ROW_Q8_0 op;
+    op.init(input_gm, indices_gm, output_gm, input_ne_ub, indices_ne_ub,
+            indices_nb_ub, output_ne_ub, output_nb_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp b/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp
new file mode 100644
index 00000000000000..8423b3f02a8f8e
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp
@@ -0,0 +1,208 @@
+#include "kernel_operator.h"
+
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+#define QK8_0 32
+
+class QUANTIZE_F16_Q8_0 {
+   public:
+    __aicore__ inline QUANTIZE_F16_Q8_0() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR output,
+                                int64_t *input_ne_ub, size_t *input_nb_ub,
+                                int64_t *output_ne_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
+
+            output_ne[i] = output_ne_ub[i];
+        }
+
+        output_stride[0] = 1;
+        for (int i = 1; i < 4; i++) {
+            output_stride[i] = output_stride[i - 1] * output_ne[i - 1];
+        }
+
+        scale_ne = input_ne;
+        scale_stride[0] = 1;
+        scale_stride[1] = input_ne[0] / QK8_0;
+        for (int i = 2; i < 4; i++) {
+            scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
+        }
+
+        // split input tensor by rows.
+        uint64_t nr = input_ne[1] * input_ne[2] * input_ne[3];
+        dr = nr / op_block_num;
+
+        uint64_t tails = nr % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        group_size_in_row = scale_stride[1];
+        int64_t output_size = output_ne[0] * output_ne[1] * output_ne[2] *
+                              output_ne[3] * sizeof(uint8_t);
+
+        input_gm.SetGlobalBuffer((__gm__ half *)input);
+        output_gm.SetGlobalBuffer((__gm__ int8_t *)output);
+        scale_gm.SetGlobalBuffer((__gm__ half *)(output + output_size + ir *
+                                                 group_size_in_row *
+                                                 sizeof(half)));
+
+        pipe.InitBuffer(input_queue, BUFFER_NUM, QK8_0 * sizeof(half));
+        pipe.InitBuffer(output_queue, BUFFER_NUM, QK8_0 * sizeof(int8_t));
+        pipe.InitBuffer(work_queue, 1, 32);
+        pipe.InitBuffer(max_queue, 1, 32);
+        pipe.InitBuffer(abs_queue, 1, QK8_0 * sizeof(float));
+        pipe.InitBuffer(scale_queue, 1, 32);
+        pipe.InitBuffer(cast_queue ,1 ,QK8_0 * sizeof(float));
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset) {
+        LocalTensor<half> input_local = input_queue.AllocTensor<half>();
+        DataCopy(input_local, input_gm[offset], QK8_0);
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset) {
+        LocalTensor<int8_t> output_local = output_queue.DeQue<int8_t>();
+        DataCopy(output_gm[offset], output_local, QK8_0);
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline half calculate_group(int64_t row, int64_t group) {
+        const int64_t i3 = row / (input_ne[1] * input_ne[2]);
+        const int64_t i2 = (row - i3 * input_ne[1] * input_ne[2]) / input_ne[1];
+        const int64_t i1 =
+            row - i3 * input_ne[1] * input_ne[2] - i2 * input_ne[1];
+
+        const int64_t input_offset = i1 * input_stride[1] +
+                                     i2 * input_stride[2] +
+                                     i3 * input_stride[3] + QK8_0 * group;
+
+        const int64_t output_offset = i1 * output_stride[1] +
+                                      i2 * output_stride[2] +
+                                      i3 * output_stride[3] + QK8_0 * group;
+
+        copy_in(input_offset);
+        LocalTensor<half> input_local = input_queue.DeQue<half>();
+        LocalTensor<int8_t> output_local = output_queue.AllocTensor<int8_t>();
+        LocalTensor<float> work_local = work_queue.AllocTensor<float>();
+        LocalTensor<float> abs_local = abs_queue.AllocTensor<float>();
+        LocalTensor<float> max_local = max_queue.AllocTensor<float>();
+        LocalTensor<float> cast_local = cast_queue.AllocTensor<float>();
+
+        Cast(cast_local, input_local, RoundMode::CAST_NONE, QK8_0);
+        Abs(abs_local, cast_local, QK8_0);
+        ReduceMax(max_local, abs_local, work_local, QK8_0);
+
+        pipe_barrier(PIPE_ALL);
+        float d = max_local.GetValue(0);
+        d = d / ((1 << 7) - 1);
+        if (d != 0) {
+            Muls(cast_local, cast_local, 1.0f / d, QK8_0);
+        }
+
+        Cast(cast_local, cast_local, RoundMode::CAST_ROUND, QK8_0);
+        Cast(input_local, cast_local, RoundMode::CAST_ROUND, QK8_0);
+        Cast(output_local, input_local, RoundMode::CAST_ROUND, QK8_0);
+        output_queue.EnQue(output_local);
+        copy_out(output_offset);
+
+        input_queue.FreeTensor(input_local);
+        work_queue.FreeTensor(work_local);
+        abs_queue.FreeTensor(abs_local);
+        max_queue.FreeTensor(max_local);
+        cast_queue.FreeTensor(cast_local);
+        return (half)d;
+    }
+
+    __aicore__ inline void calculate() {
+        LocalTensor<half> scale_local = scale_queue.AllocTensor<half>();
+        uint32_t scale_local_offset = 0;
+        uint32_t scale_global_offset = 0;
+        for (int64_t i = ir; i < ir + dr; i++) {
+            for (int64_t j = 0; j < group_size_in_row; j++) {
+                half scale = calculate_group(i, j);
+                scale_local.SetValue(scale_local_offset++, scale);
+                if (scale_local_offset == 16) {
+                    scale_local_offset = 0;
+                    // TODO: OPTIMIZE ME
+                    pipe_barrier(PIPE_ALL);
+                    DataCopy(scale_gm[scale_global_offset], scale_local, 16);
+                    pipe_barrier(PIPE_ALL);
+                    scale_global_offset += 16;
+                }
+            }
+        }
+
+        if (scale_local_offset != 0) {
+            pipe_barrier(PIPE_ALL);
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = scale_local_offset * sizeof(half);
+            DataCopyPad(scale_gm[scale_global_offset], scale_local,
+                        dataCopyParams);
+            pipe_barrier(PIPE_ALL);
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t *scale_ne;
+    size_t scale_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    int64_t group_size_in_row;
+
+    int64_t ir;
+    int64_t dr;
+
+    TPipe pipe;
+    GlobalTensor<half> input_gm;
+    GlobalTensor<half> scale_gm;
+    GlobalTensor<int8_t> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+    TQue<QuePosition::VECIN, 1> work_queue;
+    TQue<QuePosition::VECOUT, 1> max_queue;
+    TQue<QuePosition::VECIN, 1> abs_queue;
+    TQue<QuePosition::VECOUT, 1> scale_queue;
+    TQue<QuePosition::VECOUT, 1> cast_queue;
+
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_quantize_f16_q8_0(
+    GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
+    GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+
+    QUANTIZE_F16_Q8_0 op;
+    op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp b/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp
new file mode 100644
index 00000000000000..b7c575093e9c1c
--- /dev/null
+++ b/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp
@@ -0,0 +1,206 @@
+#include "kernel_operator.h"
+
+using namespace AscendC;
+
+#define BUFFER_NUM 2
+#define QK8_0 32
+
+class QUANTIZE_F32_Q8_0 {
+   public:
+    __aicore__ inline QUANTIZE_F32_Q8_0() {}
+    __aicore__ inline void init(GM_ADDR input, GM_ADDR output,
+                                int64_t *input_ne_ub, size_t *input_nb_ub,
+                                int64_t *output_ne_ub) {
+        int64_t op_block_num = GetBlockNum();
+        int64_t op_block_idx = GetBlockIdx();
+
+        for (int i = 0; i < 4; i++) {
+            input_ne[i] = input_ne_ub[i];
+            input_stride[i] = input_nb_ub[i] / input_nb_ub[0];
+
+            output_ne[i] = output_ne_ub[i];
+        }
+
+        output_stride[0] = 1;
+        for (int i = 1; i < 4; i++) {
+            output_stride[i] = output_stride[i - 1] * output_ne[i - 1];
+        }
+
+        scale_ne = input_ne;
+        scale_stride[0] = 1;
+        scale_stride[1] = input_ne[0] / QK8_0;
+        for (int i = 2; i < 4; i++) {
+            scale_stride[i] = scale_stride[i - 1] * scale_ne[i - 1];
+        }
+
+        // split input tensor by rows.
+        uint64_t nr = input_ne[1] * input_ne[2] * input_ne[3];
+        dr = nr / op_block_num;
+
+        uint64_t tails = nr % op_block_num;
+        if (op_block_idx < tails) {
+            dr += 1;
+            ir = dr * op_block_idx;
+        } else {
+            ir = dr * op_block_idx + tails;
+        }
+
+        group_size_in_row = scale_stride[1];
+        int64_t output_size = output_ne[0] * output_ne[1] * output_ne[2] *
+                              output_ne[3] * sizeof(uint8_t);
+
+        input_gm.SetGlobalBuffer((__gm__ float *)input);
+        output_gm.SetGlobalBuffer((__gm__ int8_t *)output);
+        scale_gm.SetGlobalBuffer((__gm__ half *)(output + output_size +
+                                                 ir * group_size_in_row *
+                                                 sizeof(half)));
+
+        pipe.InitBuffer(input_queue, BUFFER_NUM, QK8_0 * sizeof(float));
+        pipe.InitBuffer(output_queue, BUFFER_NUM, QK8_0 * sizeof(int8_t));
+        pipe.InitBuffer(work_queue, 1, 32);
+        pipe.InitBuffer(max_queue, 1, 32);
+        pipe.InitBuffer(abs_queue, 1, QK8_0 * sizeof(float));
+        pipe.InitBuffer(cast_queue, 1, QK8_0 * sizeof(half));
+        pipe.InitBuffer(scale_queue, 1, 32);
+    }
+
+    __aicore__ inline void copy_in(uint32_t offset) {
+        LocalTensor<float> input_local = input_queue.AllocTensor<float>();
+        DataCopy(input_local, input_gm[offset], QK8_0);
+        input_queue.EnQue(input_local);
+    }
+
+    __aicore__ inline void copy_out(uint32_t offset) {
+        LocalTensor<int8_t> output_local = output_queue.DeQue<int8_t>();
+        DataCopy(output_gm[offset], output_local, QK8_0);
+        output_queue.FreeTensor(output_local);
+    }
+
+    __aicore__ inline half calculate_group(int64_t row, int64_t group) {
+        const int64_t i3 = row / (input_ne[1] * input_ne[2]);
+        const int64_t i2 = (row - i3 * input_ne[1] * input_ne[2]) / input_ne[1];
+        const int64_t i1 =
+            row - i3 * input_ne[1] * input_ne[2] - i2 * input_ne[1];
+
+        const int64_t input_offset = i1 * input_stride[1] +
+                                     i2 * input_stride[2] +
+                                     i3 * input_stride[3] + QK8_0 * group;
+
+        const int64_t output_offset = i1 * output_stride[1] +
+                                      i2 * output_stride[2] +
+                                      i3 * output_stride[3] + QK8_0 * group;
+
+        copy_in(input_offset);
+        LocalTensor<float> input_local = input_queue.DeQue<float>();
+        LocalTensor<int8_t> output_local = output_queue.AllocTensor<int8_t>();
+        LocalTensor<float> work_local = work_queue.AllocTensor<float>();
+        LocalTensor<float> abs_local = abs_queue.AllocTensor<float>();
+        LocalTensor<float> max_local = max_queue.AllocTensor<float>();
+        LocalTensor<half> cast_local = cast_queue.AllocTensor<half>();
+
+        Abs(abs_local, input_local, QK8_0);
+        ReduceMax(max_local, abs_local, work_local, QK8_0);
+        pipe_barrier(PIPE_ALL);
+        float d = max_local.GetValue(0);
+        d = d / ((1 << 7) - 1);
+        if (d != 0) {
+            Muls(input_local, input_local, 1.0f / d, QK8_0);
+        }
+
+        Cast(input_local, input_local, RoundMode::CAST_ROUND, QK8_0);
+        Cast(cast_local, input_local, RoundMode::CAST_ROUND, QK8_0);
+        Cast(output_local, cast_local, RoundMode::CAST_ROUND, QK8_0);
+        output_queue.EnQue(output_local);
+        copy_out(output_offset);
+
+        input_queue.FreeTensor(input_local);
+        work_queue.FreeTensor(work_local);
+        abs_queue.FreeTensor(abs_local);
+        max_queue.FreeTensor(max_local);
+        cast_queue.FreeTensor(cast_local);
+
+        return (half)d;
+    }
+
+    __aicore__ inline void calculate() {
+        LocalTensor<half> scale_local = scale_queue.AllocTensor<half>();
+        uint32_t scale_local_offset = 0;
+        uint32_t scale_global_offset = 0;
+        for (int64_t i = ir; i < ir + dr; i++) {
+            for (int64_t j = 0; j < group_size_in_row; j++) {
+                half scale = calculate_group(i, j);
+                scale_local.SetValue(scale_local_offset++, scale);
+                if (scale_local_offset == 16) {
+                    scale_local_offset = 0;
+                    // TODO: OPTIMIZE ME
+                    pipe_barrier(PIPE_ALL);
+                    DataCopy(scale_gm[scale_global_offset], scale_local, 16);
+                    pipe_barrier(PIPE_ALL);
+                    scale_global_offset += 16;
+                }
+            }
+        }
+
+        if (scale_local_offset != 0) {
+            pipe_barrier(PIPE_ALL);
+            DataCopyExtParams dataCopyParams;
+            dataCopyParams.blockCount = 1;
+            dataCopyParams.blockLen = scale_local_offset * sizeof(half);
+            DataCopyPad(scale_gm[scale_global_offset], scale_local,
+                        dataCopyParams);
+            pipe_barrier(PIPE_ALL);
+        }
+    }
+
+   private:
+    int64_t input_ne[4];
+    size_t input_stride[4];
+
+    int64_t *scale_ne;
+    size_t scale_stride[4];
+
+    int64_t output_ne[4];
+    size_t output_stride[4];
+
+    int64_t group_size_in_row;
+
+    int64_t ir;
+    int64_t dr;
+
+    TPipe pipe;
+    GlobalTensor<float> input_gm;
+    GlobalTensor<half> scale_gm;
+    GlobalTensor<int8_t> output_gm;
+    TQue<QuePosition::VECIN, BUFFER_NUM> input_queue;
+    TQue<QuePosition::VECOUT, BUFFER_NUM> output_queue;
+    TQue<QuePosition::VECIN, 1> work_queue;
+    TQue<QuePosition::VECOUT, 1> max_queue;
+    TQue<QuePosition::VECIN, 1> abs_queue;
+    TQue<QuePosition::VECIN, 1> cast_queue;
+    TQue<QuePosition::VECOUT, 1> scale_queue;
+};
+
+template <typename T>
+__aicore__ inline void copy_to_ub(GM_ADDR gm, T *ub, size_t size) {
+    auto gm_ptr = (__gm__ uint8_t *)gm;
+    auto ub_ptr = (uint8_t *)(ub);
+    for (int32_t i = 0; i < size; ++i, ++ub_ptr, ++gm_ptr) {
+        *ub_ptr = *gm_ptr;
+    }
+}
+
+extern "C" __global__ __aicore__ void ascendc_quantize_f32_q8_0(
+    GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm,
+    GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) {
+    int64_t input_ne_ub[4];
+    size_t input_nb_ub[4];
+    int64_t output_ne_ub[4];
+
+    copy_to_ub(input_ne_gm, input_ne_ub, 32);
+    copy_to_ub(input_nb_gm, input_nb_ub, 32);
+    copy_to_ub(output_ne_gm, output_ne_ub, 32);
+
+    QUANTIZE_F32_Q8_0 op;
+    op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub);
+    op.calculate();
+}
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 60b3c5e7a12d37..2ae2d6d5f58a93 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -3341,7 +3341,7 @@ bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tenso
 }
 
 // check if t1 can be represented as a repeatition of t0
-static inline bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
+bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
     static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
 
     return ggml_is_empty(t0) ? ggml_is_empty(t1) :
@@ -13699,6 +13699,7 @@ static void ggml_compute_forward_soft_max(
     }
 }
 
+
 // ggml_compute_forward_soft_max_back
 
 static void ggml_compute_forward_soft_max_back_f32(
@@ -21995,6 +21996,14 @@ int ggml_cpu_has_rpc(void) {
 #endif
 }
 
+int ggml_cpu_has_cann(void) {
+#if defined(GGML_USE_CANN)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_gpublas(void) {
     return ggml_cpu_has_cuda() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl();
 }
diff --git a/src/llama.cpp b/src/llama.cpp
index 89b5763dcd7f87..20e85b3ebe5dfa 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -19,6 +19,8 @@
 #  include "ggml-sycl.h"
 #elif defined(GGML_USE_KOMPUTE)
 #   include "ggml-kompute.h"
+#elif defined(GGML_USE_CANN)
+#   include "ggml-cann.h"
 #endif
 
 #ifdef GGML_USE_BLAS
@@ -2079,6 +2081,8 @@ struct llama_state {
         ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data);
 #elif defined(GGML_USE_CUDA)
         ggml_backend_cuda_log_set_callback(log_callback, log_callback_user_data);
+#elif defined(GGML_USE_CANN)
+        ggml_backend_cann_log_set_callback(log_callback, log_callback_user_data);
 #endif
     }
 
@@ -2889,6 +2893,8 @@ static size_t llama_get_device_count(const llama_model & model) {
     count = ggml_backend_sycl_get_device_count();
 #elif defined(GGML_USE_VULKAN)
     count = ggml_backend_vk_get_device_count();
+#elif defined(GGML_USE_CANN)
+    return ggml_backend_cann_get_device_count();
 #endif
 #if defined(GGML_USE_RPC)
     count += model.rpc_servers.size();
@@ -2921,6 +2927,8 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_
     if (buft == nullptr) {
         LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
     }
+#elif defined(GGML_USE_CANN)
+    buft = ggml_backend_cann_buffer_type(gpu);
 #endif
 
     if (buft == nullptr) {
@@ -2981,6 +2989,11 @@ static size_t llama_get_device_memory(const llama_model & model, int device) {
     size_t free;
     ggml_backend_vk_get_device_memory(device, &free, &total);
     return free;
+#elif defined(GGML_USE_CANN)
+    size_t total;
+    size_t free;
+    ggml_backend_cann_get_device_memory(device, &total, &free);
+    return free;
 #else
     return 1;
 #endif
@@ -18871,6 +18884,8 @@ size_t llama_max_devices(void) {
     return GGML_SYCL_MAX_DEVICES;
 #elif defined(GGML_USE_VULKAN)
     return GGML_VK_MAX_DEVICES;
+#elif defined(GGML_USE_CANN)
+    return GGML_CANN_MAX_DEVICES;
 #else
     return 1;
 #endif
@@ -19212,6 +19227,30 @@ struct llama_context * llama_new_context_with_model(
             }
             ctx->backends.push_back(backend);
         }
+#elif defined(GGML_USE_CANN)
+    // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+    // TODO: ggml_backend_cann is not support split tensor now, just leave code here.
+    if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+        ggml_backend_t backend = ggml_backend_cann_init(model->main_gpu);
+        if (backend == nullptr) {
+            LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, model->main_gpu);
+            llama_free(ctx);
+            return nullptr;
+        }
+        ctx->backends.push_back(backend);
+    } else {
+        // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
+        // TODO: currently, CANN can't use multi-gpus, just leave code here for further cann version.
+        for (int32_t device = 0; device < ggml_backend_cann_get_device_count(); ++device) {
+            ggml_backend_t backend = ggml_backend_cann_init(device);
+            if (backend == nullptr) {
+                LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, device);
+                llama_free(ctx);
+                return nullptr;
+            }
+            ctx->backends.push_back(backend);
+        }
+    }
 #endif
 
 #ifdef GGML_USE_BLAS
@@ -21789,6 +21828,8 @@ void llama_log_set(ggml_log_callback log_callback, void * user_data) {
     ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
 #elif defined(GGML_USE_CUDA)
     ggml_backend_cuda_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
+#elif defined(GGML_USE_CANN)
+    ggml_backend_cann_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
 #endif
 }
 
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index a1bfc8fddd418c..5c309d4288f8f4 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -759,7 +759,7 @@ struct test_dup : public test_case {
     }
 
     test_dup(ggml_type type = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 10, 10, 1},
+            std::array<int64_t, 4> ne = {10, 10, 20, 1},
             std::array<int64_t, 4> permute = {0, 0, 0, 0})
         : type(type), ne(ne), permute(permute),
             _use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
@@ -779,9 +779,11 @@ struct test_cpy : public test_case {
     const ggml_type type_src;
     const ggml_type type_dst;
     const std::array<int64_t, 4> ne;
+    const std::array<int64_t, 4> permute;
+    bool _src_use_permute;
 
     std::string vars() override {
-        return VARS_TO_STR3(type_src, type_dst, ne);
+        return VARS_TO_STR4(type_src, type_dst, ne, permute);
     }
 
     double max_nmse_err() override {
@@ -793,12 +795,18 @@ struct test_cpy : public test_case {
     }
 
     test_cpy(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
-            std::array<int64_t, 4> ne = {10, 10, 10, 1})
-        : type_src(type_src), type_dst(type_dst), ne(ne) {}
+            std::array<int64_t, 4> ne = {10, 10, 10, 1},
+            std::array<int64_t, 4> permute = {0, 0, 0, 0},
+            bool _dst_use_permute = false)
+        : type_src(type_src), type_dst(type_dst), ne(ne), permute(permute),
+          _src_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
-        ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, ne.data());
+        if (_src_use_permute) {
+            src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
+        }
+        ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, src->ne);
         ggml_tensor * out = ggml_cpy(ctx, src, dst);
         return out;
     }
@@ -1174,6 +1182,7 @@ struct test_soft_max : public test_case {
     }
 };
 
+
 // GGML_OP_ROPE
 struct test_rope : public test_case {
     const ggml_type type;
@@ -2146,12 +2155,22 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_dup(GGML_TYPE_F16));
     test_cases.emplace_back(new test_dup(GGML_TYPE_I32));
     test_cases.emplace_back(new test_dup(GGML_TYPE_I16));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F32, {10, 10, 5, 1}, {0, 2, 1, 3}));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F16, {10, 10, 5, 1}, {0, 2, 1, 3})); // dup by rows
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F32, {10, 10, 5, 1}, {1, 0, 2, 3}));
+    test_cases.emplace_back(new test_dup(GGML_TYPE_F16, {10, 10, 5, 1}, {1, 0, 2, 3})); // dup dst not-contiguous
     test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3}));
     test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));
 
     for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
         for (ggml_type type_dst : all_types) {
            test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
+           test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
+        }
+    }
+    for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
+        for (ggml_type type_dst : {GGML_TYPE_F16, GGML_TYPE_F32}) {
+            test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {1, 0, 2, 3})); // cpy not-contiguous
         }
     }
 
@@ -2283,7 +2302,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         for (int n = 0; n < 10; ++n) {
             int64_t ne0 = dist_ne0(rng);
             int64_t ne1 = dist_ne1(rng);
-            test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, n/2 == 0, 0.1f, ne0 < 1000 ? 4.0f : 0.0f));
+            test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, GGML_TYPE_F32, {ne0, ne1, 1, 1}, n/2 == 0, 0.1f, ne0 < 1000 ? 4.0f : 0.0f));
         }
 
         exponent <<= 1;
@@ -2302,7 +2321,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
             }
         }
     }
-
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, true, 0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 8.0f));

From 30f80ca0bcee58669ada7a94244eeccc8c4807cc Mon Sep 17 00:00:00 2001
From: Brian <mofosyne@gmail.com>
Date: Thu, 18 Jul 2024 00:57:06 +1000
Subject: [PATCH 068/125] CONTRIBUTING.md : remove mention of noci (#8541)

---
 CONTRIBUTING.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 9ad5160495a8e1..48f9914af8819c 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -5,7 +5,6 @@
 - Test your changes:
   - Using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the GGML library
   - Execute [the full CI locally on your machine](ci/README.md) before publishing
-- If the pull request contains only documentation changes (e.g., updating READMEs, adding new wiki pages), please add `[no ci]` to the commit title. This will skip unnecessary CI checks and help reduce build times
 - Please rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs.
   - The PR template has a series of review complexity checkboxes `[ ]` that [you can mark as](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/about-task-lists) `[X]` for your conveience
 

From b3283448ce9a5098226afe1d8648ccc578511fe4 Mon Sep 17 00:00:00 2001
From: Al Mochkin <14274697+amochkin@users.noreply.github.com>
Date: Wed, 17 Jul 2024 20:21:55 +0200
Subject: [PATCH 069/125] build : Fix docker build warnings (#8535) (#8537)

---
 .devops/full-cuda.Dockerfile           | 2 +-
 .devops/full-rocm.Dockerfile           | 2 +-
 .devops/full.Dockerfile                | 2 +-
 .devops/llama-cli-cuda.Dockerfile      | 4 ++--
 .devops/llama-cli-intel.Dockerfile     | 4 ++--
 .devops/llama-cli-rocm.Dockerfile      | 2 +-
 .devops/llama-cli-vulkan.Dockerfile    | 2 +-
 .devops/llama-cli.Dockerfile           | 4 ++--
 .devops/llama-server-cuda.Dockerfile   | 4 ++--
 .devops/llama-server-intel.Dockerfile  | 4 ++--
 .devops/llama-server-rocm.Dockerfile   | 2 +-
 .devops/llama-server-vulkan.Dockerfile | 2 +-
 .devops/llama-server.Dockerfile        | 4 ++--
 13 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile
index 2a7da586a7086d..61f671465ca71b 100644
--- a/.devops/full-cuda.Dockerfile
+++ b/.devops/full-cuda.Dockerfile
@@ -6,7 +6,7 @@ ARG CUDA_VERSION=11.7.1
 # Target the CUDA build image
 ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
 
-FROM ${BASE_CUDA_DEV_CONTAINER} as build
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 ARG CUDA_DOCKER_ARCH=all
diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile
index 5cbd2e7a131aa9..680d1cb92205d7 100644
--- a/.devops/full-rocm.Dockerfile
+++ b/.devops/full-rocm.Dockerfile
@@ -6,7 +6,7 @@ ARG ROCM_VERSION=5.6
 # Target the CUDA build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
 
-FROM ${BASE_ROCM_DEV_CONTAINER} as build
+FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 # List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile
index 6f19afa9c37f1c..2a06f82b738aee 100644
--- a/.devops/full.Dockerfile
+++ b/.devops/full.Dockerfile
@@ -1,6 +1,6 @@
 ARG UBUNTU_VERSION=22.04
 
-FROM ubuntu:$UBUNTU_VERSION as build
+FROM ubuntu:$UBUNTU_VERSION AS build
 
 RUN apt-get update && \
     apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
diff --git a/.devops/llama-cli-cuda.Dockerfile b/.devops/llama-cli-cuda.Dockerfile
index bff946cbc24057..8eda63a89504d7 100644
--- a/.devops/llama-cli-cuda.Dockerfile
+++ b/.devops/llama-cli-cuda.Dockerfile
@@ -6,7 +6,7 @@ ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VER
 # Target the CUDA runtime image
 ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
 
-FROM ${BASE_CUDA_DEV_CONTAINER} as build
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 ARG CUDA_DOCKER_ARCH=all
@@ -25,7 +25,7 @@ ENV GGML_CUDA=1
 
 RUN make -j$(nproc) llama-cli
 
-FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
+FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
 
 RUN apt-get update && \
     apt-get install -y libgomp1
diff --git a/.devops/llama-cli-intel.Dockerfile b/.devops/llama-cli-intel.Dockerfile
index bd816f9f5a1e6a..2bf82bb586e6d6 100644
--- a/.devops/llama-cli-intel.Dockerfile
+++ b/.devops/llama-cli-intel.Dockerfile
@@ -1,6 +1,6 @@
 ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
 
-FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
 
 ARG GGML_SYCL_F16=OFF
 RUN apt-get update && \
@@ -17,7 +17,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
     cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
     cmake --build build --config Release --target llama-cli
 
-FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
 
 COPY --from=build /app/build/bin/llama-cli /llama-cli
 
diff --git a/.devops/llama-cli-rocm.Dockerfile b/.devops/llama-cli-rocm.Dockerfile
index caa507b0818852..c3d1ab06702ec5 100644
--- a/.devops/llama-cli-rocm.Dockerfile
+++ b/.devops/llama-cli-rocm.Dockerfile
@@ -6,7 +6,7 @@ ARG ROCM_VERSION=5.6
 # Target the CUDA build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
 
-FROM ${BASE_ROCM_DEV_CONTAINER} as build
+FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 # List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
diff --git a/.devops/llama-cli-vulkan.Dockerfile b/.devops/llama-cli-vulkan.Dockerfile
index 6155d588147c98..9b0dad8bf7a13b 100644
--- a/.devops/llama-cli-vulkan.Dockerfile
+++ b/.devops/llama-cli-vulkan.Dockerfile
@@ -1,6 +1,6 @@
 ARG UBUNTU_VERSION=jammy
 
-FROM ubuntu:$UBUNTU_VERSION as build
+FROM ubuntu:$UBUNTU_VERSION AS build
 
 # Install build tools
 RUN apt update && apt install -y git build-essential cmake wget libgomp1
diff --git a/.devops/llama-cli.Dockerfile b/.devops/llama-cli.Dockerfile
index 38382bfc9add2a..7f741aa46ecf0e 100644
--- a/.devops/llama-cli.Dockerfile
+++ b/.devops/llama-cli.Dockerfile
@@ -1,6 +1,6 @@
 ARG UBUNTU_VERSION=22.04
 
-FROM ubuntu:$UBUNTU_VERSION as build
+FROM ubuntu:$UBUNTU_VERSION AS build
 
 RUN apt-get update && \
     apt-get install -y build-essential git
@@ -11,7 +11,7 @@ COPY . .
 
 RUN make -j$(nproc) llama-cli
 
-FROM ubuntu:$UBUNTU_VERSION as runtime
+FROM ubuntu:$UBUNTU_VERSION AS runtime
 
 RUN apt-get update && \
     apt-get install -y libgomp1
diff --git a/.devops/llama-server-cuda.Dockerfile b/.devops/llama-server-cuda.Dockerfile
index d7eaa0925a4a5d..67328cf1c17881 100644
--- a/.devops/llama-server-cuda.Dockerfile
+++ b/.devops/llama-server-cuda.Dockerfile
@@ -6,7 +6,7 @@ ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VER
 # Target the CUDA runtime image
 ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
 
-FROM ${BASE_CUDA_DEV_CONTAINER} as build
+FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 ARG CUDA_DOCKER_ARCH=all
@@ -27,7 +27,7 @@ ENV LLAMA_CURL=1
 
 RUN make -j$(nproc) llama-server
 
-FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
+FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
 
 RUN apt-get update && \
     apt-get install -y libcurl4-openssl-dev libgomp1 curl
diff --git a/.devops/llama-server-intel.Dockerfile b/.devops/llama-server-intel.Dockerfile
index 8f8fef8c09730d..eb9aba618d4d88 100644
--- a/.devops/llama-server-intel.Dockerfile
+++ b/.devops/llama-server-intel.Dockerfile
@@ -1,6 +1,6 @@
 ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
 
-FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
 
 ARG GGML_SYCL_F16=OFF
 RUN apt-get update && \
@@ -17,7 +17,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
     cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
     cmake --build build --config Release --target llama-server
 
-FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
+FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
 
 RUN apt-get update && \
     apt-get install -y libcurl4-openssl-dev curl
diff --git a/.devops/llama-server-rocm.Dockerfile b/.devops/llama-server-rocm.Dockerfile
index af96c332595b1c..763b4cd3f1c2ed 100644
--- a/.devops/llama-server-rocm.Dockerfile
+++ b/.devops/llama-server-rocm.Dockerfile
@@ -6,7 +6,7 @@ ARG ROCM_VERSION=5.6
 # Target the CUDA build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
 
-FROM ${BASE_ROCM_DEV_CONTAINER} as build
+FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 
 # Unless otherwise specified, we make a fat build.
 # List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
diff --git a/.devops/llama-server-vulkan.Dockerfile b/.devops/llama-server-vulkan.Dockerfile
index 49062f84bf3141..13a61ffd8454b6 100644
--- a/.devops/llama-server-vulkan.Dockerfile
+++ b/.devops/llama-server-vulkan.Dockerfile
@@ -1,6 +1,6 @@
 ARG UBUNTU_VERSION=jammy
 
-FROM ubuntu:$UBUNTU_VERSION as build
+FROM ubuntu:$UBUNTU_VERSION AS build
 
 # Install build tools
 RUN apt update && apt install -y git build-essential cmake wget
diff --git a/.devops/llama-server.Dockerfile b/.devops/llama-server.Dockerfile
index a53a5c999c8cd6..b631d580639b2c 100644
--- a/.devops/llama-server.Dockerfile
+++ b/.devops/llama-server.Dockerfile
@@ -1,6 +1,6 @@
 ARG UBUNTU_VERSION=22.04
 
-FROM ubuntu:$UBUNTU_VERSION as build
+FROM ubuntu:$UBUNTU_VERSION AS build
 
 RUN apt-get update && \
     apt-get install -y build-essential git libcurl4-openssl-dev curl
@@ -13,7 +13,7 @@ ENV LLAMA_CURL=1
 
 RUN make -j$(nproc) llama-server
 
-FROM ubuntu:$UBUNTU_VERSION as runtime
+FROM ubuntu:$UBUNTU_VERSION AS runtime
 
 RUN apt-get update && \
     apt-get install -y libcurl4-openssl-dev libgomp1

From e02b597be3702174e7b47b44cd03e1da1553284b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Wed, 17 Jul 2024 23:35:44 +0200
Subject: [PATCH 070/125] lookup: fibonacci hashing, fix crashes (#8548)

---
 common/ngram-cache.h             | 13 ++++++++++---
 examples/lookup/lookup-stats.cpp |  3 +--
 examples/lookup/lookup.cpp       |  1 -
 3 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/common/ngram-cache.h b/common/ngram-cache.h
index e4fa4cbd12f11e..ab4c9b37665460 100644
--- a/common/ngram-cache.h
+++ b/common/ngram-cache.h
@@ -37,11 +37,18 @@ struct llama_ngram {
     }
 };
 
+struct llama_token_hash_function {
+    size_t operator()(const llama_token token) const {
+        // see https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/
+        return token * 11400714819323198485llu;
+    }
+};
+
 struct llama_ngram_hash_function {
     size_t operator()(const llama_ngram & ngram) const {
-        size_t hash = 0;
-        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            hash ^= std::hash<llama_token>{}(ngram.tokens[i]);
+        size_t hash = llama_token_hash_function{}(ngram.tokens[0]);
+        for (int i = 1; i < LLAMA_NGRAM_MAX; ++i) {
+            hash ^= llama_token_hash_function{}(ngram.tokens[i]);
         }
         return hash;
     }
diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp
index 0b171c87273d19..2fe67100e6c03b 100644
--- a/examples/lookup/lookup-stats.cpp
+++ b/examples/lookup/lookup-stats.cpp
@@ -31,7 +31,6 @@ int main(int argc, char ** argv){
 
     // load the model
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
 
     // tokenize the prompt
     std::vector<llama_token> inp;
@@ -65,7 +64,7 @@ int main(int argc, char ** argv){
     }
 
     const int n_input = inp.size();
-    const int n_ctx = params.n_ctx;
+    const int n_ctx = llama_n_ctx(ctx);
 
     int n_drafted = 0;
     int n_accept  = 0;
diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp
index 80ecd925d59627..bb571bac4d7781 100644
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -39,7 +39,6 @@ int main(int argc, char ** argv){
 
     // load the model
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
 
     // tokenize the prompt
     std::vector<llama_token> inp;

From 3807c3de04cde853418033c95e96642876545f3e Mon Sep 17 00:00:00 2001
From: RunningLeon <mnsheng@yeah.net>
Date: Thu, 18 Jul 2024 16:06:22 +0800
Subject: [PATCH 071/125] server : respect `--special` cli arg (#8553)

---
 examples/server/server.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index badeb9121324fa..7813a2957d6bca 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -1182,7 +1182,7 @@ struct server_context {
 
     bool process_token(completion_token_output & result, server_slot & slot) {
         // remember which tokens were sampled - used for repetition penalties during sampling
-        const std::string token_str = llama_token_to_piece(ctx, result.tok, false);
+        const std::string token_str = llama_token_to_piece(ctx, result.tok, params.special);
         slot.sampled = result.tok;
 
         // search stop word and delete it

From 672a6f101839a150180fc28891ebed379c8f2353 Mon Sep 17 00:00:00 2001
From: Brian <mofosyne@gmail.com>
Date: Thu, 18 Jul 2024 20:40:15 +1000
Subject: [PATCH 072/125] convert-*.py: GGUF Naming Convention Refactor and
 Metadata Override Refactor (#7499)

Main thing is that the default output filename will take this form

{name}{parameters}{finetune}{version}{encoding}{kind}

In addition this add and remove some entries in the KV store and adds a metadata class with automatic heuristics capability to derive some values based on model card content

* No Change:
  - Internal GGUF Spec
    - `general.architecture`
    - `general.quantization_version`
    - `general.alignment`
    - `general.file_type`
  - General Model Details
    - `general.name`
    - `general.author`
    - `general.version`
    - `general.description`
  - Licensing details
    - `general.license`
  - Typically represents the converted GGUF repo (Unless made from scratch)
    - `general.url`
  - Model Source during conversion
    - `general.source.url`

* Removed:
  - Model Source during conversion
    - `general.source.huggingface.repository`

* Added:
  - General Model Details
    - `general.organization`
    - `general.finetune`
    - `general.basename`
    - `general.quantized_by`
    - `general.size_label`
  - Licensing details
    - `general.license.name`
    - `general.license.link`
  - Typically represents the converted GGUF repo (Unless made from scratch)
    - `general.doi`
    - `general.uuid`
    - `general.repo_url`
  - Model Source during conversion
    - `general.source.doi`
    - `general.source.uuid`
    - `general.source.repo_url`
  - Base Model Source
    - `general.base_model.count`
    - `general.base_model.{id}.name`
    - `general.base_model.{id}.author`
    - `general.base_model.{id}.version`
    - `general.base_model.{id}.organization`
    - `general.base_model.{id}.url` (Model Website/Paper)
    - `general.base_model.{id}.doi`
    - `general.base_model.{id}.uuid`
    - `general.base_model.{id}.repo_url` (Model Source Repository (git/svn/etc...))
  - Array based KV stores
    - `general.tags`
    - `general.languages`
    - `general.datasets`

---------

Co-authored-by: compilade <git@compilade.net>
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
---
 convert_hf_to_gguf.py            | 187 +++++++-----
 convert_lora_to_gguf.py          |  29 +-
 examples/convert_legacy_llama.py | 235 ++++++++-------
 gguf-py/README.md                |   8 +
 gguf-py/gguf/__init__.py         |   2 +
 gguf-py/gguf/constants.py        |  68 ++++-
 gguf-py/gguf/gguf_writer.py      | 158 ++++++++--
 gguf-py/gguf/metadata.py         | 485 +++++++++++++++++++++++++++++++
 gguf-py/gguf/utility.py          |  69 +++++
 gguf-py/pyproject.toml           |   1 +
 gguf-py/tests/__init__.py        |   1 +
 gguf-py/tests/test_gguf.py       |   7 -
 gguf-py/tests/test_metadata.py   | 158 ++++++++++
 13 files changed, 1177 insertions(+), 231 deletions(-)
 create mode 100644 gguf-py/gguf/metadata.py
 create mode 100644 gguf-py/gguf/utility.py
 create mode 100644 gguf-py/tests/__init__.py
 delete mode 100644 gguf-py/tests/test_gguf.py
 create mode 100755 gguf-py/tests/test_metadata.py

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index c2aba909706d0a..769d49a8b6f0a8 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -48,34 +48,38 @@ class Model:
 
     dir_model: Path
     ftype: gguf.LlamaFileType
+    fname_out: Path | None
     is_big_endian: bool
     endianess: gguf.GGUFEndian
     use_temp_file: bool
     lazy: bool
-    model_name: str | None
     part_names: list[str]
     is_safetensors: bool
     hparams: dict[str, Any]
     block_count: int
     tensor_map: gguf.TensorNameMap
     tensor_names: set[str] | None
-    fname_out: Path
     gguf_writer: gguf.GGUFWriter
+    model_name: str | None
+    metadata_override: Path | None
 
     # subclasses should define this!
     model_arch: gguf.MODEL_ARCH
 
-    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool,
-                 model_name: str | None, split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
+    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path | None, is_big_endian: bool = False,
+                 use_temp_file: bool = False, eager: bool = False,
+                 metadata_override: Path | None = None, model_name: str | None = None,
+                 split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
         if type(self) is Model:
             raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
+
         self.dir_model = dir_model
         self.ftype = ftype
+        self.fname_out = fname_out
         self.is_big_endian = is_big_endian
         self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
         self.use_temp_file = use_temp_file
         self.lazy = not eager
-        self.model_name = model_name
         self.part_names = Model.get_model_part_names(self.dir_model, "model", ".safetensors")
         self.is_safetensors = len(self.part_names) > 0
         if not self.is_safetensors:
@@ -84,6 +88,10 @@ def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path,
         self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
         self.tensor_names = None
+        self.metadata_override = metadata_override
+        self.model_name = model_name
+
+        # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type
         if self.ftype == gguf.LlamaFileType.GUESSED:
             # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
             _, first_tensor = next(self.get_tensors())
@@ -93,10 +101,8 @@ def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path,
             else:
                 logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})")
                 self.ftype = gguf.LlamaFileType.MOSTLY_BF16
-        ftype_up: str = self.ftype.name.partition("_")[2].upper()
-        ftype_lw: str = ftype_up.lower()
-        # allow templating the file name with the output ftype, useful with the "auto" ftype
-        self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up)
+
+        # Configure GGUF Writer
         self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
                                            split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
 
@@ -193,7 +199,6 @@ def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", "
         return new_name
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_block_count(self.block_count)
 
         if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
@@ -250,7 +255,7 @@ def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: i
 
         return False
 
-    def write_tensors(self):
+    def prepare_tensors(self):
         max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
 
         for name, data_torch in self.get_tensors():
@@ -333,9 +338,67 @@ def write_tensors(self):
 
                 self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype)
 
+    def set_type(self):
+        self.gguf_writer.add_type(gguf.GGUFType.MODEL)
+
+    def prepare_metadata(self, vocab_only: bool):
+
+        total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count()
+
+        self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model, self.model_name, total_params)
+
+        # Fallback to model directory name if metadata name is still missing
+        if self.metadata.name is None:
+            self.metadata.name = self.dir_model.name
+
+        # Generate parameter weight class (useful for leader boards) if not yet determined
+        if self.metadata.size_label is None and total_params > 0:
+            self.metadata.size_label = gguf.size_label(total_params, shared_params, expert_params, expert_count)
+
+        # Extract the encoding scheme from the file type name. e.g. 'gguf.LlamaFileType.MOSTLY_Q8_0' --> 'Q8_0'
+        output_type: str = self.ftype.name.partition("_")[2]
+
+        # Filename Output
+        # Note: `not is_dir()` is used because `.is_file()` will not detect
+        #       file template strings as it doesn't actually exist as a file
+        if self.fname_out is not None and not self.fname_out.is_dir():
+            # Output path is a custom defined templated filename
+
+            # Process templated file name with the output ftype, useful with the "auto" ftype
+            self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)
+        else:
+            # Generate default filename based on model specification and available metadata
+            if not vocab_only:
+                fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, self.metadata.size_label, output_type, model_type="LoRA" if total_params < 0 else None)
+            else:
+                fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=None, model_type="vocab")
+
+            # Check if preferred output directory path was provided
+            if self.fname_out is not None and self.fname_out.is_dir():
+                # output path is a directory
+                self.fname_out = self.fname_out / f"{fname_default}.gguf"
+            else:
+                # output in the same directory as the model by default
+                self.fname_out = self.dir_model / f"{fname_default}.gguf"
+
+        self.set_type()
+
+        logger.info("Set meta model")
+        self.metadata.set_gguf_meta_model(self.gguf_writer)
+
+        logger.info("Set model parameters")
+        self.set_gguf_parameters()
+
+        logger.info("Set model tokenizer")
+        self.set_vocab()
+
+        logger.info("Set model quantization version")
+        self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
+
     def write(self):
-        self.write_tensors()
-        self.gguf_writer.write_header_to_file(self.fname_out)
+        self.prepare_tensors()
+        self.prepare_metadata(vocab_only=False)
+        self.gguf_writer.write_header_to_file(path=self.fname_out)
         self.gguf_writer.write_kv_data_to_file()
         self.gguf_writer.write_tensors_to_file(progress=True)
         self.gguf_writer.close()
@@ -343,7 +406,9 @@ def write(self):
     def write_vocab(self):
         if len(self.gguf_writer.tensors) != 1:
             raise ValueError('Splitting the vocabulary is not supported')
-        self.gguf_writer.write_header_to_file(self.fname_out)
+
+        self.prepare_metadata(vocab_only=True)
+        self.gguf_writer.write_header_to_file(path=self.fname_out)
         self.gguf_writer.write_kv_data_to_file()
         self.gguf_writer.close()
 
@@ -780,7 +845,6 @@ class GPTNeoXModel(Model):
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -836,7 +900,6 @@ class BloomModel(Model):
     model_arch = gguf.MODEL_ARCH.BLOOM
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name("Bloom")
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
         self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
@@ -913,7 +976,6 @@ def set_vocab(self):
 
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layers"]
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
         self.gguf_writer.add_embedding_length(self.hparams["d_model"])
         self.gguf_writer.add_block_count(block_count)
@@ -952,7 +1014,6 @@ def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
         head_count = self.hparams["num_attention_heads"]
         head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-        hf_repo = self.hparams.get("_name_or_path", "")
 
         ctx_length = 0
         if "max_sequence_length" in self.hparams:
@@ -965,8 +1026,6 @@ def set_gguf_parameters(self):
             raise ValueError("gguf: can not find ctx length parameter.")
 
         self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
-        self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -990,7 +1049,6 @@ def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
         head_count = self.hparams["num_attention_heads"]
         head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-        hf_repo = self.hparams.get("_name_or_path", "")
 
         ctx_length = 0
         if "max_sequence_length" in self.hparams:
@@ -1002,8 +1060,6 @@ def set_gguf_parameters(self):
         else:
             raise ValueError("gguf: can not find ctx length parameter.")
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
-        self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1117,7 +1173,6 @@ def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
         head_count = self.hparams["num_attention_heads"]
         head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-        hf_repo = self.hparams.get("_name_or_path", "")
 
         ctx_length = 0
         if "max_sequence_length" in self.hparams:
@@ -1129,8 +1184,6 @@ def set_gguf_parameters(self):
         else:
             raise ValueError("gguf: can not find ctx length parameter.")
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
-        self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1189,7 +1242,6 @@ def set_gguf_parameters(self):
         if n_head_kv is None:
             n_head_kv = self.hparams.get("n_head_kv", 1)  # old name
 
-        self.gguf_writer.add_name("Falcon")
         self.gguf_writer.add_context_length(2048)  # not in config.json
         self.gguf_writer.add_tensor_data_layout("jploski")  # qkv tensor transform
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1234,7 +1286,6 @@ class StarCoderModel(Model):
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
-        self.gguf_writer.add_name("StarCoder")
         self.gguf_writer.add_context_length(self.hparams["n_positions"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
         self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
@@ -1269,7 +1320,6 @@ def set_gguf_parameters(self):
 
         block_count = self.hparams["n_layer"]
 
-        self.gguf_writer.add_name("Refact")
         # refact uses Alibi. So this is from config.json which might be used by training.
         self.gguf_writer.add_context_length(self.hparams["n_positions"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
@@ -1324,7 +1374,6 @@ def set_gguf_parameters(self):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -1386,8 +1435,8 @@ def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_
 
         return [(new_name, data_torch)]
 
-    def write_tensors(self):
-        super().write_tensors()
+    def prepare_tensors(self):
+        super().prepare_tensors()
 
         if self._q_norms is not None or self._k_norms is not None:
             # flatten two `list[dict[str, Tensor]]` into a single `list[str]`
@@ -1503,8 +1552,8 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return [(self.map_tensor_name(name), data_torch)]
 
-    def write_tensors(self):
-        super().write_tensors()
+    def prepare_tensors(self):
+        super().prepare_tensors()
 
         if self._experts is not None:
             # flatten `list[dict[str, Tensor]]` into `list[str]`
@@ -1567,7 +1616,6 @@ def __init__(self, *args, **kwargs):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        self.gguf_writer.add_name("Grok")
 
     _experts: list[dict[str, Tensor]] | None = None
 
@@ -1616,7 +1664,6 @@ class DbrxModel(Model):
     def set_gguf_parameters(self):
         ffn_config = self.hparams["ffn_config"]
         attn_config = self.hparams["attn_config"]
-        self.gguf_writer.add_name(self.hparams["model_type"])
         self.gguf_writer.add_block_count(self.hparams["n_layers"])
 
         self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
@@ -1685,7 +1732,6 @@ class MiniCPMModel(Model):
 
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
-        self.gguf_writer.add_name("MiniCPM")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -1755,7 +1801,6 @@ def set_vocab(self):
         self._set_vocab_qwen()
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name("Qwen")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -1831,8 +1876,8 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return [(self.map_tensor_name(name), data_torch)]
 
-    def write_tensors(self):
-        super().write_tensors()
+    def prepare_tensors(self):
+        super().prepare_tensors()
 
         if self._experts is not None:
             # flatten `list[dict[str, Tensor]]` into `list[str]`
@@ -1846,7 +1891,6 @@ class GPT2Model(Model):
     model_arch = gguf.MODEL_ARCH.GPT2
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_block_count(self.hparams["n_layer"])
         self.gguf_writer.add_context_length(self.hparams["n_ctx"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
@@ -1889,7 +1933,6 @@ def set_gguf_parameters(self):
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
 
-        self.gguf_writer.add_name("Phi2")
         self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
 
         self.gguf_writer.add_embedding_length(n_embd)
@@ -2011,7 +2054,6 @@ def set_gguf_parameters(self):
         orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
         rope_dims = n_embd // n_head
 
-        self.gguf_writer.add_name("Phi3")
         self.gguf_writer.add_context_length(max_pos_embds)
         self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds)
         self.gguf_writer.add_embedding_length(n_embd)
@@ -2068,7 +2110,6 @@ def set_gguf_parameters(self):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name("PLaMo")
         self.gguf_writer.add_context_length(4096)  # not in config.json
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
@@ -2113,7 +2154,6 @@ class CodeShellModel(Model):
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
-        self.gguf_writer.add_name("CodeShell")
         self.gguf_writer.add_context_length(self.hparams["n_positions"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
         self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"])
@@ -2272,7 +2312,6 @@ def set_vocab(self):
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name("InternLM2")
         self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
         self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
         self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
@@ -2440,7 +2479,6 @@ def set_gguf_parameters(self):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -2481,7 +2519,6 @@ def set_gguf_parameters(self):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -2556,7 +2593,6 @@ def set_gguf_parameters(self):
         # Fail early for models which don't have a block expansion factor of 2
         assert d_inner == 2 * d_model
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
         self.gguf_writer.add_embedding_length(d_model)
         self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
@@ -2735,7 +2771,6 @@ def set_gguf_parameters(self):
         assert self.block_count == len(self._num_query_heads)
         assert self.block_count == len(self._ffn_dims)
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
         self.gguf_writer.add_block_count(self.block_count)
         self.gguf_writer.add_context_length(self.hparams["max_context_length"])
         self.gguf_writer.add_embedding_length(n_embd)
@@ -2909,8 +2944,8 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return [(self.map_tensor_name(name), data_torch)]
 
-    def write_tensors(self):
-        super().write_tensors()
+    def prepare_tensors(self):
+        super().prepare_tensors()
 
         if self._experts is not None:
             # flatten `list[dict[str, Tensor]]` into `list[str]`
@@ -2988,8 +3023,8 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return [(self.map_tensor_name(name), data_torch)]
 
-    def write_tensors(self):
-        super().write_tensors()
+    def prepare_tensors(self):
+        super().prepare_tensors()
 
         if self._experts is not None:
             # flatten `list[dict[str, Tensor]]` into `list[str]`
@@ -3107,7 +3142,6 @@ def set_vocab(self):
         self.gguf_writer.add_add_eos_token(True)
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name("T5")
         if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
             logger.warning("Couldn't find context length in config.json, assuming default value of 512")
             n_ctx = 512
@@ -3181,7 +3215,6 @@ def set_vocab(self):
         self._set_vocab_gpt2()
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.hparams["n_layer"])
         self.gguf_writer.add_context_length(self.hparams["n_positions"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
@@ -3227,8 +3260,8 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 
         return tensors
 
-    def write_tensors(self):
-        super().write_tensors()
+    def prepare_tensors(self):
+        super().prepare_tensors()
         self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
 
 
@@ -3539,6 +3572,10 @@ def parse_args() -> argparse.Namespace:
         "--no-tensor-first-split", action="store_true",
         help="do not add tensors to the first split (disabled by default)"
     )
+    parser.add_argument(
+        "--metadata", type=Path,
+        help="Specify the path for an authorship metadata override file"
+    )
 
     return parser.parse_args()
 
@@ -3564,7 +3601,10 @@ def split_str_to_n_bytes(split_str: str) -> int:
 def main() -> None:
     args = parse_args()
 
-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+    if args.verbose:
+        logging.basicConfig(level=logging.DEBUG)
+    else:
+        logging.basicConfig(level=logging.INFO)
 
     dir_model = args.model
 
@@ -3585,37 +3625,33 @@ def main() -> None:
         logger.error("Error: Cannot use temp file when splitting")
         sys.exit(1)
 
+    fname_out = None
+
     if args.outfile is not None:
         fname_out = args.outfile
-    else:
-        # output in the same directory as the model by default
-        fname_out = dir_model / 'ggml-model-{ftype}.gguf'
 
     logger.info(f"Loading model: {dir_model.name}")
 
     hparams = Model.load_hparams(dir_model)
 
     with torch.inference_mode():
+        output_type = ftype_map[args.outtype]
+        model_architecture = hparams["architectures"][0]
+
         try:
-            model_class = Model.from_model_architecture(hparams["architectures"][0])
+            model_class = Model.from_model_architecture(model_architecture)
         except NotImplementedError:
-            logger.error(f"Model {hparams['architectures'][0]} is not supported")
+            logger.error(f"Model {model_architecture} is not supported")
             sys.exit(1)
 
-        model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file,
-                                     args.no_lazy, args.model_name, split_max_tensors=args.split_max_tensors,
+        model_instance = model_class(dir_model=dir_model, ftype=output_type, fname_out=fname_out,
+                                     is_big_endian=args.bigendian, use_temp_file=args.use_temp_file,
+                                     eager=args.no_lazy,
+                                     metadata_override=args.metadata, model_name=args.model_name,
+                                     split_max_tensors=args.split_max_tensors,
                                      split_max_size=split_str_to_n_bytes(args.split_max_size), dry_run=args.dry_run,
                                      small_first_shard=args.no_tensor_first_split)
 
-        logger.info("Set model parameters")
-        model_instance.gguf_writer.add_type(gguf.GGUFType.MODEL)
-        model_instance.set_gguf_parameters()
-
-        logger.info("Set model tokenizer")
-        model_instance.set_vocab()
-
-        model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
-
         if args.vocab_only:
             logger.info("Exporting model vocab...")
             model_instance.write_vocab()
@@ -3623,6 +3659,7 @@ def main() -> None:
         else:
             logger.info("Exporting model...")
             model_instance.write()
+            assert model_instance.fname_out is not None
             out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out
             logger.info(f"Model successfully exported to {out_path}")
 
diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py
index 4bb939d45d6bd4..66e8da37cba7cf 100755
--- a/convert_lora_to_gguf.py
+++ b/convert_lora_to_gguf.py
@@ -251,6 +251,10 @@ def parse_args() -> argparse.Namespace:
         "--verbose", action="store_true",
         help="increase output verbosity",
     )
+    parser.add_argument(
+        "--dry-run", action="store_true",
+        help="only print out what will be done, without writing any new files",
+    )
     parser.add_argument(
         "--base", type=Path, required=True,
         help="directory containing base model file",
@@ -300,6 +304,12 @@ def parse_args() -> argparse.Namespace:
     # load base model
     logger.info(f"Loading base model: {dir_base_model.name}")
     hparams = Model.load_hparams(dir_base_model)
+
+    with open(lora_config, "r") as f:
+        lparams: dict[str, Any] = json.load(f)
+
+    alpha: float = lparams["lora_alpha"]
+
     with torch.inference_mode():
         try:
             model_class = Model.from_model_architecture(hparams["architectures"][0])
@@ -310,6 +320,14 @@ def parse_args() -> argparse.Namespace:
         class LoraModel(model_class):
             model_arch = model_class.model_arch
 
+            def set_type(self):
+                self.gguf_writer.add_type(gguf.GGUFType.ADAPTER)
+                self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
+
+            def set_gguf_parameters(self):
+                self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, float(alpha))
+                super().set_gguf_parameters()
+
             def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
                 tensor_map: dict[str, PartialLoraTensor] = {}
 
@@ -357,18 +375,9 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
             is_big_endian=args.bigendian,
             use_temp_file=False,
             eager=args.no_lazy,
-            model_name=None,
+            dry_run=args.dry_run,
         )
 
-        with open(lora_config, "r") as f:
-            lparams: dict[str, Any] = json.load(f)
-
-        alpha = lparams["lora_alpha"]
-
-        model_instance.gguf_writer.add_string(gguf.Keys.General.TYPE, gguf.GGUFType.ADAPTER)
-        model_instance.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
-        model_instance.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, float(alpha))
-        model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
         logger.info("Exporting model...")
         model_instance.write()
         logger.info(f"Model successfully exported to {model_instance.fname_out}")
diff --git a/examples/convert_legacy_llama.py b/examples/convert_legacy_llama.py
index c2c73e8ad39ec7..9ab9ab06edf8fa 100755
--- a/examples/convert_legacy_llama.py
+++ b/examples/convert_legacy_llama.py
@@ -24,7 +24,7 @@
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
 from dataclasses import dataclass
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar, Optional
+from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar
 
 import numpy as np
 
@@ -346,42 +346,6 @@ def load(model_plus: ModelPlus) -> Params:
         return params
 
 
-@dataclass
-class Metadata:
-    name: Optional[str] = None
-    author: Optional[str] = None
-    version: Optional[str] = None
-    url: Optional[str] = None
-    description: Optional[str] = None
-    license: Optional[str] = None
-    source_url: Optional[str] = None
-    source_hf_repo: Optional[str] = None
-
-    @staticmethod
-    def load(metadata_path: Path) -> Metadata:
-        if metadata_path is None or not metadata_path.exists():
-            return Metadata()
-
-        with open(metadata_path, 'r') as file:
-            data = json.load(file)
-
-        # Create a new Metadata instance
-        metadata = Metadata()
-
-        # Assigning values to Metadata attributes if they exist in the JSON file
-        # This is based on LLM_KV_NAMES mapping in llama.cpp
-        metadata.name = data.get("general.name")
-        metadata.author = data.get("general.author")
-        metadata.version = data.get("general.version")
-        metadata.url = data.get("general.url")
-        metadata.description = data.get("general.description")
-        metadata.license = data.get("general.license")
-        metadata.source_url = data.get("general.source.url")
-        metadata.source_hf_repo = data.get("general.source.huggingface.repository")
-
-        return metadata
-
-
 #
 # data loading
 # TODO: reuse (probably move to gguf.py?)
@@ -806,7 +770,7 @@ class OutputFile:
     def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE):
         self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
 
-    def add_meta_model(self, params: Params, metadata: Metadata | None) -> None:
+    def add_meta_model(self, params: Params, metadata: gguf.Metadata | None) -> None:
         # Metadata About The Model And Its Provenence
         name = "LLaMA"
         if metadata is not None and metadata.name is not None:
@@ -824,16 +788,73 @@ def add_meta_model(self, params: Params, metadata: Metadata | None) -> None:
                 self.gguf.add_author(metadata.author)
             if metadata.version is not None:
                 self.gguf.add_version(metadata.version)
-            if metadata.url is not None:
-                self.gguf.add_url(metadata.url)
+            if metadata.organization is not None:
+                self.gguf.add_organization(metadata.organization)
+
+            if metadata.finetune is not None:
+                self.gguf.add_finetune(metadata.finetune)
+            if metadata.basename is not None:
+                self.gguf.add_basename(metadata.basename)
+
             if metadata.description is not None:
                 self.gguf.add_description(metadata.description)
+            if metadata.quantized_by is not None:
+                self.gguf.add_quantized_by(metadata.quantized_by)
+
+            if metadata.size_label is not None:
+                self.gguf.add_size_label(metadata.size_label)
+
             if metadata.license is not None:
-                self.gguf.add_licence(metadata.license)
+                self.gguf.add_license(metadata.license)
+            if metadata.license_name is not None:
+                self.gguf.add_license_name(metadata.license_name)
+            if metadata.license_link is not None:
+                self.gguf.add_license_link(metadata.license_link)
+
+            if metadata.url is not None:
+                self.gguf.add_url(metadata.url)
+            if metadata.doi is not None:
+                self.gguf.add_doi(metadata.doi)
+            if metadata.uuid is not None:
+                self.gguf.add_uuid(metadata.uuid)
+            if metadata.repo_url is not None:
+                self.gguf.add_repo_url(metadata.repo_url)
+
             if metadata.source_url is not None:
                 self.gguf.add_source_url(metadata.source_url)
-            if metadata.source_hf_repo is not None:
-                self.gguf.add_source_hf_repo(metadata.source_hf_repo)
+            if metadata.source_doi is not None:
+                self.gguf.add_source_doi(metadata.source_doi)
+            if metadata.source_uuid is not None:
+                self.gguf.add_source_uuid(metadata.source_uuid)
+            if metadata.source_repo_url is not None:
+                self.gguf.add_source_repo_url(metadata.source_repo_url)
+
+            if metadata.base_models is not None:
+                self.gguf.add_base_model_count(len(metadata.base_models))
+                for key, base_model_entry in enumerate(metadata.base_models):
+                    if "name" in base_model_entry:
+                        self.gguf.add_base_model_name(key, base_model_entry["name"])
+                    if "author" in base_model_entry:
+                        self.gguf.add_base_model_author(key, base_model_entry["author"])
+                    if "version" in base_model_entry:
+                        self.gguf.add_base_model_version(key, base_model_entry["version"])
+                    if "organization" in base_model_entry:
+                        self.gguf.add_base_model_organization(key, base_model_entry["organization"])
+                    if "url" in base_model_entry:
+                        self.gguf.add_base_model_url(key, base_model_entry["url"])
+                    if "doi" in base_model_entry:
+                        self.gguf.add_base_model_doi(key, base_model_entry["doi"])
+                    if "uuid" in base_model_entry:
+                        self.gguf.add_base_model_uuid(key, base_model_entry["uuid"])
+                    if "repo_url" in base_model_entry:
+                        self.gguf.add_base_model_repo_url(key, base_model_entry["repo_url"])
+
+            if metadata.tags is not None:
+                self.gguf.add_tags(metadata.tags)
+            if metadata.languages is not None:
+                self.gguf.add_languages(metadata.languages)
+            if metadata.datasets is not None:
+                self.gguf.add_datasets(metadata.datasets)
 
     def add_meta_arch(self, params: Params) -> None:
         # Metadata About The Neural Architecture Itself
@@ -944,7 +965,7 @@ def close(self) -> None:
     @staticmethod
     def write_vocab_only(
         fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
-        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: Metadata | None = None,
+        endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False, metadata: gguf.Metadata | None = None,
     ) -> None:
         check_vocab_size(params, vocab, pad_vocab=pad_vocab)
 
@@ -978,7 +999,7 @@ def write_all(
         fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab,
         concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
         pad_vocab: bool = False,
-        metadata: Metadata | None = None,
+        metadata: gguf.Metadata | None = None,
     ) -> None:
         check_vocab_size(params, vocab, pad_vocab=pad_vocab)
 
@@ -1021,35 +1042,32 @@ def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileT
     raise ValueError(f"Unexpected combination of types: {name_to_type}")
 
 
-def model_parameter_count(model: LazyModel) -> int:
-    total_model_parameters = 0
-    for i, (name, lazy_tensor) in enumerate(model.items()):
-        sum_weights_in_tensor = 1
+def per_model_weight_count_estimation(tensors: Iterable[tuple[str, LazyTensor]]) -> tuple[int, int, int]:
+    total_params = 0
+    shared_params = 0
+    expert_params = 0
+
+    for name, lazy_tensor in tensors:
+        # We don't need these
+        if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
+            continue
+
+        # Got A Tensor
+        sum_weights_in_tensor: int = 1
+
+        # Tensor Volume
         for dim in lazy_tensor.shape:
             sum_weights_in_tensor *= dim
-        total_model_parameters += sum_weights_in_tensor
-    return total_model_parameters
-
-
-def model_parameter_count_rounded_notation(model_params_count: int) -> str:
-    if model_params_count > 1e12 :
-        # Trillions Of Parameters
-        scaled_model_params = model_params_count * 1e-12
-        scale_suffix = "T"
-    elif model_params_count > 1e9 :
-        # Billions Of Parameters
-        scaled_model_params = model_params_count * 1e-9
-        scale_suffix = "B"
-    elif model_params_count > 1e6 :
-        # Millions Of Parameters
-        scaled_model_params = model_params_count * 1e-6
-        scale_suffix = "M"
-    else:
-        # Thousands Of Parameters
-        scaled_model_params = model_params_count * 1e-3
-        scale_suffix = "K"
 
-    return f"{round(scaled_model_params)}{scale_suffix}"
+        if ".experts." in name:
+            if ".experts.0." in name:
+                expert_params += sum_weights_in_tensor
+        else:
+            shared_params += sum_weights_in_tensor
+
+        total_params += sum_weights_in_tensor
+
+    return total_params, shared_params, expert_params
 
 
 def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
@@ -1231,34 +1249,24 @@ def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) ->
         return vocab, special_vocab
 
 
-def default_convention_outfile(file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> str:
-    quantization = {
+def default_convention_outfile(file_type: GGMLFileType, expert_count: int | None, model_params_count: tuple[int, int, int], metadata: gguf.Metadata) -> str:
+    name = metadata.name if metadata.name is not None else None
+    basename = metadata.basename if metadata.basename is not None else None
+    finetune = metadata.finetune if metadata.finetune is not None else None
+    version = metadata.version if metadata.version is not None else None
+    size_label = metadata.size_label if metadata.size_label is not None else gguf.size_label(*model_params_count, expert_count=expert_count or 0)
+
+    output_type = {
         GGMLFileType.AllF32:    "F32",
         GGMLFileType.MostlyF16: "F16",
         GGMLFileType.MostlyQ8_0: "Q8_0",
     }[file_type]
 
-    parameters = model_parameter_count_rounded_notation(model_params_count)
-
-    expert_count = ""
-    if params.n_experts is not None:
-        expert_count = f"{params.n_experts}x"
-
-    version = ""
-    if metadata is not None and metadata.version is not None:
-        version = f"-{metadata.version}"
+    return gguf.naming_convention(name, basename, finetune, version, size_label, output_type)
 
-    name = "ggml-model"
-    if metadata is not None and metadata.name is not None:
-        name = metadata.name
-    elif params.path_model is not None:
-        name = params.path_model.name
 
-    return f"{name}{version}-{expert_count}{parameters}-{quantization}"
-
-
-def default_outfile(model_paths: list[Path], file_type: GGMLFileType, params: Params, model_params_count: int, metadata: Metadata) -> Path:
-    default_filename = default_convention_outfile(file_type, params, model_params_count, metadata)
+def default_outfile(model_paths: list[Path], file_type: GGMLFileType, expert_count: int | None, model_params_count: tuple[int, int, int], metadata: gguf.Metadata) -> Path:
+    default_filename = default_convention_outfile(file_type, expert_count, model_params_count, metadata)
     ret = model_paths[0].parent / f"{default_filename}.gguf"
     if ret in model_paths:
         logger.error(
@@ -1297,8 +1305,9 @@ def main(args_in: list[str] | None = None) -> None:
     parser.add_argument("--pad-vocab",    action="store_true",    help="add pad tokens when model vocab expects more than tokenizer metadata provides")
     parser.add_argument("--skip-unknown", action="store_true",    help="skip unknown tensor names instead of failing")
     parser.add_argument("--verbose",      action="store_true",    help="increase output verbosity")
-    parser.add_argument("--metadata",     type=Path,              help="Specify the path for a metadata file")
+    parser.add_argument("--metadata",     type=Path,              help="Specify the path for an authorship metadata override file")
     parser.add_argument("--get-outfile",  action="store_true",    help="get calculated default outfile name")
+    parser.add_argument("--model-name",   type=str, default=None, help="name of the model")
 
     args = parser.parse_args(args_in)
 
@@ -1310,32 +1319,36 @@ def main(args_in: list[str] | None = None) -> None:
     else:
         logging.basicConfig(level=logging.INFO)
 
-    metadata = Metadata.load(args.metadata)
+    model_name = args.model_name
+    dir_model = args.model
+
+    metadata = gguf.Metadata.load(args.metadata, dir_model, model_name)
 
     if args.get_outfile:
-        model_plus = load_some_model(args.model)
+        model_plus = load_some_model(dir_model)
         params = Params.load(model_plus)
-        model   = convert_model_names(model_plus.model, params, args.skip_unknown)
-        model_params_count = model_parameter_count(model_plus.model)
-        ftype   = pick_output_type(model, args.outtype)
-        print(f"{default_convention_outfile(ftype, params, model_params_count, metadata)}") # noqa: NP100
+        model = convert_model_names(model_plus.model, params, args.skip_unknown)
+        model_params_count = per_model_weight_count_estimation(model_plus.model.items())
+        ftype = pick_output_type(model, args.outtype)
+
+        if (metadata is None or metadata.name is None) and params.path_model is not None:
+            metadata.name = params.path_model.name
+
+        print(f"{default_convention_outfile(ftype, params.n_experts, model_params_count, metadata)}") # noqa: NP100
         return
 
     if args.no_vocab and args.vocab_only:
         raise ValueError("--vocab-only does not make sense with --no-vocab")
 
     if args.dump_single:
-        model_plus = lazy_load_file(args.model)
+        model_plus = lazy_load_file(dir_model)
         do_dump_model(model_plus)
         return
 
     if not args.vocab_only:
-        model_plus = load_some_model(args.model)
+        model_plus = load_some_model(dir_model)
     else:
-        model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)
-
-    model_params_count = model_parameter_count(model_plus.model)
-    logger.info(f"model parameters count : {model_params_count} ({model_parameter_count_rounded_notation(model_params_count)})")
+        model_plus = ModelPlus(model = {}, paths = [dir_model / 'dummy'], format = 'none', vocab = None)
 
     if args.dump:
         do_dump_model(model_plus)
@@ -1368,7 +1381,7 @@ def main(args_in: list[str] | None = None) -> None:
         logger.info(f"params = {params}")
 
     model_parent_path = model_plus.paths[0].parent
-    vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
+    vocab_path = Path(args.vocab_dir or dir_model or model_parent_path)
     vocab_factory = VocabFactory(vocab_path)
     vocab_types = None if args.no_vocab else args.vocab_type.split(",")
     vocab, special_vocab = vocab_factory.load_vocab(vocab_types, model_parent_path)
@@ -1399,13 +1412,21 @@ def main(args_in: list[str] | None = None) -> None:
 
     assert params is not None
 
+    if metadata.name is None and params.path_model is not None:
+        metadata.name = params.path_model.name
+
+    model_params_count = per_model_weight_count_estimation(model_plus.model.items())
+    logger.info(f"model parameters count : {model_params_count} ({gguf.model_weight_count_rounded_notation(model_params_count[0])})")
+
     logger.info(f"Vocab info: {vocab}")
     logger.info(f"Special vocab info: {special_vocab}")
     model   = model_plus.model
     model   = convert_model_names(model, params, args.skip_unknown)
     ftype   = pick_output_type(model, args.outtype)
     model   = convert_to_output_type(model, ftype)
-    outfile = args.outfile or default_outfile(model_plus.paths, ftype, params, model_params_count, metadata)
+    outfile = args.outfile or default_outfile(model_plus.paths, ftype, params.n_experts, model_params_count, metadata=metadata)
+
+    metadata.size_label = gguf.size_label(*model_params_count, expert_count=params.n_experts or 0)
 
     params.ftype = ftype
     logger.info(f"Writing {outfile}, format {ftype}")
diff --git a/gguf-py/README.md b/gguf-py/README.md
index 9dd888f3180d14..24af96a17a5bb2 100644
--- a/gguf-py/README.md
+++ b/gguf-py/README.md
@@ -78,5 +78,13 @@ python -m build
 python -m twine upload dist/*
 ```
 
+## Run Unit Tests
+
+From root of this repository you can run this command to run all the unit tests
+
+```bash
+python -m unittest discover ./gguf-py -v
+```
+
 ## TODO
 - [ ] Include conversion scripts as command line entry points in this package.
diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py
index ea5146b161bc88..243defc4c1ca42 100644
--- a/gguf-py/gguf/__init__.py
+++ b/gguf-py/gguf/__init__.py
@@ -5,3 +5,5 @@
 from .quants import *
 from .tensor_mapping import *
 from .vocab import *
+from .utility import *
+from .metadata import *
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 5eb3df706e6e29..e343c2ef1659af 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -19,19 +19,60 @@
 
 class Keys:
     class General:
-        TYPE                 = "general.type"
-        ARCHITECTURE         = "general.architecture"
-        QUANTIZATION_VERSION = "general.quantization_version"
-        ALIGNMENT            = "general.alignment"
-        NAME                 = "general.name"
-        AUTHOR               = "general.author"
-        VERSION              = "general.version"
-        URL                  = "general.url"
-        DESCRIPTION          = "general.description"
-        LICENSE              = "general.license"
-        SOURCE_URL           = "general.source.url"
-        SOURCE_HF_REPO       = "general.source.huggingface.repository"
-        FILE_TYPE            = "general.file_type"
+        TYPE                       = "general.type"
+        ARCHITECTURE               = "general.architecture"
+        QUANTIZATION_VERSION       = "general.quantization_version"
+        ALIGNMENT                  = "general.alignment"
+        FILE_TYPE                  = "general.file_type"
+
+        # Authorship Metadata
+        NAME                       = "general.name"
+        AUTHOR                     = "general.author"
+        VERSION                    = "general.version"
+        ORGANIZATION               = "general.organization"
+
+        FINETUNE                   = "general.finetune"
+        BASENAME                   = "general.basename"
+
+        DESCRIPTION                = "general.description"
+        QUANTIZED_BY               = "general.quantized_by"
+
+        SIZE_LABEL                 = "general.size_label"
+
+        # Licensing details
+        LICENSE                    = "general.license"
+        LICENSE_NAME               = "general.license.name"
+        LICENSE_LINK               = "general.license.link"
+
+        # Typically represents the converted GGUF repo (Unless native)
+        URL                        = "general.url" # Model Website/Paper
+        DOI                        = "general.doi"
+        UUID                       = "general.uuid"
+        REPO_URL                   = "general.repo_url" # Model Source Repository (git/svn/etc...)
+
+        # Model Source during conversion
+        SOURCE_URL                 = "general.source.url" # Model Website/Paper
+        SOURCE_DOI                 = "general.source.doi"
+        SOURCE_UUID                = "general.source.uuid"
+        SOURCE_REPO_URL            = "general.source.repo_url" # Model Source Repository (git/svn/etc...)
+
+        # Base Model Source. There can be more than one source if it's a merged
+        # model like with 'Mistral-7B-Merge-14-v0.1'. This will assist in
+        # tracing linage of models as it is finetuned or merged over time.
+        BASE_MODEL_COUNT           = "general.base_model.count"
+        BASE_MODEL_NAME            = "general.base_model.{id}.name"
+        BASE_MODEL_AUTHOR          = "general.base_model.{id}.author"
+        BASE_MODEL_VERSION         = "general.base_model.{id}.version"
+        BASE_MODEL_ORGANIZATION    = "general.base_model.{id}.organization"
+        BASE_MODEL_URL             = "general.base_model.{id}.url" # Model Website/Paper
+        BASE_MODEL_DOI             = "general.base_model.{id}.doi"
+        BASE_MODEL_UUID            = "general.base_model.{id}.uuid"
+        BASE_MODEL_REPO_URL        = "general.base_model.{id}.repo_url" # Model Source Repository (git/svn/etc...)
+
+        # Array based KV stores
+        TAGS                       = "general.tags"
+        LANGUAGES                  = "general.languages"
+        DATASETS                   = "general.datasets"
 
     class LLM:
         VOCAB_SIZE                        = "{arch}.vocab_size"
@@ -1233,7 +1274,6 @@ def get_type(val: Any) -> GGUFValueType:
 KEY_GENERAL_DESCRIPTION          = Keys.General.DESCRIPTION
 KEY_GENERAL_LICENSE              = Keys.General.LICENSE
 KEY_GENERAL_SOURCE_URL           = Keys.General.SOURCE_URL
-KEY_GENERAL_SOURCE_HF_REPO       = Keys.General.SOURCE_HF_REPO
 KEY_GENERAL_FILE_TYPE            = Keys.General.FILE_TYPE
 
 # LLM
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index b0197961d46a83..ba6f53cda25a18 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -7,6 +7,7 @@
 import tempfile
 from dataclasses import dataclass
 from enum import Enum, auto
+from math import prod
 from pathlib import Path
 from io import BufferedWriter
 from typing import IO, Any, Sequence, Mapping
@@ -106,6 +107,53 @@ def __init__(
 
         self.add_architecture()
 
+    def get_total_parameter_count(self) -> tuple[int, int, int, int]:
+        total_params = 0
+        shared_params = 0
+        expert_params = 0
+
+        expert_sum = 0
+        n_expert_tensors = 0
+
+        last_lora_a: tuple[str, TensorInfo] | None = None
+
+        for tensors in self.tensors:
+            for name, info in tensors.items():
+
+                shape = info.shape
+
+                if name.endswith(".lora_a"):
+                    last_lora_a = (name, info)
+                    continue
+                elif name.endswith(".lora_b"):
+                    if last_lora_a is None or last_lora_a[0] != name[:-1] + "a":
+                        # Bail when the LoRA pair can't be found trivially
+                        logger.warning("can't measure LoRA size correctly, tensor order is unusual")
+                        return 0, 0, 0, 0
+                    else:
+                        shape = (*shape[:-1], last_lora_a[1].shape[-1])
+
+                size = prod(shape)
+
+                if "_exps." in name:
+                    expert_params += (size // shape[-3])
+                    expert_sum += shape[-3]
+                    n_expert_tensors += 1
+                else:
+                    shared_params += size
+
+                total_params += size
+
+        # Hopefully this should work even for variable-expert-count models
+        expert_count = (expert_sum // n_expert_tensors) if n_expert_tensors > 0 else 0
+
+        # Negate the total to signal it's likely not exact
+        if last_lora_a is not None:
+            total_params = -total_params
+
+        # NOTE: keep the output in the same order as accepted by 'size_label' in gguf-py/gguf/utility.py
+        return total_params, shared_params, expert_params, expert_count
+
     def format_shard_names(self, path: Path) -> list[Path]:
         if len(self.tensors) == 1:
             return [path]
@@ -115,6 +163,7 @@ def open_output_file(self, path: Path | None = None) -> None:
         if self.state is WriterState.EMPTY and self.fout is not None and (path is None or path == self.path):
             # allow calling this multiple times as long as the path is the same
             return
+
         if self.state is not WriterState.NO_FILE:
             raise ValueError(f'Expected output file to be not yet opened, got {self.state}')
 
@@ -136,6 +185,8 @@ def print_plan(self) -> list[Path]:
 
         if self.dry_run:
             logger.info("Dry run, not writing files")
+            for name in filenames:
+                print(name)  # noqa: NP100
             exit()
 
         return filenames
@@ -430,43 +481,114 @@ def add_type(self, type_name: str) -> None:
     def add_architecture(self) -> None:
         self.add_string(Keys.General.ARCHITECTURE, self.arch)
 
+    def add_quantization_version(self, quantization_version: int) -> None:
+        self.add_uint32(Keys.General.QUANTIZATION_VERSION, quantization_version)
+
+    def add_custom_alignment(self, alignment: int) -> None:
+        self.data_alignment = alignment
+        self.add_uint32(Keys.General.ALIGNMENT, alignment)
+
+    def add_file_type(self, ftype: int) -> None:
+        self.add_uint32(Keys.General.FILE_TYPE, ftype)
+
+    def add_name(self, name: str) -> None:
+        self.add_string(Keys.General.NAME, name)
+
     def add_author(self, author: str) -> None:
         self.add_string(Keys.General.AUTHOR, author)
 
     def add_version(self, version: str) -> None:
         self.add_string(Keys.General.VERSION, version)
 
-    def add_tensor_data_layout(self, layout: str) -> None:
-        self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
+    def add_organization(self, organization: str) -> None:
+        self.add_string(Keys.General.ORGANIZATION, organization)
 
-    def add_url(self, url: str) -> None:
-        self.add_string(Keys.General.URL, url)
+    def add_finetune(self, finetune: str) -> None:
+        self.add_string(Keys.General.FINETUNE, finetune)
+
+    def add_basename(self, basename: str) -> None:
+        self.add_string(Keys.General.BASENAME, basename)
 
     def add_description(self, description: str) -> None:
         self.add_string(Keys.General.DESCRIPTION, description)
 
-    def add_licence(self, licence: str) -> None:
-        self.add_string(Keys.General.LICENSE, licence)
+    def add_quantized_by(self, quantized: str) -> None:
+        self.add_string(Keys.General.QUANTIZED_BY, quantized)
+
+    def add_size_label(self, size_label: str) -> None:
+        self.add_string(Keys.General.SIZE_LABEL, size_label)
+
+    def add_license(self, license: str) -> None:
+        self.add_string(Keys.General.LICENSE, license)
+
+    def add_license_name(self, license: str) -> None:
+        self.add_string(Keys.General.LICENSE_NAME, license)
+
+    def add_license_link(self, license: str) -> None:
+        self.add_string(Keys.General.LICENSE_LINK, license)
+
+    def add_url(self, url: str) -> None:
+        self.add_string(Keys.General.URL, url)
+
+    def add_doi(self, doi: str) -> None:
+        self.add_string(Keys.General.DOI, doi)
+
+    def add_uuid(self, uuid: str) -> None:
+        self.add_string(Keys.General.UUID, uuid)
+
+    def add_repo_url(self, repo_url: str) -> None:
+        self.add_string(Keys.General.REPO_URL, repo_url)
 
     def add_source_url(self, url: str) -> None:
         self.add_string(Keys.General.SOURCE_URL, url)
 
-    def add_source_hf_repo(self, repo: str) -> None:
-        self.add_string(Keys.General.SOURCE_HF_REPO, repo)
+    def add_source_doi(self, doi: str) -> None:
+        self.add_string(Keys.General.SOURCE_DOI, doi)
 
-    def add_file_type(self, ftype: int) -> None:
-        self.add_uint32(Keys.General.FILE_TYPE, ftype)
+    def add_source_uuid(self, uuid: str) -> None:
+        self.add_string(Keys.General.SOURCE_UUID, uuid)
 
-    def add_name(self, name: str) -> None:
-        self.add_string(Keys.General.NAME, name)
+    def add_source_repo_url(self, repo_url: str) -> None:
+        self.add_string(Keys.General.SOURCE_REPO_URL, repo_url)
 
-    def add_quantization_version(self, quantization_version: int) -> None:
-        self.add_uint32(
-            Keys.General.QUANTIZATION_VERSION, quantization_version)
+    def add_base_model_count(self, source_count: int) -> None:
+        self.add_uint32(Keys.General.BASE_MODEL_COUNT, source_count)
 
-    def add_custom_alignment(self, alignment: int) -> None:
-        self.data_alignment = alignment
-        self.add_uint32(Keys.General.ALIGNMENT, alignment)
+    def add_base_model_name(self, source_id: int, name: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_NAME.format(id=source_id), name)
+
+    def add_base_model_author(self, source_id: int, author: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_AUTHOR.format(id=source_id), author)
+
+    def add_base_model_version(self, source_id: int, version: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_VERSION.format(id=source_id), version)
+
+    def add_base_model_organization(self, source_id: int, organization: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_ORGANIZATION.format(id=source_id), organization)
+
+    def add_base_model_url(self, source_id: int, url: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_URL.format(id=source_id), url)
+
+    def add_base_model_doi(self, source_id: int, doi: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_DOI.format(id=source_id), doi)
+
+    def add_base_model_uuid(self, source_id: int, uuid: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_UUID.format(id=source_id), uuid)
+
+    def add_base_model_repo_url(self, source_id: int, repo_url: str) -> None:
+        self.add_string(Keys.General.BASE_MODEL_REPO_URL.format(id=source_id), repo_url)
+
+    def add_tags(self, tags: Sequence[str]) -> None:
+        self.add_array(Keys.General.TAGS, tags)
+
+    def add_languages(self, languages: Sequence[str]) -> None:
+        self.add_array(Keys.General.LANGUAGES, languages)
+
+    def add_datasets(self, datasets: Sequence[str]) -> None:
+        self.add_array(Keys.General.DATASETS, datasets)
+
+    def add_tensor_data_layout(self, layout: str) -> None:
+        self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
 
     def add_vocab_size(self, size: int) -> None:
         self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size)
diff --git a/gguf-py/gguf/metadata.py b/gguf-py/gguf/metadata.py
new file mode 100644
index 00000000000000..be297f2426f789
--- /dev/null
+++ b/gguf-py/gguf/metadata.py
@@ -0,0 +1,485 @@
+from __future__ import annotations
+
+import re
+import json
+import yaml
+import logging
+from pathlib import Path
+from typing import Any, Literal, Optional
+from dataclasses import dataclass
+
+from .constants import Keys
+
+import gguf
+
+logger = logging.getLogger("metadata")
+
+
+@dataclass
+class Metadata:
+    # Authorship Metadata to be written to GGUF KV Store
+    name: Optional[str] = None
+    author: Optional[str] = None
+    version: Optional[str] = None
+    organization: Optional[str] = None
+    finetune: Optional[str] = None
+    basename: Optional[str] = None
+    description: Optional[str] = None
+    quantized_by: Optional[str] = None
+    size_label: Optional[str] = None
+    url: Optional[str] = None
+    doi: Optional[str] = None
+    uuid: Optional[str] = None
+    repo_url: Optional[str] = None
+    source_url: Optional[str] = None
+    source_doi: Optional[str] = None
+    source_uuid: Optional[str] = None
+    source_repo_url: Optional[str] = None
+    license: Optional[str] = None
+    license_name: Optional[str] = None
+    license_link: Optional[str] = None
+    base_models: Optional[list[dict]] = None
+    tags: Optional[list[str]] = None
+    languages: Optional[list[str]] = None
+    datasets: Optional[list[str]] = None
+
+    @staticmethod
+    def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Path] = None, model_name: Optional[str] = None, total_params: int = 0) -> Metadata:
+        # This grabs as many contextual authorship metadata as possible from the model repository
+        # making any conversion as required to match the gguf kv store metadata format
+        # as well as giving users the ability to override any authorship metadata that may be incorrect
+
+        # Create a new Metadata instance
+        metadata = Metadata()
+
+        model_card = Metadata.load_model_card(model_path)
+        hf_params = Metadata.load_hf_parameters(model_path)
+
+        # heuristics
+        metadata = Metadata.apply_metadata_heuristic(metadata, model_card, hf_params, model_path, total_params)
+
+        # Metadata Override File Provided
+        # This is based on LLM_KV_NAMES mapping in llama.cpp
+        metadata_override = Metadata.load_metadata_override(metadata_override_path)
+
+        metadata.author          = metadata_override.get(Keys.General.AUTHOR,          metadata.author)
+        metadata.version         = metadata_override.get(Keys.General.VERSION,         metadata.version)
+        metadata.organization    = metadata_override.get(Keys.General.ORGANIZATION,    metadata.organization)
+
+        metadata.finetune        = metadata_override.get(Keys.General.FINETUNE,        metadata.finetune)
+        metadata.basename        = metadata_override.get(Keys.General.BASENAME,        metadata.basename)
+
+        metadata.description     = metadata_override.get(Keys.General.DESCRIPTION,     metadata.description)
+        metadata.quantized_by    = metadata_override.get(Keys.General.QUANTIZED_BY,    metadata.quantized_by)
+
+        metadata.size_label      = metadata_override.get(Keys.General.SIZE_LABEL,      metadata.size_label)
+        metadata.license_name    = metadata_override.get(Keys.General.LICENSE_NAME,    metadata.license_name)
+        metadata.license_link    = metadata_override.get(Keys.General.LICENSE_LINK,    metadata.license_link)
+
+        metadata.url             = metadata_override.get(Keys.General.URL,             metadata.url)
+        metadata.doi             = metadata_override.get(Keys.General.DOI,             metadata.doi)
+        metadata.uuid            = metadata_override.get(Keys.General.UUID,            metadata.uuid)
+        metadata.repo_url        = metadata_override.get(Keys.General.REPO_URL,        metadata.repo_url)
+
+        metadata.source_url      = metadata_override.get(Keys.General.SOURCE_URL,      metadata.source_url)
+        metadata.source_doi      = metadata_override.get(Keys.General.SOURCE_DOI,      metadata.source_doi)
+        metadata.source_uuid     = metadata_override.get(Keys.General.SOURCE_UUID,     metadata.source_uuid)
+        metadata.source_repo_url = metadata_override.get(Keys.General.SOURCE_REPO_URL, metadata.source_repo_url)
+
+        # Base Models is received here as an array of models
+        metadata.base_models     = metadata_override.get("general.base_models",        metadata.base_models)
+
+        metadata.tags            = metadata_override.get(Keys.General.TAGS,            metadata.tags)
+        metadata.languages       = metadata_override.get(Keys.General.LANGUAGES,       metadata.languages)
+        metadata.datasets        = metadata_override.get(Keys.General.DATASETS,        metadata.datasets)
+
+        # Direct Metadata Override (via direct cli argument)
+        if model_name is not None:
+            metadata.name = model_name
+
+        return metadata
+
+    @staticmethod
+    def load_metadata_override(metadata_override_path: Optional[Path] = None) -> dict[str, Any]:
+        if metadata_override_path is None or not metadata_override_path.is_file():
+            return {}
+
+        with open(metadata_override_path, "r", encoding="utf-8") as f:
+            return json.load(f)
+
+    @staticmethod
+    def load_model_card(model_path: Optional[Path] = None) -> dict[str, Any]:
+        if model_path is None or not model_path.is_dir():
+            return {}
+
+        model_card_path = model_path / "README.md"
+
+        if not model_card_path.is_file():
+            return {}
+
+        # The model card metadata is assumed to always be in YAML
+        # ref: https://github.com/huggingface/transformers/blob/a5c642fe7a1f25d3bdcd76991443ba6ff7ee34b2/src/transformers/modelcard.py#L468-L473
+        with open(model_card_path, "r", encoding="utf-8") as f:
+            if f.readline() == "---\n":
+                raw = f.read().partition("---\n")[0]
+                data = yaml.safe_load(raw)
+                if isinstance(data, dict):
+                    return data
+                else:
+                    logger.error(f"while reading YAML model card frontmatter, data is {type(data)} instead of dict")
+                    return {}
+            else:
+                return {}
+
+    @staticmethod
+    def load_hf_parameters(model_path: Optional[Path] = None) -> dict[str, Any]:
+        if model_path is None or not model_path.is_dir():
+            return {}
+
+        config_path = model_path / "config.json"
+
+        if not config_path.is_file():
+            return {}
+
+        with open(config_path, "r", encoding="utf-8") as f:
+            return json.load(f)
+
+    @staticmethod
+    def id_to_title(string):
+        # Convert capitalization into title form unless acronym or version number
+        return ' '.join([w.title() if w.islower() and not re.match(r'^(v\d+(?:\.\d+)*|\d.*)$', w) else w for w in string.strip().replace('-', ' ').split()])
+
+    @staticmethod
+    def get_model_id_components(model_id: Optional[str] = None, total_params: int = 0) -> tuple[str | None, str | None, str | None, str | None, str | None, str | None]:
+        # Huggingface often store model id as '<org>/<model name>'
+        # so let's parse it and apply some heuristics if possible for model name components
+
+        if model_id is None:
+            # model ID missing
+            return None, None, None, None, None, None
+
+        if ' ' in model_id:
+            # model ID is actually a normal human sentence
+            # which means its most likely a normal model name only
+            # not part of the hugging face naming standard, but whatever
+            return model_id, None, None, None, None, None
+
+        if '/' in model_id:
+            # model ID (huggingface style)
+            org_component, model_full_name_component = model_id.split('/', 1)
+        else:
+            # model ID but missing org components
+            org_component, model_full_name_component = None, model_id
+
+        # Check if we erroneously matched against './' or '../' etc...
+        if org_component is not None and org_component[0] == '.':
+            org_component = None
+
+        name_parts: list[str] = model_full_name_component.split('-')
+        name_types: list[
+            set[Literal["basename", "size_label", "finetune", "version", "type"]]
+        ] = [set() for _ in name_parts]
+
+        # Annotate the name
+        for i, part in enumerate(name_parts):
+            # Version
+            if re.fullmatch(r'(v|iter)?\d+([.]\d+)*', part, re.IGNORECASE):
+                name_types[i].add("version")
+            # Quant type (should not be there for base models, but still annotated)
+            elif re.fullmatch(r'i?q\d(_\w)*|b?fp?(16|32)', part, re.IGNORECASE):
+                name_types[i].add("type")
+                name_parts[i] = part.upper()
+            # Model size
+            elif i > 0 and re.fullmatch(r'(([A]|\d+[x])?\d+([._]\d+)?[KMBT][\d]?|small|mini|medium|large|x?xl)', part, re.IGNORECASE):
+                part = part.replace("_", ".")
+                # Handle weird bloom-7b1 notation
+                if part[-1].isdecimal():
+                    part = part[:-2] + "." + part[-1] + part[-2]
+                # Normalize the size suffixes
+                if len(part) > 1 and part[-2].isdecimal():
+                    if part[-1] in "kmbt":
+                        part = part[:-1] + part[-1].upper()
+                if total_params != 0:
+                    try:
+                        label_params = float(part[:-1]) * pow(1000, " KMBT".find(part[-1]))
+                        # Only use it as a size label if it's close or bigger than the model size
+                        # Note that LoRA adapters don't necessarily include all layers,
+                        # so this is why bigger label sizes are accepted.
+                        # Do not use the size label when it's smaller than 1/8 of the model size
+                        if (total_params < 0 and label_params < abs(total_params) // 8) or (
+                            # Check both directions when the current model isn't a LoRA adapter
+                            total_params > 0 and abs(label_params - total_params) > 7 * total_params // 8
+                        ):
+                            # Likely a context length
+                            name_types[i].add("finetune")
+                            # Lowercase the size when it's a context length
+                            part = part[:-1] + part[-1].lower()
+                    except ValueError:
+                        # Failed to convert the size label to float, use it anyway
+                        pass
+                if len(name_types[i]) == 0:
+                    name_types[i].add("size_label")
+                name_parts[i] = part
+            # Some easy to recognize finetune names
+            elif i > 0 and re.fullmatch(r'chat|instruct|vision|lora', part, re.IGNORECASE):
+                name_types[i].add("finetune")
+                if part.lower() == "lora":
+                    name_parts[i] = "LoRA"
+
+        at_start = True
+        # Find the basename through the annotated name
+        for part, t in zip(name_parts, name_types):
+            if at_start and ((len(t) == 0 and part[0].isalpha()) or "version" in t):
+                t.add("basename")
+            else:
+                if at_start:
+                    at_start = False
+                if len(t) == 0:
+                    t.add("finetune")
+
+        # Remove the basename annotation from trailing version
+        for part, t in zip(reversed(name_parts), reversed(name_types)):
+            if "basename" in t:
+                if len(t) > 1:
+                    t.remove("basename")
+            else:
+                break
+
+        basename = "-".join(n for n, t in zip(name_parts, name_types) if "basename" in t) or None
+        size_label = "-".join(s for s, t in zip(name_parts, name_types) if "size_label" in t) or None
+        finetune = "-".join(f for f, t in zip(name_parts, name_types) if "finetune" in t) or None
+        # TODO: should the basename version always be excluded?
+        # TODO: should multiple versions be joined together?
+        version = ([v for v, t, in zip(name_parts, name_types) if "version" in t and "basename" not in t] or [None])[-1]
+
+        if size_label is None and finetune is None and version is None:
+            # Too ambiguous, output nothing
+            basename = None
+
+        return model_full_name_component, org_component, basename, finetune, version, size_label
+
+    @staticmethod
+    def apply_metadata_heuristic(metadata: Metadata, model_card: Optional[dict] = None, hf_params: Optional[dict] = None, model_path: Optional[Path] = None, total_params: int = 0) -> Metadata:
+        # Reference Model Card Metadata: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+
+        # Model Card Heuristics
+        ########################
+        if model_card is not None:
+
+            if "model_name" in model_card and metadata.name is None:
+                # Not part of huggingface model card standard but notice some model creator using it
+                # such as TheBloke in 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF'
+                metadata.name = model_card.get("model_name")
+
+            if "model_creator" in model_card and metadata.author is None:
+                # Not part of huggingface model card standard but notice some model creator using it
+                # such as TheBloke in 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF'
+                metadata.author = model_card.get("model_creator")
+
+            if "model_type" in model_card and metadata.basename is None:
+                # Not part of huggingface model card standard but notice some model creator using it
+                # such as TheBloke in 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF'
+                metadata.basename = model_card.get("model_type")
+
+            if "base_model" in model_card:
+                # This represents the parent models that this is based on
+                # Example: stabilityai/stable-diffusion-xl-base-1.0. Can also be a list (for merges)
+                # Example of merges: https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1/blob/main/README.md
+                metadata_base_models = []
+                base_model_value = model_card.get("base_model", None)
+
+                if base_model_value is not None:
+                    if isinstance(base_model_value, str):
+                        metadata_base_models.append(base_model_value)
+                    elif isinstance(base_model_value, list):
+                        metadata_base_models.extend(base_model_value)
+
+                if metadata.base_models is None:
+                    metadata.base_models = []
+
+                for model_id in metadata_base_models:
+                    # NOTE: model size of base model is assumed to be similar to the size of the current model
+                    model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params)
+                    base_model = {}
+                    if model_full_name_component is not None:
+                        base_model["name"] = Metadata.id_to_title(model_full_name_component)
+                    if org_component is not None:
+                        base_model["organization"] = Metadata.id_to_title(org_component)
+                    if version is not None:
+                        base_model["version"] = version
+                    if org_component is not None and model_full_name_component is not None:
+                        base_model["repo_url"] = f"https://huggingface.co/{org_component}/{model_full_name_component}"
+                    metadata.base_models.append(base_model)
+
+            if "license" in model_card and metadata.license is None:
+                metadata.license = model_card.get("license")
+
+            if "license_name" in model_card and metadata.license_name is None:
+                metadata.license_name = model_card.get("license_name")
+
+            if "license_link" in model_card and metadata.license_link is None:
+                metadata.license_link = model_card.get("license_link")
+
+            tags_value = model_card.get("tags", None)
+            if tags_value is not None:
+
+                if metadata.tags is None:
+                    metadata.tags = []
+
+                if isinstance(tags_value, str):
+                    metadata.tags.append(tags_value)
+                elif isinstance(tags_value, list):
+                    metadata.tags.extend(tags_value)
+
+            pipeline_tags_value = model_card.get("pipeline_tag", None)
+            if pipeline_tags_value is not None:
+
+                if metadata.tags is None:
+                    metadata.tags = []
+
+                if isinstance(pipeline_tags_value, str):
+                    metadata.tags.append(pipeline_tags_value)
+                elif isinstance(pipeline_tags_value, list):
+                    metadata.tags.extend(pipeline_tags_value)
+
+            language_value = model_card.get("languages", model_card.get("language", None))
+            if language_value is not None:
+
+                if metadata.languages is None:
+                    metadata.languages = []
+
+                if isinstance(language_value, str):
+                    metadata.languages.append(language_value)
+                elif isinstance(language_value, list):
+                    metadata.languages.extend(language_value)
+
+            dataset_value = model_card.get("datasets", model_card.get("dataset", None))
+            if dataset_value is not None:
+
+                if metadata.datasets is None:
+                    metadata.datasets = []
+
+                if isinstance(dataset_value, str):
+                    metadata.datasets.append(dataset_value)
+                elif isinstance(dataset_value, list):
+                    metadata.datasets.extend(dataset_value)
+
+        # Hugging Face Parameter Heuristics
+        ####################################
+
+        if hf_params is not None:
+
+            hf_name_or_path = hf_params.get("_name_or_path")
+            if hf_name_or_path is not None and hf_name_or_path.count('/') <= 1:
+                # Use _name_or_path only if its actually a model name and not some computer path
+                # e.g. 'meta-llama/Llama-2-7b-hf'
+                model_id = hf_name_or_path
+                model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params)
+                if metadata.name is None and model_full_name_component is not None:
+                    metadata.name = Metadata.id_to_title(model_full_name_component)
+                if metadata.organization is None and org_component is not None:
+                    metadata.organization = Metadata.id_to_title(org_component)
+                if metadata.basename is None and basename is not None:
+                    metadata.basename = basename
+                if metadata.finetune is None and finetune is not None:
+                    metadata.finetune = finetune
+                if metadata.version is None and version is not None:
+                    metadata.version = version
+                if metadata.size_label is None and size_label is not None:
+                    metadata.size_label = size_label
+
+        # Directory Folder Name Fallback Heuristics
+        ############################################
+        if model_path is not None:
+            model_id = model_path.name
+            model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params)
+            if metadata.name is None and model_full_name_component is not None:
+                metadata.name = Metadata.id_to_title(model_full_name_component)
+            if metadata.organization is None and org_component is not None:
+                metadata.organization = Metadata.id_to_title(org_component)
+            if metadata.basename is None and basename is not None:
+                metadata.basename = basename
+            if metadata.finetune is None and finetune is not None:
+                metadata.finetune = finetune
+            if metadata.version is None and version is not None:
+                metadata.version = version
+            if metadata.size_label is None and size_label is not None:
+                metadata.size_label = size_label
+
+        return metadata
+
+    def set_gguf_meta_model(self, gguf_writer: gguf.GGUFWriter):
+        assert self.name is not None
+        gguf_writer.add_name(self.name)
+
+        if self.author is not None:
+            gguf_writer.add_author(self.author)
+        if self.version is not None:
+            gguf_writer.add_version(self.version)
+        if self.organization is not None:
+            gguf_writer.add_organization(self.organization)
+
+        if self.finetune is not None:
+            gguf_writer.add_finetune(self.finetune)
+        if self.basename is not None:
+            gguf_writer.add_basename(self.basename)
+
+        if self.description is not None:
+            gguf_writer.add_description(self.description)
+        if self.quantized_by is not None:
+            gguf_writer.add_quantized_by(self.quantized_by)
+
+        if self.size_label is not None:
+            gguf_writer.add_size_label(self.size_label)
+
+        if self.license is not None:
+            gguf_writer.add_license(self.license)
+        if self.license_name is not None:
+            gguf_writer.add_license_name(self.license_name)
+        if self.license_link is not None:
+            gguf_writer.add_license_link(self.license_link)
+
+        if self.url is not None:
+            gguf_writer.add_url(self.url)
+        if self.doi is not None:
+            gguf_writer.add_doi(self.doi)
+        if self.uuid is not None:
+            gguf_writer.add_uuid(self.uuid)
+        if self.repo_url is not None:
+            gguf_writer.add_repo_url(self.repo_url)
+
+        if self.source_url is not None:
+            gguf_writer.add_source_url(self.source_url)
+        if self.source_doi is not None:
+            gguf_writer.add_source_doi(self.source_doi)
+        if self.source_uuid is not None:
+            gguf_writer.add_source_uuid(self.source_uuid)
+        if self.source_repo_url is not None:
+            gguf_writer.add_source_repo_url(self.source_repo_url)
+
+        if self.base_models is not None:
+            gguf_writer.add_base_model_count(len(self.base_models))
+            for key, base_model_entry in enumerate(self.base_models):
+                if "name" in base_model_entry:
+                    gguf_writer.add_base_model_name(key, base_model_entry["name"])
+                if "author" in base_model_entry:
+                    gguf_writer.add_base_model_author(key, base_model_entry["author"])
+                if "version" in base_model_entry:
+                    gguf_writer.add_base_model_version(key, base_model_entry["version"])
+                if "organization" in base_model_entry:
+                    gguf_writer.add_base_model_organization(key, base_model_entry["organization"])
+                if "url" in base_model_entry:
+                    gguf_writer.add_base_model_url(key, base_model_entry["url"])
+                if "doi" in base_model_entry:
+                    gguf_writer.add_base_model_doi(key, base_model_entry["doi"])
+                if "uuid" in base_model_entry:
+                    gguf_writer.add_base_model_uuid(key, base_model_entry["uuid"])
+                if "repo_url" in base_model_entry:
+                    gguf_writer.add_base_model_repo_url(key, base_model_entry["repo_url"])
+
+        if self.tags is not None:
+            gguf_writer.add_tags(self.tags)
+        if self.languages is not None:
+            gguf_writer.add_languages(self.languages)
+        if self.datasets is not None:
+            gguf_writer.add_datasets(self.datasets)
diff --git a/gguf-py/gguf/utility.py b/gguf-py/gguf/utility.py
new file mode 100644
index 00000000000000..ef76831b521eef
--- /dev/null
+++ b/gguf-py/gguf/utility.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+from typing import Literal
+
+
+def fill_templated_filename(filename: str, output_type: str | None) -> str:
+    # Given a file name fill in any type templates e.g. 'some-model-name.{ftype}.gguf'
+    ftype_lowercase: str = output_type.lower() if output_type is not None else ""
+    ftype_uppercase: str = output_type.upper() if output_type is not None else ""
+    return filename.format(ftype_lowercase,
+                           outtype=ftype_lowercase, ftype=ftype_lowercase,
+                           OUTTYPE=ftype_uppercase, FTYPE=ftype_uppercase)
+
+
+def model_weight_count_rounded_notation(model_params_count: int, min_digits: int = 2) -> str:
+    if model_params_count > 1e12 :
+        # Trillions Of Parameters
+        scaled_model_params = model_params_count * 1e-12
+        scale_suffix = "T"
+    elif model_params_count > 1e9 :
+        # Billions Of Parameters
+        scaled_model_params = model_params_count * 1e-9
+        scale_suffix = "B"
+    elif model_params_count > 1e6 :
+        # Millions Of Parameters
+        scaled_model_params = model_params_count * 1e-6
+        scale_suffix = "M"
+    else:
+        # Thousands Of Parameters
+        scaled_model_params = model_params_count * 1e-3
+        scale_suffix = "K"
+
+    fix = max(min_digits - len(str(round(scaled_model_params)).lstrip('0')), 0)
+
+    return f"{scaled_model_params:.{fix}f}{scale_suffix}"
+
+
+def size_label(total_params: int, shared_params: int, expert_params: int, expert_count: int) -> str:
+
+    if expert_count > 0:
+        pretty_size = model_weight_count_rounded_notation(abs(shared_params) + abs(expert_params), min_digits=2)
+        size_class = f"{expert_count}x{pretty_size}"
+    else:
+        size_class = model_weight_count_rounded_notation(abs(total_params), min_digits=2)
+
+    return size_class
+
+
+def naming_convention(model_name: str | None, base_name: str | None, finetune_string: str | None, version_string: str | None, size_label: str | None, output_type: str | None, model_type: Literal['vocab', 'LoRA'] | None = None) -> str:
+    # Reference: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#gguf-naming-convention
+
+    if base_name is not None:
+        name = base_name.strip().title().replace(' ', '-').replace('/', '-')
+    elif model_name is not None:
+        name = model_name.strip().title().replace(' ', '-').replace('/', '-')
+    else:
+        name = "ggml-model"
+
+    parameters = f"-{size_label}" if size_label is not None else ""
+
+    finetune = f"-{finetune_string.strip().title().replace(' ', '-')}" if finetune_string is not None else ""
+
+    version = f"-{version_string.strip().replace(' ', '-')}" if version_string is not None else ""
+
+    encoding = f"-{output_type.strip().replace(' ', '-').upper()}" if output_type is not None else ""
+
+    kind = f"-{model_type.strip().replace(' ', '-')}" if model_type is not None else ""
+
+    return f"{name}{parameters}{finetune}{version}{encoding}{kind}"
diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml
index 62129126bdddcf..19f6761e2f9125 100644
--- a/gguf-py/pyproject.toml
+++ b/gguf-py/pyproject.toml
@@ -22,6 +22,7 @@ classifiers = [
 python = ">=3.8"
 numpy = ">=1.17"
 tqdm = ">=4.27"
+pyyaml = ">=5.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^5.2"
diff --git a/gguf-py/tests/__init__.py b/gguf-py/tests/__init__.py
new file mode 100644
index 00000000000000..d23ff9cb7380c5
--- /dev/null
+++ b/gguf-py/tests/__init__.py
@@ -0,0 +1 @@
+from .test_metadata import *
diff --git a/gguf-py/tests/test_gguf.py b/gguf-py/tests/test_gguf.py
deleted file mode 100644
index 76b52181e03910..00000000000000
--- a/gguf-py/tests/test_gguf.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import gguf  # noqa: F401  # pyright: ignore[reportUnusedImport]
-
-# TODO: add tests
-
-
-def test_write_gguf() -> None:
-    pass
diff --git a/gguf-py/tests/test_metadata.py b/gguf-py/tests/test_metadata.py
new file mode 100755
index 00000000000000..3fac8218883f1f
--- /dev/null
+++ b/gguf-py/tests/test_metadata.py
@@ -0,0 +1,158 @@
+#!/usr/bin/env python3
+
+import unittest
+from pathlib import Path
+import os
+import sys
+
+# Necessary to load the local gguf package
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import gguf
+
+
+class TestMetadataMethod(unittest.TestCase):
+
+    def test_id_to_title(self):
+        self.assertEqual(gguf.Metadata.id_to_title("Mixtral-8x7B-Instruct-v0.1"), "Mixtral 8x7B Instruct v0.1")
+        self.assertEqual(gguf.Metadata.id_to_title("Meta-Llama-3-8B"), "Meta Llama 3 8B")
+        self.assertEqual(gguf.Metadata.id_to_title("hermes-2-pro-llama-3-8b-DPO"), "Hermes 2 Pro Llama 3 8b DPO")
+
+    def test_get_model_id_components(self):
+        # This is the basic standard form with organization marker
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mistral/Mixtral-8x7B-Instruct-v0.1"),
+                         ('Mixtral-8x7B-Instruct-v0.1', "Mistral", 'Mixtral', 'Instruct', 'v0.1', '8x7B'))
+
+        # Similar to basic standard form but without organization marker
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-Instruct-v0.1"),
+                         ('Mixtral-8x7B-Instruct-v0.1', None, 'Mixtral', 'Instruct', 'v0.1', '8x7B'))
+
+        # Missing version
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-Instruct"),
+                         ('Mixtral-8x7B-Instruct', None, 'Mixtral', 'Instruct', None, '8x7B'))
+
+        # Missing finetune
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-v0.1"),
+                         ('Mixtral-8x7B-v0.1', None, 'Mixtral', None, 'v0.1', '8x7B'))
+
+        # Base name and size label only
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B"),
+                         ('Mixtral-8x7B', None, 'Mixtral', None, None, '8x7B'))
+
+        # Base name and version only
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-v0.1"),
+                         ('Mixtral-v0.1', None, 'Mixtral', None, 'v0.1', None))
+
+        ## Edge Cases ##
+
+        # This is too ambiguous... best to err on caution and output nothing
+        self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral"),
+                         ('Mixtral', None, None, None, None, None))
+
+        # Basename has numbers mixed in and also size label provided. Must avoid capturing number in basename
+        self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Meta-Llama-3-8B"),
+                         ('Meta-Llama-3-8B', "NousResearch", 'Meta-Llama-3', None, None, '8B'))
+
+        # Can't detect all non standard form in a heuristically safe way... best to err in caution and output nothing...
+        self.assertEqual(gguf.Metadata.get_model_id_components("Qwen1.5-MoE-A2.7B-Chat"),
+                         ('Qwen1.5-MoE-A2.7B-Chat', None, 'Qwen1.5-MoE', 'Chat', None, 'A2.7B'))
+
+        # Capture 'sub size labels' e.g. A14B in '57B-A14B' usually refers to activated params/weight count
+        self.assertEqual(gguf.Metadata.get_model_id_components("Qwen2-57B-A14B-Instruct"),
+                         ('Qwen2-57B-A14B-Instruct', None, 'Qwen2', 'Instruct', None, '57B-A14B'))
+
+        # Check that it can handle a real model id with no version code
+        # Note that 4k in this string is non standard and microsoft were referring to context length rather than weight count
+        self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Phi-3-mini-4k-instruct", 4 * 10**9),
+                         ('Phi-3-mini-4k-instruct', 'microsoft', 'Phi-3', '4k-instruct', None, 'mini'))
+
+        # There is some legitimate models with only thousands of parameters
+        self.assertEqual(gguf.Metadata.get_model_id_components("delphi-suite/stories-llama2-50k", 50 * 10**3),
+                         ('stories-llama2-50k', 'delphi-suite', 'stories-llama2', None, None, '50K'))
+
+        # None standard and not easy to disambiguate
+        self.assertEqual(gguf.Metadata.get_model_id_components("DeepSeek-Coder-V2-Lite-Instruct"),
+                         ('DeepSeek-Coder-V2-Lite-Instruct', None, 'DeepSeek-Coder-V2-Lite', 'Instruct', None, None))
+
+        # This is a real model_id where they append 2DPO to refer to Direct Preference Optimization
+        self.assertEqual(gguf.Metadata.get_model_id_components("crestf411/daybreak-kunoichi-2dpo-7b"),
+                         ('daybreak-kunoichi-2dpo-7b', 'crestf411', 'daybreak-kunoichi', '2dpo', None, '7B'))
+
+        # This is a real model id where the weight size has a decimal point
+        self.assertEqual(gguf.Metadata.get_model_id_components("Qwen2-0.5B-Instruct"),
+                         ('Qwen2-0.5B-Instruct', None, 'Qwen2', 'Instruct', None, '0.5B'))
+
+        # Uses an underscore in the size label
+        self.assertEqual(gguf.Metadata.get_model_id_components("smallcloudai/Refact-1_6B-fim"),
+                         ('Refact-1_6B-fim', 'smallcloudai', 'Refact', 'fim', None, '1.6B'))
+
+        # Uses Iter3 for the version
+        self.assertEqual(gguf.Metadata.get_model_id_components("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3"),
+                         ('Gemma-2-9B-It-SPPO-Iter3', 'UCLA-AGI', 'Gemma-2', 'It-SPPO', 'Iter3', '9B'))
+
+        # Has two potential versions in the basename
+        self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Hermes-2-Theta-Llama-3-8B"),
+                         ('Hermes-2-Theta-Llama-3-8B', 'NousResearch', 'Hermes-2-Theta-Llama-3', None, None, '8B'))
+
+        # Potential version in the basename
+        self.assertEqual(gguf.Metadata.get_model_id_components("SeaLLMs/SeaLLMs-v3-7B-Chat"),
+                         ('SeaLLMs-v3-7B-Chat', 'SeaLLMs', 'SeaLLMs-v3', 'Chat', None, '7B'))
+
+        # Underscore in the basename, and 1m for the context size
+        self.assertEqual(gguf.Metadata.get_model_id_components("internlm/internlm2_5-7b-chat-1m", 7 * 10**9),
+                         ('internlm2_5-7b-chat-1m', 'internlm', 'internlm2_5', 'chat-1m', None, '7B'))
+
+        # Version before the finetune name
+        self.assertEqual(gguf.Metadata.get_model_id_components("pszemraj/jamba-900M-v0.13-KIx2"),
+                         ('jamba-900M-v0.13-KIx2', 'pszemraj', 'jamba', 'KIx2', 'v0.13', '900M'))
+
+        # TODO: hf suffix which could be ignored but isn't
+        self.assertEqual(gguf.Metadata.get_model_id_components("state-spaces/mamba-2.8b-hf"),
+                         ('mamba-2.8b-hf', 'state-spaces', 'mamba', 'hf', None, '2.8B'))
+
+        # Two sizes, don't merge them, the other is the number of tokens on which it was trained
+        self.assertEqual(gguf.Metadata.get_model_id_components("abacaj/llama-161M-100B", 161 * 10**6),
+                         ('llama-161M-100B', 'abacaj', 'llama', '100b', None, '161M'))
+
+        # It's a trap, there is no size label
+        self.assertEqual(gguf.Metadata.get_model_id_components("SparseLLM/relu-100B", 1340 * 10**6),
+                         ('relu-100B', 'SparseLLM', 'relu', '100b', None, None))
+
+        # Weird size notation
+        self.assertEqual(gguf.Metadata.get_model_id_components("bigscience/bloom-7b1-petals"),
+                         ('bloom-7b1-petals', 'bigscience', 'bloom', 'petals', None, '7.1B'))
+
+    def test_apply_metadata_heuristic_from_model_card(self):
+        model_card = {
+            'tags': ['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'],
+            'model-index': [{'name': 'Mixtral-8x7B-Instruct-v0.1', 'results': []}],
+            'language': ['en'],
+            'datasets': ['teknium/OpenHermes-2.5'],
+            'widget': [{'example_title': 'Hermes 2 Pro', 'messages': [{'role': 'system', 'content': 'You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.'}, {'role': 'user', 'content': 'Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.'}]}],
+            'base_model': ["EmbeddedLLM/Mistral-7B-Merge-14-v0", "janai-hq/trinity-v1"]
+        }
+        got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
+        expect = gguf.Metadata()
+        expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': 'v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}]
+        expect.tags=['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl']
+        expect.languages=['en']
+        expect.datasets=['teknium/OpenHermes-2.5']
+
+        self.assertEqual(got, expect)
+
+    def test_apply_metadata_heuristic_from_hf_parameters(self):
+        hf_params = {"_name_or_path": "./hermes-2-pro-llama-3-8b-DPO"}
+        got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card=None, hf_params=hf_params, model_path=None)
+        expect = gguf.Metadata(name='Hermes 2 Pro Llama 3 8b DPO', finetune='DPO', basename='hermes-2-pro-llama-3', size_label='8B')
+        self.assertEqual(got, expect)
+
+    def test_apply_metadata_heuristic_from_model_dir(self):
+        model_dir_path = Path("./hermes-2-pro-llama-3-8b-DPO")
+        got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card=None, hf_params=None, model_path=model_dir_path)
+        expect = gguf.Metadata(name='Hermes 2 Pro Llama 3 8b DPO', finetune='DPO', basename='hermes-2-pro-llama-3', size_label='8B')
+        self.assertEqual(got, expect)
+
+
+if __name__ == "__main__":
+    unittest.main()

From 0d2c7321e9678e91b760ebe57f0d063856bb018b Mon Sep 17 00:00:00 2001
From: Eric Zhang <34133756+EZForever@users.noreply.github.com>
Date: Thu, 18 Jul 2024 18:43:49 +0800
Subject: [PATCH 073/125] server: use relative routes for static files in new
 UI (#8552)

* server: public: fix api_url on non-index pages

* server: public: use relative routes for static files in new UI
---
 examples/server/public/completion.js  | 4 ++--
 examples/server/public/index-new.html | 8 ++++----
 examples/server/public/index.html     | 2 +-
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js
index 987b9a3b891d0c..36818f7644aa10 100644
--- a/examples/server/public/completion.js
+++ b/examples/server/public/completion.js
@@ -21,7 +21,7 @@ let generation_settings = null;
 //
 export async function* llama(prompt, params = {}, config = {}) {
   let controller = config.controller;
-  const api_url = config.api_url || "";
+  const api_url = config.api_url?.replace(/\/+$/, '') || "";
 
   if (!controller) {
     controller = new AbortController();
@@ -196,7 +196,7 @@ export const llamaComplete = async (params, controller, callback) => {
 // Get the model info from the server. This is useful for getting the context window and so on.
 export const llamaModelInfo = async (config = {}) => {
   if (!generation_settings) {
-    const api_url = config.api_url || "";
+    const api_url = config.api_url?.replace(/\/+$/, '') || "";
     const props = await fetch(`${api_url}/props`).then(r => r.json());
     generation_settings = props.default_generation_settings;
   }
diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html
index 5513e9121a40fd..bf2b0a3f0803c9 100644
--- a/examples/server/public/index-new.html
+++ b/examples/server/public/index-new.html
@@ -14,10 +14,10 @@
   <script type="module">
     import {
       html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
-    } from '/index.js';
+    } from './index.js';
 
-    import { llama } from '/completion.js';
-    import { SchemaConverter } from '/json-schema-to-grammar.mjs';
+    import { llama } from './completion.js';
+    import { SchemaConverter } from './json-schema-to-grammar.mjs';
     import { promptFormats } from './prompt-formats.js';
     import { systemPrompts } from './system-prompts.js'; // multilingual is wip
     let selected_image = false;
@@ -225,7 +225,7 @@
         throw new Error("already running");
       }
       controller.value = new AbortController();
-      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: URL.parse('.', document.baseURI).href })) {
         const data = chunk.data;
         if (data.stop) {
           while (
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 2f60a76e88b87b..a15424613499ff 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -479,7 +479,7 @@
         throw new Error("already running");
       }
       controller.value = new AbortController();
-      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: location.pathname.replace(/\/+$/, '') })) {
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: URL.parse('.', document.baseURI).href })) {
         const data = chunk.data;
 
         if (data.stop) {

From 705b7ecf60e667ced57c15d67aa86865e3cc7aa7 Mon Sep 17 00:00:00 2001
From: 65a <10104049+65a@users.noreply.github.com>
Date: Thu, 18 Jul 2024 07:47:12 -0700
Subject: [PATCH 074/125] cmake : install all ggml public headers (#8480)

Co-authored-by: 65a <65a@65a.invalid>
---
 ggml/CMakeLists.txt | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 649ac3dcc4f63a..be22a74606c0b7 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -194,13 +194,19 @@ endif ()
 include(GNUInstallDirs)
 include(CMakePackageConfigHelpers)
 
+# all public headers
 set(GGML_PUBLIC_HEADERS
     include/ggml.h
     include/ggml-alloc.h
     include/ggml-backend.h
-    "${GGML_HEADERS_CUDA}"
-    "${GGML_HEADERS_METAL}"
-    "${GGML_HEADERS_EXTRA}")
+    include/ggml-blas.h
+    include/ggml-cuda.h
+    include/ggml.h
+    include/ggml-kompute.h
+    include/ggml-metal.h
+    include/ggml-rpc.h
+    include/ggml-sycl.h
+    include/ggml-vulkan.h)
 
 set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
 #if (GGML_METAL)

From a15ef8f8a08e12f9c5162c221e67779e71182073 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Thu, 18 Jul 2024 23:48:47 +0200
Subject: [PATCH 075/125] CUDA: fix partial offloading for ne0 % 256 != 0
 (#8572)

---
 ggml/include/ggml-backend.h | 28 +++++++++++++++-------------
 ggml/src/ggml-alloc.c       |  1 +
 ggml/src/ggml-backend.c     |  4 ++++
 ggml/src/ggml-cuda.cu       | 11 +++++++++--
 4 files changed, 29 insertions(+), 15 deletions(-)

diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index 4a38eeb5c23bde..5f3f1e286990e4 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -29,21 +29,23 @@ extern "C" {
     enum ggml_backend_buffer_usage {
         GGML_BACKEND_BUFFER_USAGE_ANY = 0,
         GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
+        GGML_BACKEND_BUFFER_USAGE_COMPUTE = 2,
     };
 
-    GGML_API           const char *               ggml_backend_buffer_name          (ggml_backend_buffer_t buffer);
-    GGML_API           void                       ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
-    GGML_API           void *                     ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
-    GGML_API           size_t                     ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
-    GGML_API GGML_CALL void                       ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-    GGML_API           size_t                     ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
-    GGML_API           size_t                     ggml_backend_buffer_get_max_size  (ggml_backend_buffer_t buffer);
-    GGML_API           size_t                     ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-    GGML_API           void                       ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
-    GGML_API           bool                       ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
-    GGML_API           void                       ggml_backend_buffer_set_usage     (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
-    GGML_API           ggml_backend_buffer_type_t ggml_backend_buffer_get_type      (ggml_backend_buffer_t buffer);
-    GGML_API           void                       ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
+    GGML_API           const char *                   ggml_backend_buffer_name          (ggml_backend_buffer_t buffer);
+    GGML_API           void                           ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
+    GGML_API           void *                         ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                         ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
+    GGML_API GGML_CALL void                           ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API           size_t                         ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                         ggml_backend_buffer_get_max_size  (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                         ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API           void                           ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
+    GGML_API           bool                           ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
+    GGML_API           void                           ggml_backend_buffer_set_usage     (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
+    GGML_API           enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage     (ggml_backend_buffer_t buffer);
+    GGML_API           ggml_backend_buffer_type_t     ggml_backend_buffer_get_type      (ggml_backend_buffer_t buffer);
+    GGML_API           void                           ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
 
     //
     // Backend
diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
index bd367c42df44e8..e176b883e38c50 100644
--- a/ggml/src/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -776,6 +776,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
                 fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
                 return false;
             }
+            ggml_backend_buffer_set_usage(galloc->buffers[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
         }
     }
 
diff --git a/ggml/src/ggml-backend.c b/ggml/src/ggml-backend.c
index 01c87efd0db6ec..d39cfed8886f42 100644
--- a/ggml/src/ggml-backend.c
+++ b/ggml/src/ggml-backend.c
@@ -134,6 +134,10 @@ void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backe
     }
 }
 
+enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage(ggml_backend_buffer_t buffer) {
+    return buffer->usage;
+}
+
 ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) {
     return buffer->buft;
 }
diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu
index 39e345b668bc11..e48269e463ec15 100644
--- a/ggml/src/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -464,12 +464,12 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t
         return;
     }
 
-    if (ggml_is_quantized(tensor->type)) {
+    if (ggml_is_quantized(tensor->type) && tensor->view_src == nullptr && ggml_backend_buffer_get_usage(buffer) != GGML_BACKEND_BUFFER_USAGE_COMPUTE) {
         // initialize padding to 0 to avoid possible NaN values
         size_t original_size = ggml_nbytes(tensor);
         size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
 
-        if (padded_size > original_size && tensor->view_src == nullptr) {
+        if (padded_size > original_size) {
             ggml_cuda_set_device(ctx->device);
             CUDA_CHECK(cudaMemset((char *)tensor->data + original_size, 0, padded_size - original_size));
         }
@@ -1485,6 +1485,13 @@ static void ggml_cuda_op_mul_mat(
             dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), ggml_nbytes(src0));
         }
 
+        // If src0 is on a temporary compute buffers (partial offloading) there may be some padding that needs to be cleared:
+        if (ne00 % MATRIX_ROW_PADDING != 0 && ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE && src0->view_src == nullptr) {
+            const int64_t nbytes_data    = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00);
+            const int64_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING);
+            CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd + nbytes_data , 0, nbytes_padding, stream));
+        }
+
         if (src1_on_device && src1_is_contiguous) {
             dev[id].src1_ddf = (float *) src1->data;
         } else {

From 3d0e4367d99087892e355ddbeebd232a0b2f40de Mon Sep 17 00:00:00 2001
From: Brian <mofosyne@gmail.com>
Date: Fri, 19 Jul 2024 17:51:51 +1000
Subject: [PATCH 076/125] convert-*.py: add general.name kv override (#8571)

---
 gguf-py/gguf/metadata.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/gguf-py/gguf/metadata.py b/gguf-py/gguf/metadata.py
index be297f2426f789..bac6ebfb3777a0 100644
--- a/gguf-py/gguf/metadata.py
+++ b/gguf-py/gguf/metadata.py
@@ -62,6 +62,7 @@ def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Pat
         # This is based on LLM_KV_NAMES mapping in llama.cpp
         metadata_override = Metadata.load_metadata_override(metadata_override_path)
 
+        metadata.name            = metadata_override.get(Keys.General.NAME,            metadata.name)
         metadata.author          = metadata_override.get(Keys.General.AUTHOR,          metadata.author)
         metadata.version         = metadata_override.get(Keys.General.VERSION,         metadata.version)
         metadata.organization    = metadata_override.get(Keys.General.ORGANIZATION,    metadata.organization)

From f299aa98ecc19cbc574e9d698e03999e89de3d3d Mon Sep 17 00:00:00 2001
From: Frank Mai <thxcode0824@gmail.com>
Date: Fri, 19 Jul 2024 17:44:41 +0800
Subject: [PATCH 077/125] fix: typo of chatglm4 chat tmpl (#8586)

Signed-off-by: thxCode <thxcode0824@gmail.com>
---
 src/llama.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index 20e85b3ebe5dfa..228e112acfe942 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -21607,7 +21607,7 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>";
         }
-    } else if (tmpl == "chaglm4" || tmpl_contains("[gMASK]<sop>")) {
+    } else if (tmpl == "chatglm4" || tmpl_contains("[gMASK]<sop>")) {
         ss << "[gMASK]" << "<sop>";
         for (auto message : chat) {
             std::string role(message->role);

From b57eb9ca4fb79f3163c6a69e154ff76157a4f716 Mon Sep 17 00:00:00 2001
From: Clint Herron <hanclinto@gmail.com>
Date: Fri, 19 Jul 2024 07:05:45 -0400
Subject: [PATCH 078/125] ggml : add friendlier error message to fopen errors
 (#8575)

* Add additional error information when model files fail to load.

* Adding additional error information to most instances of fopen.
---
 ggml/src/ggml.c | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 2ae2d6d5f58a93..7a39c685b6f989 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -19019,7 +19019,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
         FILE * fout = ggml_fopen(fname, "wb");
 
         if (!fout) {
-            fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
+            fprintf(stderr, "%s: failed to open %s: %s\n", __func__, fname, strerror(errno));
             return;
         }
 
@@ -19156,7 +19156,7 @@ struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context *
     {
         FILE * fin = ggml_fopen(fname, "rb");
         if (!fin) {
-            fprintf(stderr, "%s: failed to open %s\n", __func__, fname);
+            fprintf(stderr, "%s: failed to open %s: %s\n", __func__, fname, strerror(errno));
             return result;
         }
 
@@ -20830,6 +20830,7 @@ struct gguf_context * gguf_init_empty(void) {
 struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) {
     FILE * file = ggml_fopen(fname, "rb");
     if (!file) {
+        fprintf(stderr, "%s: failed to open '%s': '%s'\n", __func__, fname, strerror(errno));
         return NULL;
     }
 

From be0cfb41752551a4680ee7dfd29f2a05b50db442 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 19 Jul 2024 14:34:55 +0300
Subject: [PATCH 079/125] readme : fix server badge

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 058919068b872c..7c233b5e11165b 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
 ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
 
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
-[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
+[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
 [![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp)
 
 [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)

From d19754553029296c46d40b2f7bd4e2c2f531a8c5 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 19 Jul 2024 16:50:47 +0300
Subject: [PATCH 080/125] llama : bump max layers from 256 to 512 (#8530)

* llama : bump max layers from 256 to 512

* llama : replace asserts with exceptions
---
 include/llama.h |  2 +-
 src/llama.cpp   | 14 +++++++++-----
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/include/llama.h b/include/llama.h
index c0fb53060eae44..b280df3255b6a5 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -40,7 +40,7 @@
 #define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
 
 #define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN
-#define LLAMA_SESSION_VERSION 6
+#define LLAMA_SESSION_VERSION 7
 
 #define LLAMA_STATE_SEQ_MAGIC   LLAMA_FILE_MAGIC_GGSQ
 #define LLAMA_STATE_SEQ_VERSION 1
diff --git a/src/llama.cpp b/src/llama.cpp
index 228e112acfe942..7d68ed8111873b 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -114,7 +114,7 @@
 
 // bump if necessary
 #define LLAMA_MAX_NODES   8192
-#define LLAMA_MAX_LAYERS  256
+#define LLAMA_MAX_LAYERS  512
 #define LLAMA_MAX_EXPERTS 160  // DeepSeekV2
 
 //
@@ -4007,7 +4007,9 @@ struct llama_model_loader {
                 throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
         }
 
-        GGML_ASSERT(arr_info.length <= N_MAX);
+        if (arr_info.length > N_MAX) {
+            throw std::runtime_error(format("array length %u for key %s exceeds max %u", (uint32_t) arr_info.length, key.c_str(), (uint32_t) N_MAX));
+        }
 
         std::copy((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length, result.begin());
 
@@ -4043,8 +4045,6 @@ struct llama_model_loader {
     // get array of n <= N_MAX elements, or a single element repeated n times
     template<typename T, size_t N_MAX>
     bool get_key_or_arr(const std::string & key, std::array<T, N_MAX> & result, uint32_t n, const bool required = true) {
-        GGML_ASSERT(n <= N_MAX);
-
         const int kid = gguf_find_key(meta, key.c_str());
 
         if (kid < 0) {
@@ -4054,6 +4054,10 @@ struct llama_model_loader {
             return false;
         }
 
+        if (n > N_MAX) {
+            throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", (uint32_t) n, (uint32_t) N_MAX, key.c_str()));
+        }
+
         if (gguf_get_kv_type(meta, kid) == GGUF_TYPE_ARRAY) {
             struct GGUFMeta::ArrayInfo arr_info =
                 GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
@@ -19920,7 +19924,7 @@ size_t llama_state_get_size(const struct llama_context * ctx) {
     );
 
     // on session change it is very likely that the state size has changed - so we need to update this function
-    static_assert(LLAMA_SESSION_VERSION == 6, "So you just bumped the session version - good. But did you remember to update llama_state_get_size?");
+    static_assert(LLAMA_SESSION_VERSION == 7, "So you just bumped the session version - good. But did you remember to update llama_state_get_size?");
 
     return s_total;
 }

From 57b1d4f9eb6f2c139b31ea79626d954b261e1051 Mon Sep 17 00:00:00 2001
From: Brian <mofosyne@gmail.com>
Date: Sat, 20 Jul 2024 00:04:38 +1000
Subject: [PATCH 081/125] convert-*.py: remove add_name from ChatGLMModel class
 (#8590)

---
 convert_hf_to_gguf.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 769d49a8b6f0a8..e3c8aac3d32aae 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3420,7 +3420,6 @@ def set_vocab(self):
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.hparams["_name_or_path"].split("/")[1]) # THUDM/glm4-9b-chat or THUDM/chatglm3-6b
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
         n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
         n_head_kv = self.hparams.get("multi_query_group_num", n_head)

From 87e397d00bdcedd5cbf6dfda06a7b0f302462728 Mon Sep 17 00:00:00 2001
From: slaren <slarengh@gmail.com>
Date: Fri, 19 Jul 2024 17:17:27 +0200
Subject: [PATCH 082/125] ggml : fix quant dot product with odd number of
 blocks (#8549)

* ggml : fix iq4_nl dot product with odd number of blocks

* ggml : fix odd blocks for ARM_NEON (#8556)

* ggml : fix iq4_nl dot product with odd number of blocks

* ggml : fix q4_1

* ggml : fix q5_0

* ggml : fix q5_1

* ggml : fix iq4_nl metal

ggml-ci

* ggml : fix q4_0

* ggml : fix q8_0

ggml-ci

* ggml : remove special Q4_0 code for first 2 blocks

* ggml : fix sumf redefinition

---------

Co-authored-by: slaren <slarengh@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/src/ggml-metal.m      |   4 -
 ggml/src/ggml-metal.metal  |   4 +-
 ggml/src/ggml-quants.c     | 828 +++++++++++++++----------------------
 tests/test-backend-ops.cpp |  27 ++
 4 files changed, 362 insertions(+), 501 deletions(-)

diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m
index b5939efa6c279a..a7619bcca46146 100644
--- a/ggml/src/ggml-metal.m
+++ b/ggml/src/ggml-metal.m
@@ -1786,10 +1786,6 @@ static enum ggml_status ggml_metal_graph_compute(
                                     }
                             };
 
-                            if (ggml_is_quantized(src0t)) {
-                                GGML_ASSERT(ne00 >= nth0*nth1);
-                            }
-
                             [encoder setComputePipelineState:pipeline];
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                             [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal.metal
index 2a3b0c0a69a746..3bb37d32aced02 100644
--- a/ggml/src/ggml-metal.metal
+++ b/ggml/src/ggml-metal.metal
@@ -4757,7 +4757,7 @@ void kernel_mul_mv_iq4_nl_f32_impl(
         device const float4 * y4 = (device const float4 *)yb;
         yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
 
-        for (int row = 0; row < 2; ++row) {
+        for (int row = 0; row < 2 && first_row + row < ne01; ++row) {
 
             device const block_iq4_nl & xb = x[row*nb + ib];
             device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it);
@@ -4789,7 +4789,7 @@ void kernel_mul_mv_iq4_nl_f32_impl(
         yb += 16 * QK4_NL;
     }
 
-    for (int row = 0; row < 2; ++row) {
+    for (int row = 0; row < 2 && first_row + row < ne01; ++row) {
         all_sum = simd_sum(sumf[row]);
         if (tiisg == 0) {
             dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index 1839a722ed00cb..1defed3caad391 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -3808,11 +3808,15 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2);
         float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1);
 
-        vst1_f32(s, vget_low_f32(sumv2));
+        vst1_f32(s,      vget_low_f32(sumv2));
         vst1_f32(s + bs, vget_high_f32(sumv2));
         return;
     }
 #endif
+
+    int ib = 0;
+    float sumf = 0;
+
 #if defined(__ARM_FEATURE_SVE)
     if (svcntb() == QK8_0) {
         const svbool_t ptrueh = svptrue_pat_b8(SV_VL16);
@@ -3821,13 +3825,11 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         svfloat32_t sumv0 = svdup_n_f32(0.0f);
         svfloat32_t sumv1 = svdup_n_f32(0.0f);
 
-        assert(nb % 2 == 0); // TODO: handle odd nb
-
-        for (int i = 0; i < nb; i += 2) {
-            const block_q4_0 * restrict x0 = &x[i + 0];
-            const block_q4_0 * restrict x1 = &x[i + 1];
-            const block_q8_0 * restrict y0 = &y[i + 0];
-            const block_q8_0 * restrict y1 = &y[i + 1];
+        for (; ib + 1 < nb; ib += 2) {
+            const block_q4_0 * restrict x0 = &x[ib + 0];
+            const block_q4_0 * restrict x1 = &x[ib + 1];
+            const block_q8_0 * restrict y0 = &y[ib + 0];
+            const block_q8_0 * restrict y1 = &y[ib + 1];
 
             // load x
             const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs);
@@ -3850,21 +3852,17 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
             sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
         }
 
-        *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
-        return;
+        sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
     }
-#endif
-#if defined(__ARM_NEON)
+#elif defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
     float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    for (int i = 0; i < nb; i += 2) {
-        const block_q4_0 * restrict x0 = &x[i + 0];
-        const block_q4_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i + 0];
-        const block_q8_0 * restrict y1 = &y[i + 1];
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q4_0 * restrict x0 = &x[ib + 0];
+        const block_q4_0 * restrict x1 = &x[ib + 1];
+        const block_q8_0 * restrict y0 = &y[ib + 0];
+        const block_q8_0 * restrict y1 = &y[ib + 1];
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
         const int8x16_t  s8b = vdupq_n_s8(0x8);
@@ -3898,23 +3896,23 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+    sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
 #elif defined(__AVX2__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
+    for (; ib < nb; ++ib) {
         /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
+        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
 
-        __m256i qx = bytes_from_nibbles_32(x[i].qs);
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
 
         // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
         const __m256i off = _mm256_set1_epi8( 8 );
         qx = _mm256_sub_epi8( qx, off );
 
-        __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
@@ -3922,28 +3920,28 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = _mm256_fmadd_ps( d, q, acc );
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__AVX__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
+    for (; ib < nb; ++ib) {
         // Compute combined scale for the block
-        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
+        const __m256 d = _mm256_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
 
         const __m128i lowMask = _mm_set1_epi8(0xF);
         const __m128i off = _mm_set1_epi8(8);
 
-        const __m128i tmp = _mm_loadu_si128((const __m128i *)x[i].qs);
+        const __m128i tmp = _mm_loadu_si128((const __m128i *)x[ib].qs);
 
         __m128i bx_0 = _mm_and_si128(lowMask, tmp);
-        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs);
+        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[ib].qs);
         bx_0 = _mm_sub_epi8(bx_0, off);
         const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
 
         bx_0 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp, 4));
-        by_0 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
+        by_0 = _mm_loadu_si128((const __m128i *)(y[ib].qs + 16));
         bx_0 = _mm_sub_epi8(bx_0, off);
         const __m128i i32_1 = mul_sum_i8_pairs(bx_0, by_0);
 
@@ -3954,7 +3952,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = _mm256_add_ps(_mm256_mul_ps( d, p ), acc);
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__SSSE3__)
     // set constants
     const __m128i lowMask = _mm_set1_epi8(0xF);
@@ -3966,94 +3964,40 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     __m128 acc_2 = _mm_setzero_ps();
     __m128 acc_3 = _mm_setzero_ps();
 
-    // First round without accumulation
-    {
-        _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0);
+    for (; ib + 1 < nb; ib += 2) {
+        _mm_prefetch(&x[ib] + sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[ib] + sizeof(block_q8_0), _MM_HINT_T0);
 
         // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) );
+        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
 
-        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[0].qs);
+        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[ib].qs);
 
         __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
-        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[0].qs);
+        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[ib].qs);
         bx_0 = _mm_sub_epi8(bx_0, off);
         const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
 
         __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
-        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[0].qs + 16));
+        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[ib].qs + 16));
         bx_1 = _mm_sub_epi8(bx_1, off);
         const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
 
-        _mm_prefetch(&x[1] + sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[1] + sizeof(block_q8_0), _MM_HINT_T0);
+        _mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
+        _mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
 
         // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) );
+        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) );
 
-        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[1].qs);
+        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs);
 
         __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
-        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[1].qs);
+        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs);
         bx_2 = _mm_sub_epi8(bx_2, off);
         const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
 
         __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
-        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[1].qs + 16));
-        bx_3 = _mm_sub_epi8(bx_3, off);
-        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
-
-        // Convert int32_t to float
-        __m128 p0 = _mm_cvtepi32_ps(i32_0);
-        __m128 p1 = _mm_cvtepi32_ps(i32_1);
-        __m128 p2 = _mm_cvtepi32_ps(i32_2);
-        __m128 p3 = _mm_cvtepi32_ps(i32_3);
-
-        // Apply the scale
-        acc_0 = _mm_mul_ps( d_0_1, p0 );
-        acc_1 = _mm_mul_ps( d_0_1, p1 );
-        acc_2 = _mm_mul_ps( d_2_3, p2 );
-        acc_3 = _mm_mul_ps( d_2_3, p3 );
-    }
-
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    // Main loop
-    for (int i = 2; i < nb; i+=2) {
-        _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0);
-
-        // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
-
-        const __m128i tmp_0_1 = _mm_loadu_si128((const __m128i *)x[i].qs);
-
-        __m128i bx_0 = _mm_and_si128(lowMask, tmp_0_1);
-        __m128i by_0 = _mm_loadu_si128((const __m128i *)y[i].qs);
-        bx_0 = _mm_sub_epi8(bx_0, off);
-        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
-
-        __m128i bx_1 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_0_1, 4));
-        __m128i by_1 = _mm_loadu_si128((const __m128i *)(y[i].qs + 16));
-        bx_1 = _mm_sub_epi8(bx_1, off);
-        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
-
-        _mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
-
-        // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = _mm_set1_ps( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) );
-
-        const __m128i tmp_2_3 = _mm_loadu_si128((const __m128i *)x[i + 1].qs);
-
-        __m128i bx_2 = _mm_and_si128(lowMask, tmp_2_3);
-        __m128i by_2 = _mm_loadu_si128((const __m128i *)y[i + 1].qs);
-        bx_2 = _mm_sub_epi8(bx_2, off);
-        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
-
-        __m128i bx_3 = _mm_and_si128(lowMask, _mm_srli_epi64(tmp_2_3, 4));
-        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[i + 1].qs + 16));
+        __m128i by_3 = _mm_loadu_si128((const __m128i *)(y[ib + 1].qs + 16));
         bx_3 = _mm_sub_epi8(bx_3, off);
         const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
 
@@ -4076,18 +4020,16 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc_3 = _mm_add_ps(p3_d, acc_3);
     }
 
-    *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
+    sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
 #elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
     size_t vl = __riscv_vsetvl_e8m1(qk/2);
 
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         // load elements
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl);
 
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl);
 
         // mask and store lower part of x, and then upper part
         vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
@@ -4110,11 +4052,9 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
+        sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d);
     }
 
-    *s = sumf;
-
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
     const vector signed int v0 = vec_splats((int32_t)0);
@@ -4124,17 +4064,17 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vector float vsumf0 = vec_splats(0.0f);
 
 #pragma GCC unroll 8
-    for (int i = 0; i < nb; i++) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
 
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d));
+        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d));
+        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d));
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs);
-        vector signed char q8y0 = vec_xl( 0, y[i].qs);
-        vector signed char q8y1 = vec_xl(16, y[i].qs);
+        vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs);
+        vector signed char q8y0 = vec_xl( 0, y[ib].qs);
+        vector signed char q8y1 = vec_xl(16, y[ib].qs);
 
         vector signed char q4x0 = vec_and(qxs, lowMask);
         vector signed char q4x1 = vec_sr(qxs, v4);
@@ -4156,24 +4096,24 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
+    sumf = vec_extract(vsumf0, 0);
 
 #elif defined(__loongarch_asx)
     // Initialize accumulator with zeros
     __m256 acc = (__m256)__lasx_xvldi(0);
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
+    for (; ib < nb; ++ib) {
         /* Compute combined scale for the block */
-        const __m256 d = __lasx_xvreplfr2vr_s( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
+        const __m256 d = __lasx_xvreplfr2vr_s( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
 
-        __m256i qx = bytes_from_nibbles_32(x[i].qs);
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
 
         // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
         const __m256i off = __lasx_xvreplgr2vr_b( 8 );
         qx = __lasx_xvsub_b( qx, off );
 
-        __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0);
+        __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0);
 
         const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
@@ -4181,7 +4121,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = __lasx_xvfmadd_s( d, q, acc );
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__loongarch_sx)
     // set constants
     const __m128i low_mask = __lsx_vreplgr2vr_b(0xF);
@@ -4193,89 +4133,38 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     __m128 acc_2 = __lsx_vldi(0);
     __m128 acc_3 = __lsx_vldi(0);
 
-    // First round without accumulation
-    {
-        _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0);
-        _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0);
-
-        // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) );
-
-        const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[0].qs, 0);
-
-        __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1);
-        __m128i by_0 = __lsx_vld((const __m128i *)y[0].qs, 0);
-        bx_0 = __lsx_vsub_b(bx_0, off);
-        const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
-
-        __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4));
-        __m128i by_1 = __lsx_vld((const __m128i *)(y[0].qs + 16), 0);
-        bx_1 = __lsx_vsub_b(bx_1, off);
-        const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
-
-        // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) );
-
-        const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[1].qs, 0);
-
-        __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3);
-        __m128i by_2 = __lsx_vld((const __m128i *)y[1].qs, 0);
-        bx_2 = __lsx_vsub_b(bx_2, off);
-        const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
-
-        __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4));
-        __m128i by_3 = __lsx_vld((const __m128i *)(y[1].qs + 16), 0);
-        bx_3 = __lsx_vsub_b(bx_3, off);
-        const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
-
-        // Convert int32_t to float
-        __m128 p0 = __lsx_vffint_s_w(i32_0);
-        __m128 p1 = __lsx_vffint_s_w(i32_1);
-        __m128 p2 = __lsx_vffint_s_w(i32_2);
-        __m128 p3 = __lsx_vffint_s_w(i32_3);
-
-        // Apply the scale
-        acc_0 = __lsx_vfmul_s( d_0_1, p0 );
-        acc_1 = __lsx_vfmul_s( d_0_1, p1 );
-        acc_2 = __lsx_vfmul_s( d_2_3, p2 );
-        acc_3 = __lsx_vfmul_s( d_2_3, p3 );
-    }
-
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    // Main loop
-    for (int i = 2; i < nb; i+=2) {
+    for (; ib + 1 < nb; ib += 2) {
 
         // Compute combined scale for the block 0 and 1
-        const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) );
+        const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d) );
 
-        const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[i].qs, 0);
+        const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[ib].qs, 0);
 
         __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1);
-        __m128i by_0 = __lsx_vld((const __m128i *)y[i].qs, 0);
+        __m128i by_0 = __lsx_vld((const __m128i *)y[ib].qs, 0);
         bx_0 = __lsx_vsub_b(bx_0, off);
         const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0);
 
         __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4));
-        __m128i by_1 = __lsx_vld((const __m128i *)(y[i].qs + 16), 0);
+        __m128i by_1 = __lsx_vld((const __m128i *)(y[ib].qs + 16), 0);
         bx_1 = __lsx_vsub_b(bx_1, off);
         const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1);
 
-        //_mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
-        //_mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
+        //_mm_prefetch(&x[ib] + 2 * sizeof(block_q4_0), _MM_HINT_T0);
+        //_mm_prefetch(&y[ib] + 2 * sizeof(block_q8_0), _MM_HINT_T0);
 
         // Compute combined scale for the block 2 and 3
-        const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) );
+        const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[ib + 1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) );
 
-        const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[i + 1].qs, 0);
+        const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[ib + 1].qs, 0);
 
         __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3);
-        __m128i by_2 = __lsx_vld((const __m128i *)y[i + 1].qs, 0);
+        __m128i by_2 = __lsx_vld((const __m128i *)y[ib + 1].qs, 0);
         bx_2 = __lsx_vsub_b(bx_2, off);
         const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2);
 
         __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4));
-        __m128i by_3 = __lsx_vld((const __m128i *)(y[i + 1].qs + 16), 0);
+        __m128i by_3 = __lsx_vld((const __m128i *)(y[ib + 1].qs + 16), 0);
         bx_3 = __lsx_vsub_b(bx_3, off);
         const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3);
 
@@ -4298,27 +4187,22 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc_3 = __lsx_vfadd_s(p3_d, acc_3);
     }
 
-    *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
-
-#else
-    // scalar
-    float sumf = 0.0;
-
-    for (int i = 0; i < nb; i++) {
+    sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
+#endif
+    for (; ib < nb; ++ib) {
         int sumi = 0;
 
         for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[i].qs[j] & 0x0F) - 8;
-            const int v1 = (x[i].qs[j] >>   4) - 8;
+            const int v0 = (x[ib].qs[j] & 0x0F) - 8;
+            const int v1 = (x[ib].qs[j] >>   4) - 8;
 
-            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
+            sumi += (v0 * y[ib].qs[j]) + (v1 * y[ib].qs[j + qk/2]);
         }
 
-        sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d);
+        sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d);
     }
 
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
@@ -4404,11 +4288,15 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1);
         sumv2 = vaddq_f32(sumv2, summs0);
 
-        vst1_f32(s, vget_low_f32(sumv2));
+        vst1_f32(s,      vget_low_f32 (sumv2));
         vst1_f32(s + bs, vget_high_f32(sumv2));
         return;
     }
 #endif
+
+    int ib = 0;
+    float sumf = 0;
+
     // TODO: add WASM SIMD
 #if defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
@@ -4416,13 +4304,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 
     float summs = 0;
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    for (int i = 0; i < nb; i += 2) {
-        const block_q4_1 * restrict x0 = &x[i + 0];
-        const block_q4_1 * restrict x1 = &x[i + 1];
-        const block_q8_1 * restrict y0 = &y[i + 0];
-        const block_q8_1 * restrict y1 = &y[i + 1];
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q4_1 * restrict x0 = &x[ib + 0];
+        const block_q4_1 * restrict x1 = &x[ib + 1];
+        const block_q8_1 * restrict y0 = &y[ib + 0];
+        const block_q8_1 * restrict y1 = &y[ib + 1];
 
         summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s) + GGML_FP16_TO_FP32(x1->m) * GGML_FP16_TO_FP32(y1->s);
 
@@ -4451,7 +4337,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         sumv1 = vmlaq_n_f32(sumv1, vcvtq_f32_s32(p_1), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
+    sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs;
 #elif defined(__AVX2__) || defined(__AVX__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
@@ -4459,11 +4345,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     float summs = 0;
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
-        const float d0 = GGML_FP16_TO_FP32(x[i].d);
-        const float d1 = GGML_FP16_TO_FP32(y[i].d);
+    for (; ib < nb; ++ib) {
+        const float d0 = GGML_FP16_TO_FP32(x[ib].d);
+        const float d1 = GGML_FP16_TO_FP32(y[ib].d);
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
+        summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
 
         const __m256 d0v = _mm256_set1_ps( d0 );
         const __m256 d1v = _mm256_set1_ps( d1 );
@@ -4472,8 +4358,8 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         const __m256 d0d1 = _mm256_mul_ps( d0v, d1v );
 
         // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
-        const __m256i qx = bytes_from_nibbles_32(x[i].qs);
-        const __m256i qy = _mm256_loadu_si256( (const __m256i *)y[i].qs );
+        const __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        const __m256i qy = _mm256_loadu_si256( (const __m256i *)y[ib].qs );
 
         const __m256 xy = mul_sum_us8_pairs_float(qx, qy);
 
@@ -4485,18 +4371,16 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 #endif
     }
 
-    *s = hsum_float_8(acc) + summs;
+    sumf = hsum_float_8(acc) + summs;
 #elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
     size_t vl = __riscv_vsetvl_e8m1(qk/2);
 
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         // load elements
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl);
 
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl);
 
         // mask and store lower part of x, and then upper part
         vuint8mf2_t x_a = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
@@ -4515,11 +4399,9 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
     }
 
-    *s = sumf;
-
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
     const vector signed int v0 = vec_splats((int32_t)0);
@@ -4528,21 +4410,21 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     vector float vsumf0 = vec_splats(0.0f);
 
 #pragma GCC unroll 4
-    for (int i = 0; i < nb; i++) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
 
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d));
+        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d));
+        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d));
         vector float vd = vec_mul(vxd, vyd);
 
-        vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].m));
-        vector float vys = {GGML_FP16_TO_FP32(y[i].s), 0.0f, 0.0f, 0.0f};
+        vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m));
+        vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.0f, 0.0f, 0.0f};
         vsumf0 = vec_madd(vxmin, vys, vsumf0);
 
-        vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs);
-        vector signed char q8y0 = vec_xl( 0, y[i].qs);
-        vector signed char q8y1 = vec_xl(16, y[i].qs);
+        vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs);
+        vector signed char q8y0 = vec_xl( 0, y[ib].qs);
+        vector signed char q8y1 = vec_xl(16, y[ib].qs);
 
         vector unsigned char q4x0 = (vector unsigned char)vec_and(qxs, lowMask);
         vector unsigned char q4x1 = (vector unsigned char)vec_sr(qxs, v4);
@@ -4558,7 +4440,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
+    sumf = vec_extract(vsumf0, 0);
 
 #elif defined(__loongarch_asx)
     // Initialize accumulator with zeros
@@ -4567,11 +4449,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     float summs = 0;
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
-        const float d0 = GGML_FP16_TO_FP32(x[i].d);
-        const float d1 = GGML_FP16_TO_FP32(y[i].d);
+    for (; ib < nb; ++ib) {
+        const float d0 = GGML_FP16_TO_FP32(x[ib].d);
+        const float d1 = GGML_FP16_TO_FP32(y[ib].d);
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
+        summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
 
         const __m256 d0v = __lasx_xvreplfr2vr_s( d0 );
         const __m256 d1v = __lasx_xvreplfr2vr_s( d1 );
@@ -4580,8 +4462,8 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         const __m256 d0d1 = __lasx_xvfmul_s( d0v, d1v );
 
         // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes
-        const __m256i qx = bytes_from_nibbles_32(x[i].qs);
-        const __m256i qy = __lasx_xvld( (const __m256i *)y[i].qs, 0);
+        const __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        const __m256i qy = __lasx_xvld( (const __m256i *)y[ib].qs, 0);
 
         const __m256 xy = mul_sum_us8_pairs_float(qx, qy);
 
@@ -4589,33 +4471,31 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         acc = __lasx_xvfmadd_s( d0d1, xy, acc );
     }
 
-    *s = hsum_float_8(acc) + summs;
-
-#else
-    // scalar
-    float sumf = 0.0;
-
-    for (int i = 0; i < nb; i++) {
+    sumf = hsum_float_8(acc) + summs;
+#endif
+    for (; ib < nb; ++ib) {
         int sumi = 0;
 
         for (int j = 0; j < qk/2; ++j) {
-            const int v0 = (x[i].qs[j] & 0x0F);
-            const int v1 = (x[i].qs[j] >>   4);
+            const int v0 = (x[ib].qs[j] & 0x0F);
+            const int v1 = (x[ib].qs[j] >>   4);
 
-            sumi += (v0 * y[i].qs[j]) + (v1 * y[i].qs[j + qk/2]);
+            sumi += (v0 * y[ib].qs[j]) + (v1 * y[ib].qs[j + qk/2]);
         }
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
     }
 
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
 
+    int ib = 0;
+    float sumf = 0;
+
     assert(n % qk == 0);
     assert(qk == QK5_0);
     assert(nrc == 1);
@@ -4637,13 +4517,11 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     uint64_t tmp0[4];
     uint64_t tmp1[4];
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    for (int i = 0; i < nb; i += 2) {
-        const block_q5_0 * restrict x0 = &x[i];
-        const block_q5_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i];
-        const block_q8_0 * restrict y1 = &y[i + 1];
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q5_0 * restrict x0 = &x[ib];
+        const block_q5_0 * restrict x1 = &x[ib + 1];
+        const block_q8_0 * restrict y0 = &y[ib];
+        const block_q8_0 * restrict y1 = &y[ib + 1];
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
@@ -4695,7 +4573,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
                         ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+    sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
 #elif defined(__wasm_simd128__)
     v128_t sumv = wasm_f32x4_splat(0.0f);
 
@@ -4703,9 +4581,9 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     uint64_t tmp[4];
 
     // TODO: check if unrolling this is better
-    for (int i = 0; i < nb; ++i) {
-        const block_q5_0 * restrict x0 = &x[i];
-        const block_q8_0 * restrict y0 = &y[i];
+    for (; ib < nb; ++ib) {
+        const block_q5_0 * restrict x0 = &x[ib];
+        const block_q8_0 * restrict y0 = &y[ib];
 
         const v128_t m4b  = wasm_i8x16_splat(0x0F);
 
@@ -4755,23 +4633,23 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
                     wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d))));
     }
 
-    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
-         wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
+    sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
+           wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
 #elif defined(__AVX2__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
 
     // Main loop
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d));
 
-        __m256i qx = bytes_from_nibbles_32(x[i].qs);
-        __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        __m256i bxhi = bytes_from_bits_32(x[ib].qh);
         bxhi = _mm256_andnot_si256(bxhi, _mm256_set1_epi8((char)0xF0));
         qx = _mm256_or_si256(qx, bxhi);
 
-        __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
@@ -4779,19 +4657,19 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = _mm256_fmadd_ps(d, q, acc);
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__AVX__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
     __m128i mask = _mm_set1_epi8((char)0xF0);
 
     // Main loop
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         /* Compute combined scale for the block */
-        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d));
 
-        __m256i bx_0 = bytes_from_nibbles_32(x[i].qs);
-        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs);
+        const __m256i bxhi = bytes_from_bits_32(x[ib].qh);
         __m128i bxhil = _mm256_castsi256_si128(bxhi);
         __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
         bxhil = _mm_andnot_si128(bxhil, mask);
@@ -4802,7 +4680,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         bxh = _mm_or_si128(bxh, bxhih);
         bx_0 = MM256_SET_M128I(bxh, bxl);
 
-        const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_i8_pairs_float(bx_0, by_0);
 
@@ -4810,10 +4688,8 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = _mm256_add_ps(_mm256_mul_ps(d, q), acc);
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
     uint32_t qh;
 
     size_t vl = __riscv_vsetvl_e8m1(qk/2);
@@ -4825,8 +4701,8 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vuint32m2_t vt_3 = __riscv_vsll_vx_u32m2(vt_2, 16, vl);
     vuint32m2_t vt_4 = __riscv_vadd_vx_u32m2(vt_1, 12, vl);
 
-    for (int i = 0; i < nb; i++) {
-        memcpy(&qh, x[i].qh, sizeof(uint32_t));
+    for (; ib < nb; ++ib) {
+        memcpy(&qh, x[ib].qh, sizeof(uint32_t));
 
         // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
         vuint32m2_t xha_0 = __riscv_vand_vx_u32m2(vt_2, qh, vl);
@@ -4845,10 +4721,10 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl);
 
         // load
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl);
 
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl);
 
         vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
         vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
@@ -4875,8 +4751,6 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
     }
 
-    *s = sumf;
-
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
     const vector unsigned char v4 = vec_splats((unsigned char)4);
@@ -4884,27 +4758,27 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vector float vsumf0 = vec_splats(0.0f);
 
 #pragma GCC unroll 4
-    for (int i = 0; i < nb; ++i) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
 
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d));
+        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d));
+        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d));
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed long long aux64x2_0 = {(uint64_t)(table_b2b_1[x[i].qh[0]]), (uint64_t)(table_b2b_1[x[i].qh[1]])};
-        vector signed long long aux64x2_1 = {(uint64_t)(table_b2b_1[x[i].qh[2]]), (uint64_t)(table_b2b_1[x[i].qh[3]])};
+        vector signed long long aux64x2_0 = {(uint64_t)(table_b2b_1[x[ib].qh[0]]), (uint64_t)(table_b2b_1[x[ib].qh[1]])};
+        vector signed long long aux64x2_1 = {(uint64_t)(table_b2b_1[x[ib].qh[2]]), (uint64_t)(table_b2b_1[x[ib].qh[3]])};
 
         vector signed char qh0 = (vector signed char)aux64x2_0;
         vector signed char qh1 = (vector signed char)aux64x2_1;
 
-        vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs);
+        vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs);
 
         vector signed char q5x0 = vec_sub(vec_and (qxs, lowMask), qh0);
         vector signed char q5x1 = vec_sub(vec_sr(qxs, v4), qh1);
 
-        vector signed char q8y0 = vec_xl(  0, y[i].qs);
-        vector signed char q8y1 = vec_xl( 16, y[i].qs);
+        vector signed char q8y0 = vec_xl(  0, y[ib].qs);
+        vector signed char q8y1 = vec_xl( 16, y[ib].qs);
 
         vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0));
         vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1));
@@ -4919,23 +4793,23 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
+    sumf = vec_extract(vsumf0, 0);
 
 #elif defined(__loongarch_asx)
     // Initialize accumulator with zeros
     __m256 acc = (__m256)__lasx_xvldi(0);
 
     // Main loop
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         /* Compute combined scale for the block */
-        const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); //FIXME
+        const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d)); //FIXME
 
-        __m256i qx = bytes_from_nibbles_32(x[i].qs);
-        __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        __m256i bxhi = bytes_from_bits_32(x[ib].qh);
         bxhi = __lasx_xvandn_v(bxhi, __lasx_xvreplgr2vr_b((char)0xF0));
         qx = __lasx_xvor_v(qx, bxhi);
 
-        __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0);
+        __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0);
 
         const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
@@ -4943,15 +4817,11 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = __lasx_xvfmadd_s(d, q, acc);
     }
 
-    *s = hsum_float_8(acc);
-
-#else
-    // scalar
-    float sumf = 0.0;
-
-    for (int i = 0; i < nb; i++) {
+    sumf = hsum_float_8(acc);
+#endif
+    for (; ib < nb; ++ib) {
         uint32_t qh;
-        memcpy(&qh, x[i].qh, sizeof(qh));
+        memcpy(&qh, x[ib].qh, sizeof(qh));
 
         int sumi = 0;
 
@@ -4959,23 +4829,25 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
             const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
             const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
 
-            const int32_t x0 = ((x[i].qs[j] & 0x0F) | xh_0) - 16;
-            const int32_t x1 = ((x[i].qs[j] >>   4) | xh_1) - 16;
+            const int32_t x0 = ((x[ib].qs[j] & 0x0F) | xh_0) - 16;
+            const int32_t x1 = ((x[ib].qs[j] >>   4) | xh_1) - 16;
 
-            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
+            sumi += (x0 * y[ib].qs[j]) + (x1 * y[ib].qs[j + qk/2]);
         }
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi;
     }
 
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
     const int qk = QK8_1;
     const int nb = n / qk;
 
+    int ib = 0;
+    float sumf = 0;
+
     assert(n % qk == 0);
     assert(qk == QK5_1);
     assert(nrc == 1);
@@ -5000,13 +4872,11 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     uint64_t tmp0[4];
     uint64_t tmp1[4];
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    for (int i = 0; i < nb; i += 2) {
-        const block_q5_1 * restrict x0 = &x[i];
-        const block_q5_1 * restrict x1 = &x[i + 1];
-        const block_q8_1 * restrict y0 = &y[i];
-        const block_q8_1 * restrict y1 = &y[i + 1];
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q5_1 * restrict x0 = &x[ib];
+        const block_q5_1 * restrict x1 = &x[ib + 1];
+        const block_q8_1 * restrict y0 = &y[ib];
+        const block_q8_1 * restrict y1 = &y[ib + 1];
 
         const uint8x16_t m4b = vdupq_n_u8(0x0F);
 
@@ -5061,7 +4931,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
                         ggml_vdotq_s32(vdupq_n_s32(0), v0_1hf, v1_1h))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
+    sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1) + summs0 + summs1;
 #elif defined(__wasm_simd128__)
     v128_t sumv = wasm_f32x4_splat(0.0f);
 
@@ -5071,9 +4941,9 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     uint64_t tmp[4];
 
     // TODO: check if unrolling this is better
-    for (int i = 0; i < nb; ++i) {
-        const block_q5_1 * restrict x0 = &x[i];
-        const block_q8_1 * restrict y0 = &y[i];
+    for (; ib < nb; ++ib) {
+        const block_q5_1 * restrict x0 = &x[ib];
+        const block_q8_1 * restrict y0 = &y[ib];
 
         summs += GGML_FP16_TO_FP32(x0->m) * GGML_FP16_TO_FP32(y0->s);
 
@@ -5125,8 +4995,8 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
                     wasm_f32x4_splat(GGML_FP16_TO_FP32(x0->d) * GGML_FP16_TO_FP32(y0->d))));
     }
 
-    *s = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
-         wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
+    sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
+           wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
 #elif defined(__AVX2__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
@@ -5134,25 +5004,25 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     float summs = 0.0f;
 
     // Main loop
-    for (int i = 0; i < nb; i++) {
-        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
+    for (; ib < nb; ++ib) {
+        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d));
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
+        summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
 
-        __m256i qx = bytes_from_nibbles_32(x[i].qs);
-        __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        __m256i bxhi = bytes_from_bits_32(x[ib].qh);
         bxhi = _mm256_and_si256(bxhi, _mm256_set1_epi8(0x10));
         qx = _mm256_or_si256(qx, bxhi);
 
-        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[i].d));
-        const __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d));
+        const __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_us8_pairs_float(qx, qy);
 
         acc = _mm256_fmadd_ps(q, _mm256_mul_ps(dx, dy), acc);
     }
 
-    *s = hsum_float_8(acc) + summs;
+    sumf = hsum_float_8(acc) + summs;
 #elif defined(__AVX__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
@@ -5161,13 +5031,13 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     float summs = 0.0f;
 
     // Main loop
-    for (int i = 0; i < nb; i++) {
-        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d));
+    for (; ib < nb; ++ib) {
+        const __m256 dx = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d));
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
+        summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
 
-        __m256i bx_0 = bytes_from_nibbles_32(x[i].qs);
-        const __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m256i bx_0 = bytes_from_nibbles_32(x[ib].qs);
+        const __m256i bxhi = bytes_from_bits_32(x[ib].qh);
         __m128i bxhil = _mm256_castsi256_si128(bxhi);
         __m128i bxhih = _mm256_extractf128_si256(bxhi, 1);
         bxhil = _mm_and_si128(bxhil, mask);
@@ -5178,18 +5048,16 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         bxh = _mm_or_si128(bxh, bxhih);
         bx_0 = MM256_SET_M128I(bxh, bxl);
 
-        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[i].d));
-        const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256 dy = _mm256_set1_ps(GGML_FP16_TO_FP32(y[ib].d));
+        const __m256i by_0 = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_us8_pairs_float(bx_0, by_0);
 
         acc = _mm256_add_ps(_mm256_mul_ps(q, _mm256_mul_ps(dx, dy)), acc);
     }
 
-    *s = hsum_float_8(acc) + summs;
+    sumf = hsum_float_8(acc) + summs;
 #elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
-
     uint32_t qh;
 
     size_t vl = __riscv_vsetvl_e8m1(qk/2);
@@ -5198,8 +5066,8 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     vuint32m2_t vt_1 = __riscv_vid_v_u32m2(vl);
     vuint32m2_t vt_2 = __riscv_vadd_vx_u32m2(vt_1, 12, vl);
 
-    for (int i = 0; i < nb; i++) {
-        memcpy(&qh, x[i].qh, sizeof(uint32_t));
+    for (; ib < nb; ++ib) {
+        memcpy(&qh, x[ib].qh, sizeof(uint32_t));
 
         // load qh
         vuint32m2_t vqh = __riscv_vmv_v_x_u32m2(qh, vl);
@@ -5221,10 +5089,10 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
         vuint8mf2_t xh_1 = __riscv_vncvt_x_x_w_u8mf2(xhc_1, vl);
 
         // load
-        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[i].qs, vl);
+        vuint8mf2_t tx = __riscv_vle8_v_u8mf2(x[ib].qs, vl);
 
-        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[i].qs, vl);
-        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[i].qs+16, vl);
+        vint8mf2_t y0 = __riscv_vle8_v_i8mf2(y[ib].qs, vl);
+        vint8mf2_t y1 = __riscv_vle8_v_i8mf2(y[ib].qs+16, vl);
 
         vuint8mf2_t x_at = __riscv_vand_vx_u8mf2(tx, 0x0F, vl);
         vuint8mf2_t x_lt = __riscv_vsrl_vx_u8mf2(tx, 0x04, vl);
@@ -5245,11 +5113,9 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
     }
 
-    *s = sumf;
-
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
     const vector signed int v0 = vec_splats((int32_t)0);
@@ -5258,31 +5124,31 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     vector float vsumf0 = vec_splats(0.0f);
 
 #pragma GCC unroll 4
-    for (int i = 0; i < nb; ++i) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
 
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d));
+        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d));
+        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d));
         vector float vd = vec_mul(vxd, vyd);
 
-        vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].m));
-        vector float vys = {GGML_FP16_TO_FP32(y[i].s), 0.f, 0.f, 0.f};
+        vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[ib].m));
+        vector float vys = {GGML_FP16_TO_FP32(y[ib].s), 0.f, 0.f, 0.f};
         vsumf0 = vec_madd(vxmin, vys, vsumf0);
 
-        vector unsigned long long aux64x2_0 = {(uint64_t)(table_b2b_0[x[i].qh[0]]), (uint64_t)(table_b2b_0[x[i].qh[1]])};
-        vector unsigned long long aux64x2_1 = {(uint64_t)(table_b2b_0[x[i].qh[2]]), (uint64_t)(table_b2b_0[x[i].qh[3]])};
+        vector unsigned long long aux64x2_0 = {(uint64_t)(table_b2b_0[x[ib].qh[0]]), (uint64_t)(table_b2b_0[x[ib].qh[1]])};
+        vector unsigned long long aux64x2_1 = {(uint64_t)(table_b2b_0[x[ib].qh[2]]), (uint64_t)(table_b2b_0[x[ib].qh[3]])};
 
         vector signed char qh0 = (vector signed char)aux64x2_0;
         vector signed char qh1 = (vector signed char)aux64x2_1;
 
-        vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs);
+        vector signed char qxs = (vector signed char)vec_xl( 0, x[ib].qs);
 
         vector unsigned char q5x0 = (vector unsigned char)vec_or(vec_and(qxs, lowMask), qh0);
         vector unsigned char q5x1 = (vector unsigned char)vec_or(vec_sr(qxs, v4), qh1);
 
-        vector signed char q8y0 = vec_xl(  0, y[i].qs);
-        vector signed char q8y1 = vec_xl( 16, y[i].qs);
+        vector signed char q8y0 = vec_xl(  0, y[ib].qs);
+        vector signed char q8y1 = vec_xl( 16, y[ib].qs);
 
         vector signed int vsumi0 = v0;
 
@@ -5295,7 +5161,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
+    sumf = vec_extract(vsumf0, 0);
 
 #elif defined(__loongarch_asx)
     // Initialize accumulator with zeros
@@ -5304,33 +5170,29 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
     float summs = 0.0f;
 
     // Main loop
-    for (int i = 0; i < nb; i++) {
-        const __m256 dx = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[i].d));
+    for (; ib < nb; ++ib) {
+        const __m256 dx = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d));
 
-        summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s);
+        summs += GGML_FP16_TO_FP32(x[ib].m) * GGML_FP16_TO_FP32(y[ib].s);
 
-        __m256i qx = bytes_from_nibbles_32(x[i].qs);
-        __m256i bxhi = bytes_from_bits_32(x[i].qh);
+        __m256i qx = bytes_from_nibbles_32(x[ib].qs);
+        __m256i bxhi = bytes_from_bits_32(x[ib].qh);
         bxhi = __lasx_xvand_v(bxhi, __lasx_xvreplgr2vr_b(0x10));
         qx = __lasx_xvor_v(qx, bxhi);
 
-        const __m256 dy = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[i].d));
-        const __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0);
+        const __m256 dy = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib].d));
+        const __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0);
 
         const __m256 q = mul_sum_us8_pairs_float(qx, qy);
 
         acc = __lasx_xvfmadd_s(q, __lasx_xvfmul_s(dx, dy), acc);
     }
 
-    *s = hsum_float_8(acc) + summs;
-
-#else
-    // scalar
-    float sumf = 0.0;
-
-    for (int i = 0; i < nb; i++) {
+    sumf = hsum_float_8(acc) + summs;
+#endif
+    for (; ib < nb; ++ib) {
         uint32_t qh;
-        memcpy(&qh, x[i].qh, sizeof(qh));
+        memcpy(&qh, x[ib].qh, sizeof(qh));
 
         int sumi = 0;
 
@@ -5338,17 +5200,16 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
             const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
             const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
 
-            const int32_t x0 = (x[i].qs[j] & 0xF) | xh_0;
-            const int32_t x1 = (x[i].qs[j] >>  4) | xh_1;
+            const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
+            const int32_t x1 = (x[ib].qs[j] >>  4) | xh_1;
 
-            sumi += (x0 * y[i].qs[j]) + (x1 * y[i].qs[j + qk/2]);
+            sumi += (x0 * y[ib].qs[j]) + (x1 * y[ib].qs[j + qk/2]);
         }
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d))*sumi + GGML_FP16_TO_FP32(x[i].m)*GGML_FP16_TO_FP32(y[i].s);
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
     }
 
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
@@ -5425,18 +5286,20 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         return;
     }
 #endif
+
+    int ib = 0;
+    float sumf = 0;
+
 #if defined(__ARM_FEATURE_SVE)
     if (svcntb() == QK8_0) {
         svfloat32_t sumv0 = svdup_n_f32(0.0f);
         svfloat32_t sumv1 = svdup_n_f32(0.0f);
 
-        assert(nb % 2 == 0); // TODO: handle odd nb
-
-        for (int i = 0; i < nb; i += 2) {
-            const block_q8_0 * restrict x0 = &x[i + 0];
-            const block_q8_0 * restrict x1 = &x[i + 1];
-            const block_q8_0 * restrict y0 = &y[i + 0];
-            const block_q8_0 * restrict y1 = &y[i + 1];
+        for (; ib + 1 < nb; ib += 2) {
+            const block_q8_0 * restrict x0 = &x[ib + 0];
+            const block_q8_0 * restrict x1 = &x[ib + 1];
+            const block_q8_0 * restrict y0 = &y[ib + 0];
+            const block_q8_0 * restrict y1 = &y[ib + 1];
 
             // load x
             const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs);
@@ -5450,21 +5313,17 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
             sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
         }
 
-        *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
-        return;
+        sumf = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1));
     }
-#endif
-#if defined(__ARM_NEON)
+#elif defined(__ARM_NEON)
     float32x4_t sumv0 = vdupq_n_f32(0.0f);
     float32x4_t sumv1 = vdupq_n_f32(0.0f);
 
-    assert(nb % 2 == 0); // TODO: handle odd nb
-
-    for (int i = 0; i < nb; i += 2) {
-        const block_q8_0 * restrict x0 = &x[i + 0];
-        const block_q8_0 * restrict x1 = &x[i + 1];
-        const block_q8_0 * restrict y0 = &y[i + 0];
-        const block_q8_0 * restrict y1 = &y[i + 1];
+    for (; ib + 1 < nb; ib += 2) {
+        const block_q8_0 * restrict x0 = &x[ib + 0];
+        const block_q8_0 * restrict x1 = &x[ib + 1];
+        const block_q8_0 * restrict y0 = &y[ib + 0];
+        const block_q8_0 * restrict y1 = &y[ib + 1];
 
         const int8x16_t x0_0 = vld1q_s8(x0->qs);
         const int8x16_t x0_1 = vld1q_s8(x0->qs + 16);
@@ -5486,17 +5345,17 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
                         ggml_vdotq_s32(vdupq_n_s32(0), x1_1, y1_1))), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d));
     }
 
-    *s = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
+    sumf = vaddvq_f32(sumv0) + vaddvq_f32(sumv1);
 #elif defined(__AVX2__) || defined(__AVX__)
     // Initialize accumulator with zeros
     __m256 acc = _mm256_setzero_ps();
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
+    for (; ib < nb; ++ib) {
         // Compute combined scale for the block
-        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
-        __m256i qx = _mm256_loadu_si256((const __m256i *)x[i].qs);
-        __m256i qy = _mm256_loadu_si256((const __m256i *)y[i].qs);
+        const __m256 d = _mm256_set1_ps(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d));
+        __m256i qx = _mm256_loadu_si256((const __m256i *)x[ib].qs);
+        __m256i qy = _mm256_loadu_si256((const __m256i *)y[ib].qs);
 
         const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
@@ -5508,15 +5367,14 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
 #endif
     }
 
-    *s = hsum_float_8(acc);
+    sumf = hsum_float_8(acc);
 #elif defined(__riscv_v_intrinsic)
-    float sumf = 0.0;
     size_t vl = __riscv_vsetvl_e8m1(qk);
 
-    for (int i = 0; i < nb; i++) {
+    for (; ib < nb; ++ib) {
         // load elements
-        vint8m1_t bx_0 = __riscv_vle8_v_i8m1(x[i].qs, vl);
-        vint8m1_t by_0 = __riscv_vle8_v_i8m1(y[i].qs, vl);
+        vint8m1_t bx_0 = __riscv_vle8_v_i8m1(x[ib].qs, vl);
+        vint8m1_t by_0 = __riscv_vle8_v_i8m1(y[ib].qs, vl);
 
         vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx_0, by_0, vl);
 
@@ -5525,28 +5383,25 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum);
 
-        sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
+        sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d));
     }
-
-    *s = sumf;
-
 #elif defined(__POWER9_VECTOR__)
     const vector signed int v0 = vec_splats((int32_t)0);
     vector float vsumf0 = vec_splats(0.0f);
 
 #pragma GCC unroll 8
-    for (int i = 0; i < nb; i++) {
-        __builtin_prefetch(x[i].qs, 0, 1);
-        __builtin_prefetch(y[i].qs, 0, 1);
+    for (; ib < nb; ++ib) {
+        __builtin_prefetch(x[ib].qs, 0, 1);
+        __builtin_prefetch(y[ib].qs, 0, 1);
 
-        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d));
-        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[i].d));
+        vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[ib].d));
+        vector float vyd = vec_splats(GGML_FP16_TO_FP32(y[ib].d));
         vector float vd = vec_mul(vxd, vyd);
 
-        vector signed char q8x0 = vec_xl( 0, x[i].qs);
-        vector signed char q8x1 = vec_xl(16, x[i].qs);
-        vector signed char q8y0 = vec_xl( 0, y[i].qs);
-        vector signed char q8y1 = vec_xl(16, y[i].qs);
+        vector signed char q8x0 = vec_xl( 0, x[ib].qs);
+        vector signed char q8x1 = vec_xl(16, x[ib].qs);
+        vector signed char q8y0 = vec_xl( 0, y[ib].qs);
+        vector signed char q8y1 = vec_xl(16, y[ib].qs);
 
         vector signed short qv0 = vec_mule(q8x0, q8y0);
         vector signed short qv1 = vec_mulo(q8x0, q8y0);
@@ -5569,18 +5424,18 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
+    sumf = vec_extract(vsumf0, 0);
 
 #elif defined(__loongarch_asx)
     // Initialize accumulator with zeros
     __m256 acc = (__m256)__lasx_xvldi(0);
 
     // Main loop
-    for (int i = 0; i < nb; ++i) {
+    for (; ib < nb; ++ib) {
         // Compute combined scale for the block
-        const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d));
-        __m256i qx = __lasx_xvld((const __m256i *)x[i].qs, 0);
-        __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0);
+        const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ib].d) * GGML_FP16_TO_FP32(y[ib].d));
+        __m256i qx = __lasx_xvld((const __m256i *)x[ib].qs, 0);
+        __m256i qy = __lasx_xvld((const __m256i *)y[ib].qs, 0);
 
         const __m256 q = mul_sum_i8_pairs_float(qx, qy);
 
@@ -5588,24 +5443,19 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r
         acc = __lasx_xvfmadd_s( d, q, acc );
     }
 
-    *s = hsum_float_8(acc);
-
-#else
-    // scalar
-    float sumf = 0.0;
-
-    for (int i = 0; i < nb; i++) {
+    sumf = hsum_float_8(acc);
+#endif
+    for (; ib < nb; ++ib) {
         int sumi = 0;
 
         for (int j = 0; j < qk; j++) {
-            sumi += x[i].qs[j]*y[i].qs[j];
+            sumi += x[ib].qs[j]*y[ib].qs[j];
         }
 
-        sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d));
+        sumf += sumi*(GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d));
     }
 
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
@@ -11745,6 +11595,9 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
 
     const int nb = n / QK4_NL;
 
+    int ib = 0;
+    float sumf = 0;
+
 #if defined __ARM_NEON
     const int8x16_t values = vld1q_s8(kvalues_iq4nl);
     const uint8x16_t m4b = vdupq_n_u8(0x0f);
@@ -11753,16 +11606,14 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
     int8x16x4_t q8b;
     int32x4_t prod_1, prod_2;
 
-    float sumf = 0;
-
-    for (int ib = 0; ib < nb; ib += 2) {
+    for (; ib + 1 < nb; ib += 2) {
 
-        q4bits.val[0] = vld1q_u8(x[ib+0].qs);
-        q4bits.val[1] = vld1q_u8(x[ib+1].qs);
-        q8b.val[0]    = vld1q_s8(y[ib+0].qs);
-        q8b.val[1]    = vld1q_s8(y[ib+0].qs + 16);
-        q8b.val[2]    = vld1q_s8(y[ib+1].qs);
-        q8b.val[3]    = vld1q_s8(y[ib+1].qs + 16);
+        q4bits.val[0] = vld1q_u8(x[ib + 0].qs);
+        q4bits.val[1] = vld1q_u8(x[ib + 1].qs);
+        q8b.val[0]    = vld1q_s8(y[ib + 0].qs);
+        q8b.val[1]    = vld1q_s8(y[ib + 0].qs + 16);
+        q8b.val[2]    = vld1q_s8(y[ib + 1].qs);
+        q8b.val[3]    = vld1q_s8(y[ib + 1].qs + 16);
 
         q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[0], m4b));
         q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
@@ -11773,12 +11624,10 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
         prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
 
         sumf +=
-            GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib+0].d) * vaddvq_s32(prod_1) +
-            GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib+1].d) * vaddvq_s32(prod_2);
+            GGML_FP16_TO_FP32(x[ib+0].d) * GGML_FP16_TO_FP32(y[ib + 0].d) * vaddvq_s32(prod_1) +
+            GGML_FP16_TO_FP32(x[ib+1].d) * GGML_FP16_TO_FP32(y[ib + 1].d) * vaddvq_s32(prod_2);
     }
 
-    *s = sumf;
-
 #elif defined __AVX2__
 
     const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl);
@@ -11787,11 +11636,11 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
 
     __m256 accum1 = _mm256_setzero_ps();
     __m256 accum2 = _mm256_setzero_ps();
-    for (int ib = 0; ib < nb; ib += 2) {
-        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[0].qs);
-        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[1].qs);
-        const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[0].qs);
-        const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[1].qs);
+    for (; ib + 1 < nb; ib += 2) {
+        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[ib + 0].qs);
+        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[ib + 1].qs);
+        const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[ib + 0].qs);
+        const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[ib + 1].qs);
         const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
                                               _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
         const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
@@ -11800,16 +11649,13 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
         const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
         const __m256i p_1 = _mm256_madd_epi16(p16_1, mone);
         const __m256i p_2 = _mm256_madd_epi16(p16_2, mone);
-        accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[0].d)*GGML_FP16_TO_FP32(x[0].d)),
+        accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)),
                 _mm256_cvtepi32_ps(p_1), accum1);
-        accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[1].d)*GGML_FP16_TO_FP32(x[1].d)),
+        accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)),
                 _mm256_cvtepi32_ps(p_2), accum2);
-
-        y += 2;
-        x += 2;
     }
 
-    *s = hsum_float_8(_mm256_add_ps(accum1, accum2));
+    sumf = hsum_float_8(_mm256_add_ps(accum1, accum2));
 
 #elif defined __AVX__
     const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_iq4nl);
@@ -11818,13 +11664,13 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
 
     __m256 accum1 = _mm256_setzero_ps();
     __m256 accum2 = _mm256_setzero_ps();
-    for (int ib = 0; ib < nb; ib += 2) {
-        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[0].qs);
-        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[1].qs);
-        const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[0].qs);
-        const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[0].qs + 1);
-        const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[1].qs);
-        const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[1].qs + 1);
+    for (; ib + 1 < nb; ib += 2) {
+        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs);
+        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs);
+        const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs);
+        const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1);
+        const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs);
+        const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1);
 
         const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b));
         const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b));
@@ -11838,16 +11684,13 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
         const __m128i p_1_1 = _mm_madd_epi16(p16_1_1, mone);
         const __m128i p_2_0 = _mm_madd_epi16(p16_2_0, mone);
         const __m128i p_2_1 = _mm_madd_epi16(p16_2_1, mone);
-        accum1 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[0].d)*GGML_FP16_TO_FP32(x[0].d)),
+        accum1 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)),
                 _mm256_cvtepi32_ps(MM256_SET_M128I(p_1_1, p_1_0))), accum1);
-        accum2 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[1].d)*GGML_FP16_TO_FP32(x[1].d)),
+        accum2 = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)),
                 _mm256_cvtepi32_ps(MM256_SET_M128I(p_2_1, p_2_0))), accum2);
-
-        y += 2;
-        x += 2;
     }
 
-    *s = hsum_float_8(_mm256_add_ps(accum1, accum2));
+    sumf = hsum_float_8(_mm256_add_ps(accum1, accum2));
 
 #elif defined(__POWER9_VECTOR__)
     const vector signed char lowMask = vec_splats((signed char)0xF);
@@ -11860,7 +11703,7 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
     const vector signed char values = vec_xl( 0, kvalues_iq4nl);
 
 #pragma GCC unroll 4
-    for (int ib = 0; ib < nb; ++ib) {
+    for (; ib < nb; ++ib) {
         __builtin_prefetch(x[ib].qs, 0, 1);
         __builtin_prefetch(y[ib].qs, 0, 1);
 
@@ -11897,7 +11740,7 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4));
     vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8));
 
-    *s = vec_extract(vsumf0, 0);
+    sumf = vec_extract(vsumf0, 0);
 
 #elif defined (__loongarch_asx)
 
@@ -11907,11 +11750,11 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
 
     __m256 accum1 = (__m256)__lasx_xvldi(0);
     __m256 accum2 = (__m256)__lasx_xvldi(0);
-    for (int ib = 0; ib < nb; ib += 2) {
-        const __m128i q4bits_1 = __lsx_vld((const __m128i*)x[0].qs, 0);
-        const __m128i q4bits_2 = __lsx_vld((const __m128i*)x[1].qs, 0);
-        const __m256i q8b_1 = __lasx_xvld((const __m256i *)y[0].qs, 0);
-        const __m256i q8b_2 = __lasx_xvld((const __m256i *)y[1].qs, 0);
+    for (; ib + 1 < nb; ib += 2) {
+        const __m128i q4bits_1 = __lsx_vld((const __m128i*)x[ib + 0].qs, 0);
+        const __m128i q4bits_2 = __lsx_vld((const __m128i*)x[ib + 1].qs, 0);
+        const __m256i q8b_1 = __lasx_xvld((const __m256i *)y[ib + 0].qs, 0);
+        const __m256i q8b_2 = __lasx_xvld((const __m256i *)y[ib + 1].qs, 0);
         const __m256i q4b_1 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b)),
                                               lsx_shuffle_b(values128, __lsx_vand_v(q4bits_1, m4b)));
         const __m256i q4b_2 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b)),
@@ -11920,20 +11763,16 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
         const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
         const __m256i p_1 = lasx_madd_h(p16_1, mone);
         const __m256i p_2 = lasx_madd_h(p16_2, mone);
-        accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[0].d)*GGML_FP16_TO_FP32(x[0].d)),
+        accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 0].d)*GGML_FP16_TO_FP32(x[ib + 0].d)),
                 __lasx_xvffint_s_w(p_1), accum1);
-        accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[1].d)*GGML_FP16_TO_FP32(x[1].d)),
+        accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[ib + 1].d)*GGML_FP16_TO_FP32(x[ib + 1].d)),
                 __lasx_xvffint_s_w(p_2), accum2);
-
-        y += 2;
-        x += 2;
     }
 
-    *s = hsum_float_8(__lasx_xvfadd_s(accum1, accum2));
+    sumf = hsum_float_8(__lasx_xvfadd_s(accum1, accum2));
 
-#else
-    float sumf = 0;
-    for (int ib = 0; ib < nb; ++ib) {
+#endif
+    for (; ib < nb; ++ib) {
         const float d = GGML_FP16_TO_FP32(y[ib].d)*GGML_FP16_TO_FP32(x[ib].d);
         int sumi1 = 0, sumi2 = 0;
         for (int j = 0; j < QK4_NL/2; ++j) {
@@ -11943,7 +11782,6 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void *
         sumf += d * (sumi1 + sumi2);
     }
     *s = sumf;
-#endif
 }
 
 void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) {
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 5c309d4288f8f4..2c03c60d469230 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -79,8 +79,16 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
                 im = nullptr;
             }
         }
+
         ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
         GGML_ASSERT(ggml_validate_row_data(tensor->type, dataq.data(), dataq.size()));
+        // TODO: other cases
+        //#pragma omp parallel for
+        //for (int i = 0; i < tensor->ne[1]; i++) {
+        //    ggml_quantize_chunk(tensor->type, data.data(), dataq.data(),
+        //        i * tensor->ne[0], 1, tensor->ne[0], im);
+        //}
+
         ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
     } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
         // This is going to create some weird integers though.
@@ -2220,6 +2228,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
     }
 
+#if 1
     for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
             test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1,  1}, {1, 1}));
@@ -2239,6 +2248,24 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
             test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 2}));
         }
     }
+#else
+    // m = a rows
+    // n = b rows
+    // k = cols
+    std::uniform_int_distribution<> dist_m(1, 128);
+    std::uniform_int_distribution<> dist_n(16, 128);
+    std::uniform_int_distribution<> dist_k(1, 16);
+    for (int i = 0; i < 1000; i++) {
+        for (ggml_type type_a : all_types) {
+            for (ggml_type type_b : {GGML_TYPE_F32}) {
+                int m = dist_m(rng);
+                int n = dist_n(rng);
+                int k = dist_k(rng) * ggml_blck_size(type_a);
+                test_cases.emplace_back(new test_mul_mat(type_a, type_b, m, n, k, { 1,  1}, {1, 1}));
+            }
+        }
+    }
+#endif
 
     for (ggml_type type_a : other_types) {
         for (ggml_type type_b : {GGML_TYPE_F32}) {

From c3776cacabce2ee35f172fb72be7a519752125fa Mon Sep 17 00:00:00 2001
From: Brian <mofosyne@gmail.com>
Date: Sat, 20 Jul 2024 17:35:25 +1000
Subject: [PATCH 083/125] gguf_dump.py: fix markddown kv array print (#8588)

* gguf_dump.py: fix markddown kv array print

* Update gguf-py/scripts/gguf_dump.py

Co-authored-by: compilade <git@compilade.net>

* gguf_dump.py: refactor kv array string handling

* gguf_dump.py: escape backticks inside of strings

* gguf_dump.py: inline code markdown escape handler added

>>> escape_markdown_inline_code("hello world")
'`hello world`'
>>> escape_markdown_inline_code("hello ` world")
'``hello ` world``'

* gguf_dump.py: handle edge case about backticks on start or end of a string

---------

Co-authored-by: compilade <git@compilade.net>
---
 gguf-py/scripts/gguf_dump.py | 43 +++++++++++++++++++++++++++++++-----
 1 file changed, 38 insertions(+), 5 deletions(-)

diff --git a/gguf-py/scripts/gguf_dump.py b/gguf-py/scripts/gguf_dump.py
index a73ca2776d32b3..1b65465419ddb1 100755
--- a/gguf-py/scripts/gguf_dump.py
+++ b/gguf-py/scripts/gguf_dump.py
@@ -4,6 +4,7 @@
 import logging
 import argparse
 import os
+import re
 import sys
 from pathlib import Path
 from typing import Any
@@ -244,26 +245,58 @@ def dump_markdown_metadata(reader: GGUFReader, args: argparse.Namespace) -> None
         else:
             pretty_type = str(field.types[-1].name)
 
+        def escape_markdown_inline_code(value_string):
+            # Find the longest contiguous sequence of backticks in the string then
+            # wrap string with appropriate number of backticks required to escape it
+            max_backticks = max((len(match.group(0)) for match in re.finditer(r'`+', value_string)), default=0)
+            inline_code_marker = '`' * (max_backticks + 1)
+
+            # If the string starts or ends with a backtick, add a space at the beginning and end
+            if value_string.startswith('`') or value_string.endswith('`'):
+                value_string = f" {value_string} "
+
+            return f"{inline_code_marker}{value_string}{inline_code_marker}"
+
         total_elements = len(field.data)
         value = ""
         if len(field.types) == 1:
             curr_type = field.types[0]
             if curr_type == GGUFValueType.STRING:
-                value = repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60])
+                truncate_length = 60
+                value_string = str(bytes(field.parts[-1]), encoding='utf-8')
+                if len(value_string) > truncate_length:
+                    head = escape_markdown_inline_code(value_string[:truncate_length // 2])
+                    tail = escape_markdown_inline_code(value_string[-truncate_length // 2:])
+                    value = "{head}...{tail}".format(head=head, tail=tail)
+                else:
+                    value = escape_markdown_inline_code(value_string)
             elif curr_type in reader.gguf_scalar_to_np:
                 value = str(field.parts[-1][0])
         else:
             if field.types[0] == GGUFValueType.ARRAY:
                 curr_type = field.types[1]
+                array_elements = []
+
                 if curr_type == GGUFValueType.STRING:
                     render_element = min(5, total_elements)
                     for element_pos in range(render_element):
-                        value += repr(str(bytes(field.parts[-1 - element_pos]), encoding='utf-8')[:5]) + (", " if total_elements > 1 else "")
+                        truncate_length = 30
+                        value_string = str(bytes(field.parts[-1 - (total_elements - element_pos - 1) * 2]), encoding='utf-8')
+                        if len(value_string) > truncate_length:
+                            head = escape_markdown_inline_code(value_string[:truncate_length // 2])
+                            tail = escape_markdown_inline_code(value_string[-truncate_length // 2:])
+                            value = "{head}...{tail}".format(head=head, tail=tail)
+                        else:
+                            value = escape_markdown_inline_code(value_string)
+                        array_elements.append(value)
+
                 elif curr_type in reader.gguf_scalar_to_np:
                     render_element = min(7, total_elements)
                     for element_pos in range(render_element):
-                        value += str(field.parts[-1 - element_pos][0]) + (", " if total_elements > 1 else "")
-                value = f'[ {value}{" ..." if total_elements > 1 else ""} ]'
+                        array_elements.append(str(field.parts[-1 - (total_elements - element_pos - 1)][0]))
+
+                value = f'[ {", ".join(array_elements).strip()}{", ..." if total_elements > len(array_elements) else ""} ]'
+
         kv_dump_table.append({"n":n, "pretty_type":pretty_type, "total_elements":total_elements, "field_name":field.name, "value":value})
 
     kv_dump_table_header_map = [
@@ -382,7 +415,7 @@ def dump_markdown_metadata(reader: GGUFReader, args: argparse.Namespace) -> None
             markdown_content += f"- Percentage of total elements: {group_percentage:.2f}%\n"
             markdown_content += "\n\n"
 
-        print(markdown_content)  # noqa: NP100
+    print(markdown_content)  # noqa: NP100
 
 
 def main() -> None:

From 69b9945b44c3057ec17cb556994cd36060455d44 Mon Sep 17 00:00:00 2001
From: Huifeng Ou <79071290+ho2103@users.noreply.github.com>
Date: Sat, 20 Jul 2024 09:09:37 -0400
Subject: [PATCH 084/125] llama.swiftui: fix end of generation bug (#8268)

* fix continuing generating blank lines after getting EOT token or EOS token from LLM

* change variable name to is_done (variable name suggested by ggerganov)

* minor : fix trailing whitespace

* minor : add space

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 examples/llama.swiftui/llama.cpp.swift/LibLlama.swift        | 4 +++-
 examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
index 2a3f9f75890fc7..58c32ca533bb10 100644
--- a/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
+++ b/examples/llama.swiftui/llama.cpp.swift/LibLlama.swift
@@ -26,11 +26,12 @@ actor LlamaContext {
     private var context: OpaquePointer
     private var batch: llama_batch
     private var tokens_list: [llama_token]
+    var is_done: Bool = false
 
     /// This variable is used to store temporarily invalid cchars
     private var temporary_invalid_cchars: [CChar]
 
-    var n_len: Int32 = 64
+    var n_len: Int32 = 1024
     var n_cur: Int32 = 0
 
     var n_decode: Int32 = 0
@@ -160,6 +161,7 @@ actor LlamaContext {
 
         if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
             print("\n")
+            is_done = true
             let new_token_str = String(cString: temporary_invalid_cchars + [0])
             temporary_invalid_cchars.removeAll()
             return new_token_str
diff --git a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
index 2c1e3f61bad206..b8f6a31d582cdc 100644
--- a/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
+++ b/examples/llama.swiftui/llama.swiftui/Models/LlamaState.swift
@@ -132,7 +132,7 @@ class LlamaState: ObservableObject {
         messageLog += "\(text)"
 
         Task.detached {
-            while await llamaContext.n_cur < llamaContext.n_len {
+            while await !llamaContext.is_done {
                 let result = await llamaContext.completion_loop()
                 await MainActor.run {
                     self.messageLog += "\(result)"

From 940362224d20e35f13aa5fd34a0d937ae57bdf7d Mon Sep 17 00:00:00 2001
From: Michael Coppola <m18coppola@gmail.com>
Date: Sat, 20 Jul 2024 09:43:51 -0400
Subject: [PATCH 085/125] llama : add support for Tekken pre-tokenizer (#8579)

* llama : Added support for Tekken pre-tokenizer (#8577)

Removed uneeded `vocab.tokenizer_clean_spaces` assignment

* llama : fix order of pre-tokenizers

* * Tekken pre-tokenizer no longer uses clean_up_tokenization_spaces
* Updated chkhsh for Tekken tokenizer

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 convert_hf_to_gguf.py        |  3 +++
 convert_hf_to_gguf_update.py |  1 +
 include/llama.h              |  1 +
 src/llama.cpp                | 13 +++++++++++++
 4 files changed, 18 insertions(+)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index e3c8aac3d32aae..a0661f120eea71 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -593,6 +593,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901":
             # ref: https://huggingface.co/core42/jais-13b
             res = "jais"
+        if chkhsh == "63b97e4253352e6f357cc59ea5b583e3a680eaeaf2632188c2b952de2588485e":
+            # ref: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407
+            res = "tekken"
 
         if res is None:
             logger.warning("\n")
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index e4165ae2d977c0..29942333704ee8 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -91,6 +91,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "gemma-2",        "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
     {"name": "jais",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
     {"name": "t5",             "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
+    {"name": "tekken",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
 ]
 
 
diff --git a/include/llama.h b/include/llama.h
index b280df3255b6a5..93fd77ca6b03d4 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -92,6 +92,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_CHATGLM4       = 17,
         LLAMA_VOCAB_PRE_TYPE_VIKING         = 18,
         LLAMA_VOCAB_PRE_TYPE_JAIS           = 19,
+        LLAMA_VOCAB_PRE_TYPE_TEKKEN         = 20,
     };
 
     // note: these values should be synchronized with ggml_rope
diff --git a/src/llama.cpp b/src/llama.cpp
index 7d68ed8111873b..80a0dd0f4fb5e5 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -5524,6 +5524,12 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "jais") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS;
+            } else if (
+                tokenizer_pre == "tekken") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_TEKKEN;
+                vocab.tokenizer_clean_spaces = false;
+                vocab.tokenizer_ignore_merges = true;
+                vocab.tokenizer_add_bos = true;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -15585,6 +15591,13 @@ struct llm_tokenizer_bpe {
                     "\\p{N}",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_TEKKEN:
+                    // original regex from tokenizer.json
+                    // "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                regex_exprs = {
+                    "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {

From 07283b1a90e1320aae4762c7e03c879043910252 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 20 Jul 2024 17:15:42 +0300
Subject: [PATCH 086/125] gguf : handle null name during init (#8587)

---
 examples/gguf/gguf.cpp | 5 +++++
 ggml/src/ggml.c        | 2 +-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp
index 57514377107d7c..7498f85efc4f90 100644
--- a/examples/gguf/gguf.cpp
+++ b/examples/gguf/gguf.cpp
@@ -92,6 +92,11 @@ static bool gguf_ex_read_0(const std::string & fname) {
 
     struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
 
+    if (!ctx) {
+        fprintf(stderr, "%s: failed to load '%s'\n", __func__, fname.c_str());
+        return false;
+    }
+
     printf("%s: version:      %d\n", __func__, gguf_get_version(ctx));
     printf("%s: alignment:   %zu\n", __func__, gguf_get_alignment(ctx));
     printf("%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx));
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 7a39c685b6f989..dbb3a3ebe1cca4 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -21015,7 +21015,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
             gguf_tensor_info_sanitize(info);
 
             // make sure there is no duplicated tensor names
-            for (uint64_t j = 0; j < i; ++j) {
+            for (uint64_t j = 0; j < i && ok; ++j) {
                 if (strcmp(info->name.data, ctx->infos[j].name.data) == 0) {
                     fprintf(stderr, "%s: duplicated tensor name %s\n", __func__, info->name.data);
                     ok = false;

From 69c487f4ed57bb4d4514a1b7ff12608d5a8e7ef0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sat, 20 Jul 2024 22:25:26 +0200
Subject: [PATCH 087/125] CUDA: MMQ code deduplication + iquant support (#8495)

* CUDA: MMQ code deduplication + iquant support

* 1 less parallel job for CI build
---
 .github/workflows/build.yml                   |    2 +-
 ggml/src/ggml-cuda/mmq.cu                     |   24 +
 ggml/src/ggml-cuda/mmq.cuh                    | 1377 +++++++++--------
 .../template-instances/generate_cu_files.py   |    3 +-
 .../template-instances/mmq-instance-iq1_s.cu  |    5 +
 .../template-instances/mmq-instance-iq2_s.cu  |    5 +
 .../template-instances/mmq-instance-iq2_xs.cu |    5 +
 .../mmq-instance-iq2_xxs.cu                   |    5 +
 .../template-instances/mmq-instance-iq3_s.cu  |    5 +
 .../mmq-instance-iq3_xxs.cu                   |    5 +
 ggml/src/ggml-cuda/vecdotq.cuh                |   21 +
 11 files changed, 809 insertions(+), 648 deletions(-)
 create mode 100644 ggml/src/ggml-cuda/template-instances/mmq-instance-iq1_s.cu
 create mode 100644 ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_s.cu
 create mode 100644 ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xs.cu
 create mode 100644 ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xxs.cu
 create mode 100644 ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_s.cu
 create mode 100644 ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_xxs.cu

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 1f275603a3742f..a1e183d1182683 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -860,7 +860,7 @@ jobs:
           mkdir build
           cd build
           cmake .. -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=ON
-          cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS}
+          cmake --build . --config Release -j $((${env:NUMBER_OF_PROCESSORS} - 1))
 
       - name: Determine tag name
         id: tag
diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu
index 3af2eec2f41632..84f6387e2491a3 100644
--- a/ggml/src/ggml-cuda/mmq.cu
+++ b/ggml/src/ggml-cuda/mmq.cu
@@ -59,6 +59,24 @@ void ggml_cuda_op_mul_mat_q(
         case GGML_TYPE_Q6_K:
             mul_mat_q_case<GGML_TYPE_Q6_K>(ctx, args, stream);
             break;
+        case GGML_TYPE_IQ2_XXS:
+            mul_mat_q_case<GGML_TYPE_IQ2_XXS>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ2_XS:
+            mul_mat_q_case<GGML_TYPE_IQ2_XS>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ2_S:
+            mul_mat_q_case<GGML_TYPE_IQ2_S>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ3_XXS:
+            mul_mat_q_case<GGML_TYPE_IQ3_XXS>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ3_S:
+            mul_mat_q_case<GGML_TYPE_IQ3_S>(ctx, args, stream);
+            break;
+        case GGML_TYPE_IQ1_S:
+            mul_mat_q_case<GGML_TYPE_IQ1_S>(ctx, args, stream);
+            break;
         case GGML_TYPE_IQ4_XS:
             mul_mat_q_case<GGML_TYPE_IQ4_XS>(ctx, args, stream);
             break;
@@ -93,6 +111,12 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
+        case GGML_TYPE_IQ1_S:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
             mmq_supported = true;
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index 51c44d857a4908..f08a4758d44fd9 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -63,6 +63,14 @@ static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) {
         case GGML_TYPE_Q5_K:
             return MMQ_Q8_1_DS_LAYOUT_DS4;
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ2_XXS:
+        case GGML_TYPE_IQ2_XS:
+        case GGML_TYPE_IQ2_S:
+        case GGML_TYPE_IQ3_XXS:
+        case GGML_TYPE_IQ3_S:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
+        case GGML_TYPE_IQ1_S:
+            return MMQ_Q8_1_DS_LAYOUT_DS4;
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_IQ4_NL:
             return MMQ_Q8_1_DS_LAYOUT_D4;
@@ -131,15 +139,16 @@ static constexpr __device__ int get_mmq_y_device() {
 #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
 }
 
-#define MMQ_DP4A_TXS_Q4_0 tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_0   + mmq_y/QI4_0,     0}
-#define MMQ_DP4A_TXS_Q4_1 tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_1   + mmq_y/QI4_1,     0}
-#define MMQ_DP4A_TXS_Q8_0 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE*2/QI8_0 + mmq_y/(QI8_0/2), 0}
-#define MMQ_DP4A_TXS_Q8_1 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE*2/QI8_1 + mmq_y/(QI8_1/2), 0}
-#define MMQ_DP4A_TXS_Q2_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE         + mmq_y,           0}
-#define MMQ_DP4A_TXS_Q3_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y,                                     mmq_y*WARP_SIZE/8 + mmq_y/8}
-#define MMQ_DP4A_TXS_Q4_K tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_K   + mmq_y/QI4_K,     mmq_y*WARP_SIZE/8 + mmq_y/8}
-#define MMQ_DP4A_TXS_Q5_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_K   + mmq_y/QI5_K,     mmq_y*WARP_SIZE/8 + mmq_y/8}
-#define MMQ_DP4A_TXS_Q6_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI6_K   + mmq_y/QI6_K,     mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q4_0    tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_0   + mmq_y/QI4_0,     0}
+#define MMQ_DP4A_TXS_Q4_1    tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_1   + mmq_y/QI4_1,     0}
+#define MMQ_DP4A_TXS_Q8_0    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE*2/QI8_0 + mmq_y/(QI8_0/2), 0}
+#define MMQ_DP4A_TXS_Q8_0_16 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE*4/QI8_0 + mmq_y/(QI8_0/4), 0}
+#define MMQ_DP4A_TXS_Q8_1    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE*2/QI8_1 + mmq_y/(QI8_1/2), 0}
+#define MMQ_DP4A_TXS_Q2_K    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE         + mmq_y,           0}
+#define MMQ_DP4A_TXS_Q3_K    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y,                                     mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q4_K    tile_x_sizes{mmq_y*WARP_SIZE   + mmq_y, mmq_y*WARP_SIZE/QI4_K,                     mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q5_K    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_K   + mmq_y/QI5_K,     mmq_y*WARP_SIZE/8 + mmq_y/8}
+#define MMQ_DP4A_TXS_Q6_K    tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI6_K   + mmq_y/QI6_K,     mmq_y*WARP_SIZE/8 + mmq_y/8}
 
 static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml_type type, int mmq_y) {
     return type == GGML_TYPE_Q4_0 ? MMQ_DP4A_TXS_Q4_0 :
@@ -152,42 +161,46 @@ static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml
         type == GGML_TYPE_Q4_K    ? MMQ_DP4A_TXS_Q4_K :
         type == GGML_TYPE_Q5_K    ? MMQ_DP4A_TXS_Q5_K :
         type == GGML_TYPE_Q6_K    ? MMQ_DP4A_TXS_Q6_K :
+        type == GGML_TYPE_IQ2_XXS ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_IQ2_XS  ? MMQ_DP4A_TXS_Q8_0_16 :
+        type == GGML_TYPE_IQ2_S   ? MMQ_DP4A_TXS_Q8_0_16 :
+        type == GGML_TYPE_IQ3_XXS ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_IQ3_S   ? MMQ_DP4A_TXS_Q8_0 :
+        type == GGML_TYPE_IQ1_S   ? MMQ_DP4A_TXS_Q8_0 :
         type == GGML_TYPE_IQ4_XS  ? MMQ_DP4A_TXS_Q8_0 :
         type == GGML_TYPE_IQ4_NL  ? MMQ_DP4A_TXS_Q8_0 :
         tile_x_sizes{0, 0, 0};
 }
 
-#define MMQ_MMA_TILE_X_K_Q4_0 (1*WARP_SIZE + WARP_SIZE/QI4_0                   + 4)
-#define MMQ_MMA_TILE_X_K_Q4_1 (1*WARP_SIZE + WARP_SIZE/QI4_1                   + 4)
 #define MMQ_MMA_TILE_X_K_Q8_0 (2*WARP_SIZE + 2*WARP_SIZE/QI8_0                 + 4)
 #define MMQ_MMA_TILE_X_K_Q8_1 (2*WARP_SIZE + 2*WARP_SIZE/QI8_0                 + 4)
 #define MMQ_MMA_TILE_X_K_Q2_K (2*WARP_SIZE + WARP_SIZE                         + 4)
-#define MMQ_MMA_TILE_X_K_Q3_K (2*WARP_SIZE + WARP_SIZE/(2*QI3_K) + WARP_SIZE/8 + 7)
-#define MMQ_MMA_TILE_X_K_Q4_K (1*WARP_SIZE + WARP_SIZE/QI4_K     + WARP_SIZE/8 + 7)
-#define MMQ_MMA_TILE_X_K_Q5_K (2*WARP_SIZE + WARP_SIZE/QI5_K     + WARP_SIZE/8 + 7)
+#define MMQ_MMA_TILE_X_K_Q3_K (2*WARP_SIZE + WARP_SIZE/2                       + 4)
 #define MMQ_MMA_TILE_X_K_Q6_K (2*WARP_SIZE + WARP_SIZE/QI6_K     + WARP_SIZE/8 + 7)
 
-static_assert(MMQ_MMA_TILE_X_K_Q4_0 % 8 == 4, "Wrong padding.");
-static_assert(MMQ_MMA_TILE_X_K_Q4_1 % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q8_0 % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q8_1 % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q2_K % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q3_K % 8 == 4, "Wrong padding.");
-static_assert(MMQ_MMA_TILE_X_K_Q4_K % 8 == 4, "Wrong padding.");
-static_assert(MMQ_MMA_TILE_X_K_Q5_K % 8 == 4, "Wrong padding.");
 static_assert(MMQ_MMA_TILE_X_K_Q6_K % 8 == 4, "Wrong padding.");
 
 static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
-    return type == GGML_TYPE_Q4_0 ? MMQ_MMA_TILE_X_K_Q4_0 :
-        type == GGML_TYPE_Q4_1    ? MMQ_MMA_TILE_X_K_Q4_1 :
+    return type == GGML_TYPE_Q4_0 ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_Q4_1    ? MMQ_MMA_TILE_X_K_Q8_1 :
         type == GGML_TYPE_Q5_0    ? MMQ_MMA_TILE_X_K_Q8_0 :
         type == GGML_TYPE_Q5_1    ? MMQ_MMA_TILE_X_K_Q8_1 :
         type == GGML_TYPE_Q8_0    ? MMQ_MMA_TILE_X_K_Q8_0 :
         type == GGML_TYPE_Q2_K    ? MMQ_MMA_TILE_X_K_Q2_K :
         type == GGML_TYPE_Q3_K    ? MMQ_MMA_TILE_X_K_Q3_K :
-        type == GGML_TYPE_Q4_K    ? MMQ_MMA_TILE_X_K_Q4_K :
-        type == GGML_TYPE_Q5_K    ? MMQ_MMA_TILE_X_K_Q5_K :
+        type == GGML_TYPE_Q4_K    ? MMQ_MMA_TILE_X_K_Q8_1 :
+        type == GGML_TYPE_Q5_K    ? MMQ_MMA_TILE_X_K_Q8_1 :
         type == GGML_TYPE_Q6_K    ? MMQ_MMA_TILE_X_K_Q6_K :
+        type == GGML_TYPE_IQ2_XXS ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_IQ2_XS  ? MMQ_MMA_TILE_X_K_Q3_K :
+        type == GGML_TYPE_IQ2_S   ? MMQ_MMA_TILE_X_K_Q3_K :
+        type == GGML_TYPE_IQ3_XXS ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_IQ3_S   ? MMQ_MMA_TILE_X_K_Q8_0 :
+        type == GGML_TYPE_IQ1_S   ? MMQ_MMA_TILE_X_K_Q8_0 :
         type == GGML_TYPE_IQ4_XS  ? MMQ_MMA_TILE_X_K_Q8_0 :
         type == GGML_TYPE_IQ4_NL  ? MMQ_MMA_TILE_X_K_Q8_0 :
         0;
@@ -216,7 +229,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
 #ifdef INT8_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
-    float * x_df = (float *) (x_qs + WARP_SIZE);
+    float * x_df = (float *) (x_qs + 2*WARP_SIZE);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_0, mmq_y);
     int   * x_qs = (int   *)  x_tile;
@@ -235,11 +248,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         }
 
         const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbx;
+        const int qs0 = get_int_b2(bxi->qs, kqsx);
 
 #ifdef INT8_MMA_AVAILABLE
-        x_qs[i*MMQ_MMA_TILE_X_K_Q4_0 + threadIdx.x] = get_int_b2(bxi->qs, kqsx);
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + 0]     = __vsubss4((qs0 >> 0) & 0x0F0F0F0F, 0x08080808);
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + QI4_0] = __vsubss4((qs0 >> 4) & 0x0F0F0F0F, 0x08080808);
 #else
-        x_qs[i*(WARP_SIZE + 1)       + threadIdx.x] = get_int_b2(bxi->qs, kqsx);
+        x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
 #endif // INT8_MMA_AVAILABLE
     }
 
@@ -257,7 +272,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbxd;
 
 #ifdef INT8_MMA_AVAILABLE
-        x_df[i*MMQ_MMA_TILE_X_K_Q4_0       + kbxd] = bxi->d;
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0       + kbxd] = bxi->d;
 #else
         x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + kbxd] = bxi->d;
 #endif // INT8_MMA_AVAILABLE
@@ -304,95 +319,12 @@ static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a(
     }
 }
 
-template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
-
-    typedef mma_int_A_I16K8 mma_A;
-    typedef mma_int_B_J8K8  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
-
-    constexpr int granularity = mmq_get_granularity_device(mmq_x);
-    constexpr int rows_per_warp = 2 * granularity;
-    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
-
-    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
-
-    const int   * x_qs = (const int   *) x;
-    const float * x_df = (const float *) x_qs + WARP_SIZE;
-    const int   * y_qs = (const int   *) y + 4;
-    const half2 * y_ds = (const half2 *) y;
-
-    mma_A A[ntx][4];
-    float dA[ntx][mma_C::ne/2][4];
-
-    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
-
-#pragma unroll
-    for (int n = 0; n < ntx; ++n) {
-#pragma unroll
-        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_0*QI4_0) {
-            const int k0 = k00 + k01;
-
-#pragma unroll
-            for (int l = 0; l < mma_A::ne; ++l) {
-                const int i     = i0 + n*mma_A::I + mma_A::get_i(l);
-                const int k     = k0/QR4_0        + mma_A::get_k(l) % QI4_0;
-                const int shift =                4*(mma_A::get_k(l) / QI4_0);
-
-                A[n][k01/(QR4_0*QI4_0)].x[l] = __vsubss4((x_qs[i*MMQ_MMA_TILE_X_K_Q4_0 + k] >> shift) & 0x0F0F0F0F, 0x08080808);
-            }
-
-#pragma unroll
-            for (int l = 0; l < mma_C::ne/2; ++l) {
-                const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
-
-                dA[n][l][k01/(QR4_0*QI4_0)] = x_df[i*MMQ_MMA_TILE_X_K_Q4_0 + k0/(QR4_0*QI4_0)];
-            }
-        }
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-#pragma unroll
-        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_0*QI4_0) {
-            mma_B  B;
-            float dB[mma_C::ne/2];
-
-            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
-
-#pragma unroll
-            for (int l = 0; l < mma_C::ne/2; ++l) {
-                const int j = j0 + mma_C::get_j(l);
-
-                dB[l] = __low2float(y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
-            }
-
-#pragma unroll
-            for (int n = 0; n < ntx; ++n) {
-                mma_C C;
-                C.mma_K8(A[n][k01/(QR4_0*QI4_0)], B);
-
-#pragma unroll
-                for (int l = 0; l < mma_C::ne; ++l) {
-                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += dA[n][l/2][k01/(QR4_0*QI4_0)]*dB[l%2]*C.x[l];
-                }
-            }
-        }
-    }
-#else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
-    NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
-}
-
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
 #ifdef INT8_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
-    half2 * x_dm = (half2 *) (x_qs + WARP_SIZE);
+    half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_1, mmq_y);
     int   * x_qs = (int   *)  x_tile;
@@ -411,11 +343,13 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         }
 
         const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbx;
+        const int qs0 = get_int_b4(bxi->qs, kqsx);
 
 #ifdef INT8_MMA_AVAILABLE
-        x_qs[i*MMQ_MMA_TILE_X_K_Q4_1 + threadIdx.x] = get_int_b4(bxi->qs, kqsx);
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + 0]     = (qs0 >> 0) & 0x0F0F0F0F;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + QI4_1] = (qs0 >> 4) & 0x0F0F0F0F;
 #else
-        x_qs[i*(WARP_SIZE + 1)       + threadIdx.x] = get_int_b4(bxi->qs, kqsx);
+        x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
 #endif // INT8_MMA_AVAILABLE
     }
 
@@ -433,7 +367,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbxd;
 
 #ifdef INT8_MMA_AVAILABLE
-        x_dm[i*MMQ_MMA_TILE_X_K_Q4_1       + kbxd] = bxi->dm;
+        x_dm[i*MMQ_MMA_TILE_X_K_Q8_1       + kbxd] = bxi->dm;
 #else
         x_dm[i*(WARP_SIZE/QI4_1) + i/QI4_1 + kbxd] = bxi->dm;
 #endif // INT8_MMA_AVAILABLE
@@ -480,88 +414,6 @@ static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a(
     }
 }
 
-template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
-
-    typedef mma_int_A_I16K8 mma_A;
-    typedef mma_int_A_I16K4 mma_A_K4;
-    typedef mma_int_B_J8K8  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
-
-    constexpr int granularity = mmq_get_granularity_device(mmq_x);
-    constexpr int rows_per_warp = 2 * granularity;
-    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
-
-    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
-
-    const int   * x_qs = (const int   *) x;
-    const half2 * x_dm = (const half2 *) x_qs + WARP_SIZE;
-    const int   * y_qs = (const int   *) y + 4;
-    const half2 * y_ds = (const half2 *) y;
-
-    mma_A A[ntx][4];
-    half2 dmA[ntx][mma_C::ne/2][4];
-
-    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
-
-#pragma unroll
-    for (int n = 0; n < ntx; ++n) {
-#pragma unroll
-        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_1*QI4_1) {
-            const int k0 = k00 + k01;
-
-            A[n][k01/(QR4_1*QI4_1)].load_low(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q4_1 + k0/QR4_1, MMQ_MMA_TILE_X_K_Q4_1);
-            A[n][k01/(QR4_1*QI4_1)].x[2]  = (A[n][k01/(QR4_1*QI4_1)].x[0] >> 4) & 0x0F0F0F0F;
-            A[n][k01/(QR4_1*QI4_1)].x[3]  = (A[n][k01/(QR4_1*QI4_1)].x[1] >> 4) & 0x0F0F0F0F;
-            A[n][k01/(QR4_1*QI4_1)].x[0] &= 0x0F0F0F0F;
-            A[n][k01/(QR4_1*QI4_1)].x[1] &= 0x0F0F0F0F;
-
-#pragma unroll
-            for (int l = 0; l < mma_C::ne/2; ++l) {
-                const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
-
-                dmA[n][l][k01/(QR4_1*QI4_1)] = x_dm[i*MMQ_MMA_TILE_X_K_Q4_1 + k0/(QR4_1*QI4_1)];
-            }
-        }
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-#pragma unroll
-        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR4_1*QI4_1) {
-            mma_B B;
-            half2 dsB[mma_C::ne/2];
-
-            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
-
-#pragma unroll
-            for (int l = 0; l < mma_C::ne/2; ++l) {
-                const int j = j0 + mma_C::get_j(l);
-
-                dsB[l] = y_ds[j*MMQ_TILE_Y_K + k01/QI8_1];
-            }
-
-#pragma unroll
-            for (int n = 0; n < ntx; ++n) {
-                mma_C C;
-                C.mma_K8(A[n][k01/(QR4_1*QI4_1)], B);
-
-#pragma unroll
-                for (int l = 0; l < mma_C::ne; ++l) {
-                    const half2 dmA_dsB = dmA[n][l/2][k01/(QR4_1*QI4_1)]*dsB[l%2];
-                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB);
-                }
-            }
-        }
-    }
-#else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
-    NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
-}
-
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
@@ -789,10 +641,9 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a(
     }
 }
 
-template <int mmq_x, int mmq_y, int nwarps>
+template <int mmq_x, int mmq_y, int nwarps, mmq_q8_1_ds_layout ds_layout>
 static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
     const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K8 mma_A;
     typedef mma_int_B_J8K8  mma_B;
@@ -808,6 +659,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
     const float * x_df = (const float *) x_qs + 2*WARP_SIZE;
     const int   * y_qs = (const int   *) y + 4;
     const float * y_df = (const float *) y;
+    const half2 * y_ds = (const half2 *) y;
 
     mma_A A[ntx][WARP_SIZE/QI8_0];
     float dA[ntx][mma_C::ne/2][WARP_SIZE/QI8_0];
@@ -840,18 +692,20 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
     for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
 #pragma unroll
         for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
-            const int k0 = k00 + k01;
-
-            mma_B B;
+            mma_B  B;
             float dB[mma_C::ne/2];
 
-            B.load(y_qs + j0*MMQ_TILE_Y_K + k0 % WARP_SIZE, MMQ_TILE_Y_K);
+            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
 
 #pragma unroll
             for (int l = 0; l < mma_C::ne/2; ++l) {
                 const int j = j0 + mma_C::get_j(l);
 
-                dB[l] = y_df[j*MMQ_TILE_Y_K + (k0/QI8_1) % (WARP_SIZE/QI8_1)];
+                if (ds_layout == MMQ_Q8_1_DS_LAYOUT_D4) {
+                    dB[l] =             y_df[j*MMQ_TILE_Y_K + k01/QI8_1];
+                } else {
+                    dB[l] = __low2float(y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
+                }
             }
 
 #pragma unroll
@@ -866,10 +720,6 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
             }
         }
     }
-#else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
-    NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
@@ -905,7 +755,6 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_dp4a(
 template <int mmq_x, int mmq_y, int nwarps>
 static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
     const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
 
     typedef mma_int_A_I16K8 mma_A;
     typedef mma_int_B_J8K8  mma_B;
@@ -922,8 +771,8 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
     const int   * y_qs = (const int   *) y + 4;
     const half2 * y_dm = (const half2 *) y;
 
-    mma_A   A[ntx][WARP_SIZE/QI8_1];
-    half2 dmA[ntx][mma_C::ne/2][WARP_SIZE/QI8_1];
+    mma_A    A[ntx][WARP_SIZE/QI8_1];
+    float2 dmA[ntx][mma_C::ne/2][WARP_SIZE/QI8_1];
 
     const int i0 = (threadIdx.y/ntx)*rows_per_warp;
 
@@ -944,7 +793,7 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
             for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
                 const int k0 = k00 + k01;
 
-                dmA[n][l][k01/QI8_1] = x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + k0/QI8_1];
+                dmA[n][l][k01/QI8_1] = __half22float2(x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + k0/QI8_1]);
             }
         }
     }
@@ -953,18 +802,16 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
     for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
 #pragma unroll
         for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
-            const int k0 = k00 + k01;
-
-            mma_B   B;
-            half2 dsB[mma_C::ne/2];
+            mma_B    B;
+            float2 dsB[mma_C::ne/2];
 
-            B.load(y_qs + j0*MMQ_TILE_Y_K + k0 % WARP_SIZE, MMQ_TILE_Y_K);
+            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
 
 #pragma unroll
             for (int l = 0; l < mma_C::ne/2; ++l) {
                 const int j = j0 + mma_C::get_j(l);
 
-                dsB[l] = y_dm[j*MMQ_TILE_Y_K + (k0/QI8_1) % (WARP_SIZE/QI8_1)];
+                dsB[l] = __half22float2(y_dm[j*MMQ_TILE_Y_K + k01/QI8_1]);
             }
 
 #pragma unroll
@@ -974,8 +821,120 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
 
 #pragma unroll
                 for (int l = 0; l < mma_C::ne; ++l) {
-                const half2 dmA_dsB = dmA[n][l/2][k01/QI8_1]*dsB[l%2];
-                sum[(j0/mma_C::J + n)*mma_C::ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB);
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += dmA[n][l/2][k01/QI8_1].x*dsB[l%2].x*C.x[l];
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += dmA[n][l/2][k01/QI8_1].y*dsB[l%2].y;
+                }
+            }
+        }
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_dp4a(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+
+    constexpr tile_x_sizes txs = MMQ_DP4A_TXS_Q8_0_16;
+    const int   * x_qs = (const int   *) x;
+    const float * x_df = (const float *) x_qs + txs.qs;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+// #pragma unroll
+    for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
+        const int k0 = k00 + k01;
+
+#pragma unroll
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+#pragma unroll
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_16_q8_1_impl<QI8_0>(
+                    &x_qs[i*(2*WARP_SIZE + 1) + k0],
+                    &y_qs[j*MMQ_TILE_Y_K + k01],
+                    &x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + k0/(QI8_0/2)],
+                    y_df[j*MMQ_TILE_Y_K + k01/QI8_1]);
+            }
+        }
+    }
+}
+
+template <int mmq_x, int mmq_y, int nwarps>
+static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
+    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
+#ifdef INT8_MMA_AVAILABLE
+
+    typedef mma_int_A_I16K4 mma_A;
+    typedef mma_int_A_I16K8 mma_A_K8;
+    typedef mma_int_B_J8K4  mma_B;
+    typedef mma_int_C_I16J8 mma_C;
+
+    constexpr int granularity = mmq_get_granularity_device(mmq_x);
+    constexpr int rows_per_warp = 2 * granularity;
+    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
+
+    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
+
+    const int   * x_qs = (const int   *) x;
+    const float * x_df = (const float *) x_qs + WARP_SIZE*2;
+    const int   * y_qs = (const int   *) y + 4;
+    const float * y_df = (const float *) y;
+
+    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
+
+    mma_A   A[ntx][8];
+    float  dA[ntx][mma_C::ne/2][8];
+
+#pragma unroll
+    for (int n = 0; n < ntx; ++n) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
+            const int k0 = k00 + k01;
+
+            ((mma_A_K8 *) A[n])[k01/8].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q3_K + k0, MMQ_MMA_TILE_X_K_Q3_K);
+        }
+
+#pragma unroll
+        for (int l = 0; l < mma_C::ne/2; ++l) {
+            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
+
+#pragma unroll
+            for (int k01 = 0; k01 < WARP_SIZE; k01 += 4) {
+                const int k0 = k00 + k01;
+
+                dA[n][l][k01/4] = x_df[i*MMQ_MMA_TILE_X_K_Q3_K + k0/4];
+            }
+        }
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
+#pragma unroll
+        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR3_K*VDR_Q3_K_Q8_1_MMQ) {
+            mma_B B[2];
+            float dB[mma_C::ne/2];
+
+            B[0].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0),        MMQ_TILE_Y_K);
+            B[1].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
+
+#pragma unroll
+            for (int l = 0; l < mma_C::ne/2; ++l) {
+                const int j = j0 + mma_C::get_j(l);
+
+                dB[l] = y_df[j*MMQ_TILE_Y_K + k01/QI8_1];
+            }
+
+#pragma unroll
+            for (int n = 0; n < ntx; ++n) {
+                mma_C C[2];
+                C[0].mma_K4(A[n][k01/4 + 0], B[0]);
+                C[1].mma_K4(A[n][k01/4 + 1], B[1]);
+
+#pragma unroll
+                for (int l = 0; l < mma_C::ne; ++l) {
+                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += dB[l%2]*(C[0].x[l]*dA[n][l/2][k01/4 + 0] + C[1].x[l]*dA[n][l/2][k01/4 + 1]);
                 }
             }
         }
@@ -1222,7 +1181,6 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 #ifdef INT8_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     float * x_df = (float *) (x_qs + WARP_SIZE*2);
-    int   * x_sc = (int   *) (x_df + 1);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q3_K, mmq_y);
     int   * x_qs = (int   *)  x_tile;
@@ -1262,23 +1220,6 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         }
     }
 
-#pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*WARP_SIZE) {
-        int i = (i0 + threadIdx.y*WARP_SIZE + threadIdx.x) % mmq_y;
-
-        if (need_check) {
-            i = min(i, i_max);
-        }
-
-        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride;
-
-#ifdef INT8_MMA_AVAILABLE
-        x_df[i*MMQ_MMA_TILE_X_K_Q3_K] = bxi->d;
-#else
-        x_df[i]                       = bxi->d;
-#endif // INT8_MMA_AVAILABLE
-    }
-
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps*8) {
         int i = i0 + threadIdx.y*8 + threadIdx.x/(WARP_SIZE/8);
@@ -1302,11 +1243,32 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
 
 #ifdef INT8_MMA_AVAILABLE
-        x_sc[i*MMQ_MMA_TILE_X_K_Q3_K + threadIdx.x % (WARP_SIZE/8)] = sc;
+        const int8_t * sc8 = (const int8_t *) &sc;
+        const float d = bxi->d;
+
+#pragma unroll
+        for (int l = 0; l < sizeof(int); ++l) {
+            x_df[i*MMQ_MMA_TILE_X_K_Q3_K + sizeof(int)*(threadIdx.x % (WARP_SIZE/8)) + l] = d*sc8[l];
+        }
 #else
-        x_sc[i*(WARP_SIZE/8) + i/8   + threadIdx.x % (WARP_SIZE/8)] = sc;
+        x_sc[i*(WARP_SIZE/8) + i/8 + threadIdx.x % (WARP_SIZE/8)] = sc;
 #endif // INT8_MMA_AVAILABLE
     }
+
+#ifndef INT8_MMA_AVAILABLE
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*WARP_SIZE) {
+        int i = (i0 + threadIdx.y*WARP_SIZE + threadIdx.x) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride;
+
+        x_df[i] = bxi->d;
+    }
+#endif // INT8_MMA_AVAILABLE
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
@@ -1342,99 +1304,14 @@ static __device__ __forceinline__ void vec_dot_q3_K_q8_1_dp4a(
     }
 }
 
-template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q3_K_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
-
-    typedef mma_int_A_I16K4 mma_A;
-    typedef mma_int_A_I16K8 mma_A_K8;
-    typedef mma_int_B_J8K4  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
-
-    constexpr int granularity = mmq_get_granularity_device(mmq_x);
-    constexpr int rows_per_warp = 2 * granularity;
-    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
-
-    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
-
-    const int   * x_qs = (const int   *) x;
-    const float * x_df = (const float *) x_qs + WARP_SIZE*2;
-    const int   * x_sc = (const int   *) x_df + 1;
-    const int   * y_qs = (const int   *) y + 4;
-    const float * y_df = (const float *) y;
-
-    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
-
-    mma_A   A[ntx][8];
-    int   scA[ntx][mma_C::ne/2][8];
-    float  dA[ntx][mma_C::ne/2];
-
-#pragma unroll
-    for (int n = 0; n < ntx; ++n) {
-#pragma unroll
-        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
-            const int k0 = k00 + k01;
-
-            ((mma_A_K8 *) A[n])[k01/8].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q3_K + k0, MMQ_MMA_TILE_X_K_Q3_K);
-        }
-
-#pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
-
-#pragma unroll
-            for (int k01 = 0; k01 < WARP_SIZE; k01 += 16) {
-                const int k0 = k00 + k01;
-
-                const int sc_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q3_K + k0/16];
-                const int8_t * sc = (const int8_t *) &sc_packed;
-
-#pragma unroll
-                for (int ksc = 0; ksc < sizeof(int); ++ksc) {
-                    scA[n][l][k01/4 + ksc] = sc[ksc];
-                }
-            }
-
-            dA[n][l] = x_df[i*MMQ_MMA_TILE_X_K_Q3_K];
-        }
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-#pragma unroll
-        for (int k01 = 0; k01 < WARP_SIZE; k01 += QR3_K*VDR_Q3_K_Q8_1_MMQ) {
-            mma_B B[2];
-            float dB[mma_C::ne/2];
-
-            B[0].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0),        MMQ_TILE_Y_K);
-            B[1].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
-
-#pragma unroll
-            for (int l = 0; l < mma_C::ne/2; ++l) {
-                const int j = j0 + mma_C::get_j(l);
-
-                dB[l] = y_df[j*MMQ_TILE_Y_K + k01/QI8_1];
-            }
-
-#pragma unroll
-            for (int n = 0; n < ntx; ++n) {
-                mma_C C[2];
-                C[0].mma_K4(A[n][k01/4 + 0], B[0]);
-                C[1].mma_K4(A[n][k01/4 + 1], B[1]);
-
-#pragma unroll
-                for (int l = 0; l < mma_C::ne; ++l) {
-                    sum[(j0/mma_C::J + n)*mma_C::ne + l] += dA[n][l/2]*dB[l%2]*
-                        (C[0].x[l]*scA[n][l/2][k01/4 + 0] + C[1].x[l]*scA[n][l/2][k01/4 + 1]);
-                }
-            }
-        }
-    }
-#else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
-    NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+static __device__ __forceinline__ int unpack_scales_q45_K(const int * scales, const int ksc) {
+    // scale arrangement after the following two lines:
+    //   - ksc == 0: sc0, sc1, sc2, sc3
+    //   - ksc == 1: sc4, sc5, sc6, sc7
+    //   - ksc == 2:  m0,  m1,  m2,  m3
+    //   - ksc == 3:  m4,  m5,  m6,  m7
+    return ((scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F) | // lower 4 bits
+           ((scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030);  // upper 2 bits
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
@@ -1442,8 +1319,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
 #ifdef INT8_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
-    half2 * x_dm = (half2 *) (x_qs + WARP_SIZE);
-    int   * x_sc = (int   *) (x_dm + WARP_SIZE/QI4_K);
+    half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_K, mmq_y);
     int   * x_qs = (int   *)  x_tile;
@@ -1451,9 +1327,6 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
     int   * x_sc = (int   *) (x_dm + txs.dm);
 #endif // INT8_MMA_AVAILABLE
 
-    const int kbx  = 0;           // threadIdx.x / QI4_K
-    const int kqsx = threadIdx.x; // threadIdx.x % QI4_K
-
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
         int i = i0 + threadIdx.y;
@@ -1462,33 +1335,59 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
             i = min(i, i_max);
         }
 
-        const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride + kbx;
+        const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride;
+        const int qs0 = get_int_b4(bxi->qs, threadIdx.x);
 
 #ifdef INT8_MMA_AVAILABLE
-        x_qs[i*MMQ_MMA_TILE_X_K_Q4_K + threadIdx.x] = get_int_b4(bxi->qs, kqsx);
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(threadIdx.x/8) + threadIdx.x % 8 + 0] = (qs0 >> 0) & 0x0F0F0F0F;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(threadIdx.x/8) + threadIdx.x % 8 + 8] = (qs0 >> 4) & 0x0F0F0F0F;
 #else
-        x_qs[i*(WARP_SIZE + 1)       + threadIdx.x] = get_int_b4(bxi->qs, kqsx);
+        x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
 #endif // INT8_MMA_AVAILABLE
     }
 
-    const int blocks_per_tile_x_row = WARP_SIZE / QI4_K;  // == 1 if QK_K == 256
-    const int kbxd = threadIdx.x % blocks_per_tile_x_row; // == 0 if QK_K == 256
+#ifdef INT8_MMA_AVAILABLE
 
 #pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
-        int i = (i0 + threadIdx.y * QI4_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y;
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*16) {
+        int i = (i0 + threadIdx.y*16 + threadIdx.x/(WARP_SIZE/16)) % mmq_y;
 
         if (need_check) {
             i = min(i, i_max);
         }
 
-        const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride + kbxd;
+        const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride;
+
+        const int * scales = (const int *) bxi->scales;
+        const int ksc = threadIdx.x % (WARP_SIZE/16);
+
+        const int sc32 = unpack_scales_q45_K(scales, ksc + 0);
+        const int  m32 = unpack_scales_q45_K(scales, ksc + 2);
+
+        const uint8_t * sc8 = (const uint8_t *) &sc32;
+        const uint8_t *  m8 = (const uint8_t *)  &m32;
+
+        const half2 dm = bxi->dm * make_half2(1.0f, -1.0f);
+
+#pragma unroll
+        for (int l = 0; l < sizeof(int); ++l) {
+            x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + sizeof(int)*ksc + l] = dm*make_half2(sc8[l], m8[l]);
+        }
+    }
 
-#ifdef INT8_MMA_AVAILABLE
-        x_dm[i*MMQ_MMA_TILE_X_K_Q4_K       + kbxd] = bxi->dm;
 #else
-        x_dm[i*(WARP_SIZE/QI4_K) + i/QI4_K + kbxd] = bxi->dm;
-#endif // INT8_MMA_AVAILABLE
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*QI4_K) {
+        int i = (i0 + threadIdx.y*QI4_K + threadIdx.x) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride;
+
+        x_dm[i] = bxi->dm;
     }
 
 #pragma unroll
@@ -1504,17 +1403,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const int * scales = (const int *) bxi->scales;
 
         const int ksc = threadIdx.x % (WARP_SIZE/8);
+        const int scales8 = unpack_scales_q45_K(scales, ksc);
 
-        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
-        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
-        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
-
-#ifdef INT8_MMA_AVAILABLE
-        x_sc[i*MMQ_MMA_TILE_X_K_Q4_K + ksc] = scales8;
-#else
-        x_sc[i*(WARP_SIZE/8) + i/8   + ksc] = scales8;
-#endif // INT8_MMA_AVAILABLE
+        x_sc[i*(WARP_SIZE/8) + i/8 + ksc] = scales8;
     }
+#endif // INT8_MMA_AVAILABLE
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
@@ -1544,124 +1437,10 @@ static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a(
 
                 sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_K_q8_1_impl_mmq(
                     &x_qs[i*(WARP_SIZE + 1) + k0/2], &y_qs[j*MMQ_TILE_Y_K + k01], sc, sc+8,
-                    x_dm[i*(WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
-            }
-        }
-    }
-}
-
-template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
-
-    typedef mma_int_A_I16K8 mma_A;
-    typedef mma_int_B_J8K8  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
-
-    constexpr int granularity = mmq_get_granularity_device(mmq_x);
-    constexpr int rows_per_warp = 2 * granularity;
-    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
-
-    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
-
-    const int   * x_qs = (const int   *) x;
-    const half2 * x_dm = (const half2 *) x_qs + WARP_SIZE;
-    const int   * x_sc = (const int   *) x_dm + WARP_SIZE/QI4_K;
-    const int   * y_qs = (const int   *) y + 4;
-    const half2 * y_ds = (const half2 *) y;
-
-    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
-
-    mma_A   A[ntx][4];
-    int   scA[ntx][mma_C::ne/2][4];
-    int    mA[ntx][mma_C::ne/2][4];
-    half2 dmA[ntx][mma_C::ne/2];
-
-#pragma unroll
-    for (int n = 0; n < ntx; ++n) {
-#pragma unroll
-        for (int k01 = 0; k01 < WARP_SIZE; k01 += 16) {
-            const int k0 = k00 + k01;
-
-            A[n][k01/8 + 0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q4_K + k0/QR4_K, MMQ_MMA_TILE_X_K_Q4_K);
-
-#pragma unroll
-            for (int l = 0; l < mma_A::ne; ++l) {
-                A[n][k01/8 + 1].x[l]  = (A[n][k01/8 + 0].x[l] >> 4) & 0x0F0F0F0F;
-                A[n][k01/8 + 0].x[l] &= 0x0F0F0F0F;
-            }
-        }
-
-#pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
-
-            const int sc_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q4_K + (k00/32 + 0)];
-            const int  m_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q4_K + (k00/32 + 2)];
-
-            const uint8_t * sc = (const uint8_t *) &sc_packed;
-            const uint8_t *  m = (const uint8_t *)  &m_packed;
-
-#pragma unroll
-            for (int ksc = 0; ksc < sizeof(int); ++ksc) {
-                scA[n][l][ksc] = sc[ksc];
-                mA[n][l][ksc]  =  m[ksc];
+                    x_dm[i], &y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
             }
         }
-
-#pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int i = i0 + n*mma_A::I + mma_C::get_i(2*l);
-
-            dmA[n][l] = x_dm[i*MMQ_MMA_TILE_X_K_Q4_K];
-        }
     }
-
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-        float tmpd[ntx][mma_C::ne] = {{0.0f}};
-        float tmpm[ntx][mma_C::ne] = {{0.0f}};
-
-#pragma unroll
-        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
-            mma_B   B;
-            half2 dsB[mma_C::ne/2];
-
-            B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
-
-#pragma unroll
-            for (int l = 0; l < mma_C::ne/2; ++l) {
-                const int j = j0 + mma_C::get_j(l);
-
-                dsB[l] = y_ds[j*MMQ_TILE_Y_K + k01/QI8_1];
-            }
-
-#pragma unroll
-            for (int n = 0; n < ntx; ++n) {
-                mma_C C;
-                C.mma_K8(A[n][k01/8], B);
-
-#pragma unroll
-                for (int l = 0; l < mma_C::ne; ++l) {
-                    tmpd[n][l] += (C.x[l]*scA[n][l/2][k01/8]) *  __low2float(dsB[l%2]);
-                    tmpm[n][l] += mA[n][l/2][k01/8]           * __high2float(dsB[l%2]);
-                }
-            }
-        }
-
-#pragma unroll
-        for (int n = 0; n < ntx; ++n) {
-#pragma unroll
-            for (int l = 0; l < mma_C::ne; ++l) {
-                sum[(j0/mma_C::J + n)*mma_C::ne + l] += __low2float(dmA[n][l/2])*tmpd[n][l] - __high2float(dmA[n][l/2])*tmpm[n][l];
-            }
-        }
-    }
-#else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
-    NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
@@ -1670,7 +1449,6 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 #ifdef INT8_MMA_AVAILABLE
     int   * x_qs = (int   *)  x_tile;
     half2 * x_dm = (half2 *) (x_qs + WARP_SIZE*2);
-    int   * x_sc = (int   *) (x_dm + WARP_SIZE/QI5_K);
 #else
     constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_K, mmq_y);
     int   * x_qs = (int   *)  x_tile;
@@ -1678,9 +1456,6 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
     int   * x_sc = (int   *) (x_dm + txs.dm);
 #endif // INT8_MMA_AVAILABLE
 
-    const int kbx  = 0;           // threadIdx.x / QI5_K
-    const int kqsx = threadIdx.x; // threadIdx.x % QI5_K
-
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
         int i = i0 + threadIdx.y;
@@ -1689,73 +1464,91 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
             i = min(i, i_max);
         }
 
-        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride + kbx;
-        const int ky = QR5_K*kqsx;
+        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride;
+        const int ky = QR5_K*threadIdx.x;
 
-        const int ql = get_int_b4(bxi->qs, kqsx);
+        const int ql = get_int_b4(bxi->qs, threadIdx.x);
         const int ql0 = (ql >> 0) & 0x0F0F0F0F;
         const int ql1 = (ql >> 4) & 0x0F0F0F0F;
 
-        const int qh = get_int_b4(bxi->qh, kqsx % (QI5_K/4));
-        const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
-        const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
+        const int qh = get_int_b4(bxi->qh, threadIdx.x % (QI5_K/4));
+        const int qh0 = ((qh >> (2 * (threadIdx.x / (QI5_K/4)) + 0)) << 4) & 0x10101010;
+        const int qh1 = ((qh >> (2 * (threadIdx.x / (QI5_K/4)) + 1)) << 4) & 0x10101010;
 
         const int kq0 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + 0;
-        const int kq1 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + (QI5_K/4);
+        const int kq1 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + QI5_K/4;
 
 #ifdef INT8_MMA_AVAILABLE
-        x_qs[i*MMQ_MMA_TILE_X_K_Q5_K + kq0] = ql0 | qh0;
-        x_qs[i*MMQ_MMA_TILE_X_K_Q5_K + kq1] = ql1 | qh1;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq0] = ql0 | qh0;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq1] = ql1 | qh1;
 #else
         x_qs[i*(2*WARP_SIZE + 1)     + kq0] = ql0 | qh0;
         x_qs[i*(2*WARP_SIZE + 1)     + kq1] = ql1 | qh1;
 #endif // INT8_MMA_AVAILABLE
     }
 
-    const int blocks_per_tile_x_row = WARP_SIZE / QI5_K;  // == 1 if QK_K == 256
-    const int kbxd = threadIdx.x % blocks_per_tile_x_row; // == 0 if QK_K == 256
+#ifdef INT8_MMA_AVAILABLE
 
 #pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
-        int i = (i0 + threadIdx.y * QI5_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y;
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*16) {
+        int i = (i0 + threadIdx.y*16 + threadIdx.x/(WARP_SIZE/16)) % mmq_y;
 
         if (need_check) {
             i = min(i, i_max);
         }
 
-        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride + kbxd;
+        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride;
+
+        const int * scales = (const int *) bxi->scales;
+        const int ksc = threadIdx.x % (WARP_SIZE/16);
+
+        const int sc32 = unpack_scales_q45_K(scales, ksc + 0);
+        const int  m32 = unpack_scales_q45_K(scales, ksc + 2);
+
+        const uint8_t * sc8 = (const uint8_t *) &sc32;
+        const uint8_t *  m8 = (const uint8_t *)  &m32;
+
+        const half2 dm = bxi->dm * make_half2(1.0f, -1.0f);
+
+#pragma unroll
+        for (int l = 0; l < sizeof(int); ++l) {
+            x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + sizeof(int)*ksc + l] = dm*make_half2(sc8[l], m8[l]);
+        }
+    }
 
-#ifdef INT8_MMA_AVAILABLE
-        x_dm[i*MMQ_MMA_TILE_X_K_Q5_K       + kbxd] = bxi->dm;
 #else
-        x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K + kbxd] = bxi->dm;
-#endif // INT8_MMA_AVAILABLE
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*QI5_K) {
+        int i = (i0 + threadIdx.y*QI5_K + threadIdx.x) % mmq_y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride;
+
+        x_dm[i] = bxi->dm;
     }
 
 #pragma unroll
-    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
-        int i = (i0 + threadIdx.y * 8 + threadIdx.x / (WARP_SIZE/8)) % mmq_y;
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps*8) {
+        int i = (i0 + threadIdx.y*8 + threadIdx.x/(WARP_SIZE/8)) % mmq_y;
 
         if (need_check) {
             i = min(i, i_max);
         }
 
-        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / (QI5_K/8);
+        const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride;
 
         const int * scales = (const int *) bxi->scales;
 
         const int ksc = threadIdx.x % (WARP_SIZE/8);
+        const int scales8 = unpack_scales_q45_K(scales, ksc);
 
-        // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
-        int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
-        scales8    |= (scales[ksc/2]              >> (2 * (ksc % 2)))       & 0x30303030; // upper 2 bits
-
-#ifdef INT8_MMA_AVAILABLE
-        x_sc[i*MMQ_MMA_TILE_X_K_Q5_K + ksc] = scales8;
-#else
-        x_sc[i*(WARP_SIZE/8) + i/8   + ksc] = scales8;
-#endif // INT8_MMA_AVAILABLE
+        x_sc[i*(WARP_SIZE/8) + i/8 + ksc] = scales8;
     }
+#endif // INT8_MMA_AVAILABLE
 }
 
 template <int mmq_x, int mmq_y, int nwarps>
@@ -1771,134 +1564,24 @@ static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a(
 
 // #pragma unroll
     for (int k01 = 0; k01 < WARP_SIZE; k01 += QR5_K*VDR_Q5_K_Q8_1_MMQ) {
-        const int k0 = k00 + k01;
-
-#pragma unroll
-        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
-            const int j = j0 + threadIdx.y;
-
-#pragma unroll
-            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
-                const int i = i0 + threadIdx.x;
-
-                const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k00/32]) + 2*(k01/16);
-
-                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q5_K_q8_1_impl_mmq(
-                    &x_qs[i*(QR5_K*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01], sc, sc+8,
-                    x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
-            }
-        }
-    }
-}
-
-template <int mmq_x, int mmq_y, int nwarps>
-static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mma(
-    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
-
-    typedef mma_int_A_I16K8 mma_A;
-    typedef mma_int_B_J8K8  mma_B;
-    typedef mma_int_C_I16J8 mma_C;
-
-    constexpr int granularity = mmq_get_granularity_device(mmq_x);
-    constexpr int rows_per_warp = 2 * granularity;
-    constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
-
-    y += (threadIdx.y % ntx) * (mma_B::J*MMQ_TILE_Y_K);
-
-    const int   * x_qs = (const int   *) x;
-    const half2 * x_dm = (const half2 *) x_qs + WARP_SIZE*2;
-    const int   * x_sc = (const int   *) x_dm + WARP_SIZE/QI5_K;
-    const int   * y_qs = (const int   *) y + 4;
-    const half2 * y_ds = (const half2 *) y;
-
-    const int i0 = (threadIdx.y / ntx) * (ntx*mma_A::I);
-
-    mma_A   A[ntx][4];
-    int   scA[ntx][mma_C::ne/2][4];
-    int    mA[ntx][mma_C::ne/2][4];
-    half2 dmA[ntx][mma_C::ne/2];
-
-#pragma unroll
-    for (int n = 0; n < ntx; ++n) {
-#pragma unroll
-        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
-            const int k0 = k00 + k01;
-
-            A[n][k01/8].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q5_K + k0, MMQ_MMA_TILE_X_K_Q5_K);
-        }
-
-#pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
-
-            const int sc_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q5_K + (k00/32 + 0)];
-            const int  m_packed = x_sc[i*MMQ_MMA_TILE_X_K_Q5_K + (k00/32 + 2)];
-
-            const uint8_t * sc = (const uint8_t *) &sc_packed;
-            const uint8_t *  m = (const uint8_t *)  &m_packed;
-
-#pragma unroll
-            for (int ksc = 0; ksc < sizeof(int); ++ksc) {
-                scA[n][l][ksc] = sc[ksc];
-                mA[n][l][ksc]  =  m[ksc];
-            }
-        }
-
-    #pragma unroll
-        for (int l = 0; l < mma_C::ne/2; ++l) {
-            const int i = i0 + n*mma_C::I + mma_C::get_i(2*l);
-
-            dmA[n][l] = x_dm[i*MMQ_MMA_TILE_X_K_Q5_K];
-        }
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
-        float tmpd[ntx][mma_C::ne] = {{0.0f}};
-        float tmpm[ntx][mma_C::ne] = {{0.0f}};
-
-#pragma unroll
-        for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
-            const int k0 = k00 + k01;
-
-            mma_B   B;
-            half2 dsB[mma_C::ne/2];
-
-            B.load(y_qs + j0*MMQ_TILE_Y_K + k0 % WARP_SIZE, MMQ_TILE_Y_K);
-
-#pragma unroll
-            for (int l = 0; l < mma_C::ne/2; ++l) {
-                const int j = j0 + mma_C::get_j(l);
-
-                dsB[l] = y_ds[j*MMQ_TILE_Y_K + (k0/QI8_1) % (WARP_SIZE/QI8_1)];
-            }
+        const int k0 = k00 + k01;
 
 #pragma unroll
-        for (int n = 0; n < ntx; ++n) {
-                mma_C C;
-                C.mma_K8(A[n][k01/8], B);
+        for (int j0 = 0; j0 < mmq_x; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
 
 #pragma unroll
-                for (int l = 0; l < mma_C::ne; ++l) {
-                    tmpd[n][l] += (C.x[l]*scA[n][l/2][k01/8]) *  __low2float(dsB[l%2]);
-                    tmpm[n][l] += mA[n][l/2][k01/8]           * __high2float(dsB[l%2]);
-                }
-            }
-        }
+            for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
 
-#pragma unroll
-        for (int n = 0; n < ntx; ++n) {
-#pragma unroll
-            for (int l = 0; l < mma_C::ne; ++l) {
-                sum[(j0/mma_C::J + n)*mma_C::ne + l] += __low2float(dmA[n][l/2])*tmpd[n][l] - __high2float(dmA[n][l/2])*tmpm[n][l];
+                const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k00/32]) + 2*(k01/16);
+
+                sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q5_K_q8_1_impl_mmq(
+                    &x_qs[i*(QR5_K*WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k01], sc, sc+8,
+                    x_dm[i], &y_ds[j*MMQ_TILE_Y_K + k01/QI8_1]);
             }
         }
     }
-#else
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
-    NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
 }
 
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
@@ -1915,9 +1598,6 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
     int   * x_sc = (int   *) (x_df + txs.dm);
 #endif // INT8_MMA_AVAILABLE
 
-    const int kbx  = 0;           // threadIdx.x / QI6_K
-    const int kqsx = threadIdx.x; // threadIdx.x % QI6_K
-
 #pragma unroll
     for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
         int i = i0 + threadIdx.y;
@@ -1926,19 +1606,18 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
             i = min(i, i_max);
         }
 
-        const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + kbx;
-        const int ky = QR6_K*kqsx;
+        const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride;
 
-        const int ql = get_int_b2(bxi->ql, kqsx);
+        const int ql = get_int_b2(bxi->ql, threadIdx.x);
         const int ql0 = (ql >> 0) & 0x0F0F0F0F;
         const int ql1 = (ql >> 4) & 0x0F0F0F0F;
 
-        const int qh = get_int_b2(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
-        const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
-        const int qh1 =  (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4))))       & 0x30303030;
+        const int qh = get_int_b2(bxi->qh, (QI6_K/4) * (threadIdx.x / (QI6_K/2)) + threadIdx.x % (QI6_K/4));
+        const int qh0 = ((qh >> ((threadIdx.x & 0x08) >> 2)) << 4) & 0x30303030;
+        const int qh1 =  (qh >> ((threadIdx.x & 0x08) >> 2))       & 0x30303030;
 
-        const int kq0 = ky - ky % QI6_K + threadIdx.x % (QI6_K/2) + 0;
-        const int kq1 = ky - ky % QI6_K + threadIdx.x % (QI6_K/2) + (QI6_K/2);
+        const int kq0 = 2*threadIdx.x - threadIdx.x % (QI6_K/2) + 0;
+        const int kq1 = 2*threadIdx.x - threadIdx.x % (QI6_K/2) + QI6_K/2;
 
 #ifdef INT8_MMA_AVAILABLE
         x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
@@ -2187,6 +1866,358 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
     }
 }
 
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_xxs(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_XXS, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % (QI2_XXS/2);
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/(QI2_XXS/2)) {
+        int i = i0 + threadIdx.y*(2*WARP_SIZE/QI2_XXS) + threadIdx.x/(QI2_XXS/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq2_xxs * bxi = (const block_iq2_xxs *) x + kbx0 + i*stride;
+
+        const int q2 = get_int_b2(bxi->qs, 2*kqsx+0);
+        const uint8_t * aux8 = (const uint8_t *) &q2;
+        const uint32_t aux32 = get_int_b2(bxi->qs, 2*kqsx+1);
+
+#pragma unroll
+        for (int l = 0; l < QR2_XXS; ++l) {
+            const int * grid_pos = (const int *) (iq2xxs_grid + aux8[l]);
+            const int signs_packed = ksigns_iq2xs[(aux32 >> (7*l)) & 0x7F];
+
+            const int signs0 = __vcmpne4(((signs_packed & 0x03) << 7) | ((signs_packed & 0x0C) << 21), 0x00000000);
+            const int grid0 = __vsub4(grid_pos[0] ^ signs0, signs0);
+
+            const int signs1 = __vcmpne4(((signs_packed & 0x30) << 3) | ((signs_packed & 0xC0) << 17), 0x00000000);
+            const int grid1 = __vsub4(grid_pos[1] ^ signs1, signs1);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid0;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid1;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid0;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid1;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int ls = aux32 >> 28;
+        const float d = bxi->d;
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = (ls*d + d/2)/4;
+#else
+        x_df[i*(WARP_SIZE/4) + i/4   + kqsx] = (ls*d + d/2)/4;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_xs(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = MMQ_DP4A_TXS_Q8_0_16;
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % (QI2_XS/2);
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/(QI2_XS/2)) {
+        int i = i0 + threadIdx.y*(2*WARP_SIZE/QI2_XS) + threadIdx.x/(QI2_XS/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq2_xs * bxi = (const block_iq2_xs *) x + kbx0 + i*stride;
+
+        const int2 q2_packed = make_int2(get_int_b2(bxi->qs, 2*kqsx+0), get_int_b2(bxi->qs, 2*kqsx+1));
+        const uint16_t * q2 = (const uint16_t *) &q2_packed;
+
+    #pragma unroll
+        for (int l = 0; l < QR2_XS; ++l) {
+            const uint32_t * grid_pos = (const uint32_t *)(iq2xs_grid + (q2[l] & 0x000001FF));
+            const uint32_t * signs    = (const uint32_t *)(ksigns64   + (q2[l] >> 9));
+
+            const int grid_l = __vsub4(grid_pos[0] ^ signs[0], signs[0]);
+            const int grid_h = __vsub4(grid_pos[1] ^ signs[1], signs[1]);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid_h;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int ls = bxi->scales[kqsx];
+        const float d = bxi->d;
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
+        x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
+#else
+        x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
+        x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_s(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_S, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % (QI2_S/2);
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/(QI2_S/2)) {
+        int i = i0 + threadIdx.y*(2*WARP_SIZE/QI2_S) + threadIdx.x/(QI2_S/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq2_s * bxi = (const block_iq2_s *) x + kbx0 + i*stride;
+
+        const int       qs_packed = get_int_b2(bxi->qs, kqsx);
+        const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+        const int qh = bxi->qh[kqsx];
+
+        const int       signs_packed_32 = get_int_b2(bxi->qs, QK_K/32 + kqsx);
+        const uint8_t * signs_packed_8  = (const uint8_t *) &signs_packed_32;
+
+#pragma unroll
+        for (int l = 0; l < QR2_S; ++l) {
+            const int * grid_pos = (const int *)(iq2s_grid + (qs[l] | ((qh << (8-2*l)) & 0x300)));
+
+            const int signs0 = __vcmpne4(((signs_packed_8[l] & 0x03) << 7) | ((signs_packed_8[l] & 0x0C) << 21), 0x00000000);
+            const int signs1 = __vcmpne4(((signs_packed_8[l] & 0x30) << 3) | ((signs_packed_8[l] & 0xC0) << 17), 0x00000000);
+
+            const int grid_l = __vsub4(grid_pos[0] ^ signs0, signs0);
+            const int grid_h = __vsub4(grid_pos[1] ^ signs1, signs1);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid_h;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int ls = bxi->scales[kqsx];
+        const float d = bxi->d;
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
+        x_df[i*MMQ_MMA_TILE_X_K_Q3_K               + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
+#else
+        x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls &  0x0F)*d + d/2)/4;
+        x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >>    4)*d + d/2)/4;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq3_xxs(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_XXS, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % (QI3_XXS/2);
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/(QI3_XXS/2)) {
+        int i = i0 + threadIdx.y*(2*WARP_SIZE/QI3_XXS) + threadIdx.x/(QI3_XXS/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq3_xxs * bxi = (const block_iq3_xxs *) x + kbx0 + i*stride;
+
+        const int2 q3_packed = make_int2(get_int_b2(bxi->qs, 2*kqsx+0), get_int_b2(bxi->qs, 2*kqsx+1));
+        const uint8_t * q3 = (const uint8_t *) &q3_packed;
+        const uint32_t aux32 = get_int_b2(bxi->qs, QK_K/16 + kqsx);
+
+#pragma unroll
+        for (int l = 0; l < QR3_XXS; ++l) {
+            const int2 grid_pos = make_int2(iq3xxs_grid[q3[2*l+0]], iq3xxs_grid[q3[2*l+1]]);
+
+            const int * signs = (const int *)(ksigns64 + ((aux32 >> (7*l)) & 0x7F));
+
+            const int grid_l = __vsub4(grid_pos.x ^ signs[0], signs[0]);
+            const int grid_h = __vsub4(grid_pos.y ^ signs[1], signs[1]);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid_h;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 0)] = grid_l;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l + 1)] = grid_h;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int ls = aux32 >> 28;
+        const float d = bxi->d;
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = (ls*d + d/2)/2;
+#else
+        x_df[i*(WARP_SIZE/4) + i/4   + kqsx] = (ls*d + d/2)/2;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq3_s(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % (QI3_S/2);
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/(QI3_S/2)) {
+        int i = i0 + threadIdx.y*(2*WARP_SIZE/QI3_S) + threadIdx.x/(QI3_S/2);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq3_s * bxi = (const block_iq3_s *) x + kbx0 + i*stride;
+
+        const int2      qs_packed = make_int2(get_int_b2(bxi->qs, 2*kqsx+0), get_int_b2(bxi->qs, 2*kqsx+1));
+        const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+        const int qh = bxi->qh[kqsx];
+
+        const int       signs_packed_32 = get_int_b2(bxi->signs, kqsx);
+        const uint8_t * signs_packed_8  = (const uint8_t *) &signs_packed_32;
+
+#pragma unroll
+        for (int l = 0; l < QR3_S; ++l) {
+            const int2 grid_pos = make_int2(
+                iq3s_grid[qs[2*l+0] | ((qh << (8 - 2*l)) & 0x100)],
+                iq3s_grid[qs[2*l+1] | ((qh << (7 - 2*l)) & 0x100)]);
+
+            const int signs0 = __vcmpne4(((signs_packed_8[l] & 0x03) << 7) | ((signs_packed_8[l] & 0x0C) << 21), 0x00000000);
+            const int signs1 = __vcmpne4(((signs_packed_8[l] & 0x30) << 3) | ((signs_packed_8[l] & 0xC0) << 17), 0x00000000);
+
+            const int grid_l = __vsub4(grid_pos.x ^ signs0, signs0);
+            const int grid_h = __vsub4(grid_pos.y ^ signs1, signs1);
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+0)] = grid_l;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+1)] = grid_h;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+0)] = grid_l;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+1)] = grid_h;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const int ls = 1 + 2*((bxi->scales[kqsx/2] >> (((2*kqsx) << 1) & 0x04)) & 0x0F);
+        const float d = bxi->d;
+#ifdef INT8_MMA_AVAILABLE
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = ls*d;
+#else
+        x_df[i*(WARP_SIZE/4) + i/4   + kqsx] = ls*d;
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
+template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq1_s(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
+
+#ifdef INT8_MMA_AVAILABLE
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_ds = (half2 *) (x_qs + WARP_SIZE*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    half2 * x_ds = (half2 *) (x_qs + txs.qs);
+#endif // INT8_MMA_AVAILABLE
+
+    const int kqsx = threadIdx.x % QI1_S;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * WARP_SIZE/QI1_S) {
+        int i = i0 + threadIdx.y*(WARP_SIZE/QI1_S) + threadIdx.x/QI1_S;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_iq1_s * bxi = (const block_iq1_s *) x + kbx0 + i*stride;
+
+        const int       qs_packed = get_int_b2(bxi->qs, kqsx);
+        const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+        const int qh = bxi->qh[kqsx];
+
+    #pragma unroll
+        for (int l = 0; l < QR1_S/2; ++l) {
+            const int grid = iq1s_grid_gpu[qs[l] | (((qh >> (3*l)) & 0x07) << 8)];
+
+            const int grid0 = (grid >> 0) & 0x0F0F0F0F;
+            const int grid1 = (grid >> 4) & 0x0F0F0F0F;
+
+#ifdef INT8_MMA_AVAILABLE
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+0)] = grid0;
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+1)] = grid1;
+#else
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+0)] = grid0;
+            x_qs[i*(2*WARP_SIZE + 1)     + 8*kqsx + (2*l+1)] = grid1;
+#endif // INT8_MMA_AVAILABLE
+        }
+
+        const float  d1q   = __half2float(bxi->d) * (((qh >> 11) & 0x0E) + 1);
+        const float  delta = -1.0f + IQ1S_DELTA - (qh & 0x8000) * (2.0f*IQ1S_DELTA/0x8000);
+
+#ifdef INT8_MMA_AVAILABLE
+        x_ds[i*MMQ_MMA_TILE_X_K_Q8_1 + kqsx] = make_half2(d1q, d1q*delta);
+#else
+        x_ds[i*(WARP_SIZE/4) + i/4   + kqsx] = make_half2(d1q, d1q*delta);
+#endif // INT8_MMA_AVAILABLE
+    }
+}
+
 template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq4_xs(
     const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
 
@@ -2320,7 +2351,7 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_0> {
     static constexpr int              vdr          = VDR_Q4_0_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q4_0<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q4_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_DS4>;
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q4_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
@@ -2328,7 +2359,7 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_1> {
     static constexpr int              vdr          = VDR_Q4_1_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q4_1<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q4_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q4_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
@@ -2336,7 +2367,7 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_0> {
     static constexpr int              vdr          = VDR_Q5_0_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q5_0<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
@@ -2352,7 +2383,7 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q8_0> {
     static constexpr int              vdr          = VDR_Q8_0_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q8_0<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
@@ -2368,7 +2399,7 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q3_K> {
     static constexpr int              vdr          = VDR_Q3_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q3_K<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q3_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_16_q8_1_mma<mmq_x, mmq_y, nwarps>;
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q3_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
@@ -2376,7 +2407,7 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q4_K> {
     static constexpr int              vdr          = VDR_Q4_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q4_K<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q4_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q4_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
@@ -2384,7 +2415,7 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q5_K> {
     static constexpr int              vdr          = VDR_Q5_K_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q5_K<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q5_K_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q5_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
@@ -2396,11 +2427,59 @@ struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_Q6_K> {
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q6_K_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ2_XXS> {
+    static constexpr int              vdr          = VDR_IQ2_XXS_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq2_xxs<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ2_XS> {
+    static constexpr int              vdr          = VDR_IQ2_XS_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq2_xs<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_16_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_16_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ2_S> {
+    static constexpr int              vdr          = VDR_IQ2_S_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq2_s<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_16_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_16_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ3_XXS> {
+    static constexpr int              vdr          = VDR_IQ3_XXS_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq3_xxs<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ3_S> {
+    static constexpr int              vdr          = VDR_IQ3_S_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq3_s<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
+template <int mmq_x, int mmq_y, int nwarps, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ1_S> {
+    static constexpr int              vdr          = VDR_IQ1_S_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq1_s<mmq_y, nwarps, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_1_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_1_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
+};
+
 template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ4_NL> {
     static constexpr int              vdr          = VDR_IQ4_NL_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq4_nl<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
@@ -2408,7 +2487,7 @@ template <int mmq_x, int mmq_y, int nwarps, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, GGML_TYPE_IQ4_XS> {
     static constexpr int              vdr          = VDR_IQ4_XS_Q8_1_MMQ;
     static constexpr load_tiles_mmq_t load_tiles   = load_tiles_iq4_xs<mmq_y, nwarps, need_check>;
-    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, nwarps, MMQ_Q8_1_DS_LAYOUT_D4>;
     static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y, nwarps>;
 };
 
@@ -2837,6 +2916,12 @@ extern DECL_MMQ_CASE(GGML_TYPE_Q3_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q4_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q5_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q6_K);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ2_XXS);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ2_XS);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ2_S);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ3_XXS);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ3_S);
+extern DECL_MMQ_CASE(GGML_TYPE_IQ1_S);
 extern DECL_MMQ_CASE(GGML_TYPE_IQ4_NL);
 extern DECL_MMQ_CASE(GGML_TYPE_IQ4_XS);
 
diff --git a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
index ffeb3c27df2f43..d7874e6eaf8328 100755
--- a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
+++ b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
@@ -23,7 +23,8 @@
 TYPES_MMQ = [
     "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0",
     "GGML_TYPE_Q2_K", "GGML_TYPE_Q3_K", "GGML_TYPE_Q4_K", "GGML_TYPE_Q5_K", "GGML_TYPE_Q6_K",
-    "GGML_TYPE_IQ4_NL", "GGML_TYPE_IQ4_XS"
+    "GGML_TYPE_IQ2_XXS", "GGML_TYPE_IQ2_XS", "GGML_TYPE_IQ2_S", "GGML_TYPE_IQ3_XXS", "GGML_TYPE_IQ3_S",
+    "GGML_TYPE_IQ1_S", "GGML_TYPE_IQ4_NL", "GGML_TYPE_IQ4_XS"
 ]
 
 SOURCE_MMQ = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq1_s.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq1_s.cu
new file mode 100644
index 00000000000000..84ec8502947313
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq1_s.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ1_S);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_s.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_s.cu
new file mode 100644
index 00000000000000..583c4e5a515012
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_s.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ2_S);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xs.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xs.cu
new file mode 100644
index 00000000000000..edaf1560defdd5
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xs.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ2_XS);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xxs.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xxs.cu
new file mode 100644
index 00000000000000..233d9342c99411
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq2_xxs.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ2_XXS);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_s.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_s.cu
new file mode 100644
index 00000000000000..6092dc71363412
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_s.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ3_S);
diff --git a/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_xxs.cu b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_xxs.cu
new file mode 100644
index 00000000000000..1d5bd201fb5a93
--- /dev/null
+++ b/ggml/src/ggml-cuda/template-instances/mmq-instance-iq3_xxs.cu
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_IQ3_XXS);
diff --git a/ggml/src/ggml-cuda/vecdotq.cuh b/ggml/src/ggml-cuda/vecdotq.cuh
index 6a17d0f3eb0d29..40091a0ef07b43 100644
--- a/ggml/src/ggml-cuda/vecdotq.cuh
+++ b/ggml/src/ggml-cuda/vecdotq.cuh
@@ -188,6 +188,27 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_imp
     return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
 }
 
+template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_16_q8_1_impl(
+    const int * v, const int * u, const float * d8_0, const float & d8_1) {
+
+    float sumf = 0.0f;
+
+#pragma unroll
+    for (int i0 = 0; i0 < vdr; i0 += QI8_0/2) {
+        int sumi = 0;
+
+#pragma unroll
+        for (int i = i0; i < i0 + QI8_0/2; ++i) {
+            // SIMD dot product of quantized values
+            sumi = ggml_cuda_dp4a(v[i], u[i], sumi);
+        }
+
+        sumf += d8_0[i0/(QI8_0/2)]*sumi;
+    }
+
+    return d8_1*sumf;
+}
+
 #define VDR_Q2_K_Q8_1_MMVQ 1
 #define VDR_Q2_K_Q8_1_MMQ  4
 

From c69c63039cd75f8f33f253ab7485d82a8b4cd403 Mon Sep 17 00:00:00 2001
From: compilade <git@compilade.net>
Date: Sat, 20 Jul 2024 21:53:01 -0400
Subject: [PATCH 088/125] convert_hf : fix Gemma v1 conversion (#8597)

* convert_hf : fix Gemma v1 conversion

* convert_hf : allow renaming tokens, but with a warning

* convert_hf : fix Gemma v1 not setting BOS and EOS tokens
---
 convert_hf_to_gguf.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index a0661f120eea71..fba8dbbedebbd2 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -753,7 +753,8 @@ def _create_vocab_sentencepiece(self):
                     token_id = int(token_id)
                     token: str = token_data["content"]
                     if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
-                        assert tokens[token_id] == token.encode("utf-8")
+                        if tokens[token_id] != token.encode("utf-8"):
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token!r}')
                     if token_data.get("special") or self.does_token_look_special(token):
                         toktypes[token_id] = SentencePieceTokenTypes.CONTROL
                     else:
@@ -1312,6 +1313,7 @@ def set_vocab(self):
         special_vocab._set_special_token("prefix", 1)
         special_vocab._set_special_token("suffix", 3)
         special_vocab._set_special_token("middle", 2)
+        special_vocab.chat_template = None  # do not add it twice
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
@@ -2014,7 +2016,8 @@ def set_vocab(self):
                     token_id = int(token_id)
                     token = foken_data["content"].encode("utf-8")
                     if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
-                        assert tokens[token_id] == token
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
                     tokens[token_id] = token
                     scores[token_id] = -1000.0
                     toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
@@ -2030,7 +2033,8 @@ def set_vocab(self):
                     token_id = int(foken_data["id"])
                     token = foken_data["content"].encode("utf-8")
                     if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
-                        assert tokens[token_id] == token
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
                     tokens[token_id] = token
                     scores[token_id] = -1000.0
                     toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
@@ -2269,7 +2273,8 @@ def set_vocab(self):
                         chat_eos_token_id = token_id
                     token = token.encode("utf-8")
                     if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
-                        assert(tokens[token_id] == token)
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
                     tokens[token_id] = token
                     scores[token_id] = -1000.0
                     toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
@@ -2288,7 +2293,8 @@ def set_vocab(self):
                         chat_eos_token_id = token_id
                     token = token.encode("utf-8")
                     if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
-                        assert(tokens[token_id] == token)
+                        if tokens[token_id] != token:
+                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token.decode("utf-8")!r}')
                     tokens[token_id] = token
                     scores[token_id] = -1000.0
                     toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
@@ -2474,6 +2480,7 @@ def set_vocab(self):
         special_vocab._set_special_token("middle", 68)
         special_vocab._set_special_token("fsep",   70)
         special_vocab._set_special_token("eot",    107)
+        special_vocab.chat_template = None  # do not add it twice
         special_vocab.add_to_gguf(self.gguf_writer)
 
         self.gguf_writer.add_add_space_prefix(False)

From 328884f4219c0228673cf1870ac63987fb4f9fd0 Mon Sep 17 00:00:00 2001
From: compilade <git@compilade.net>
Date: Sat, 20 Jul 2024 21:58:49 -0400
Subject: [PATCH 089/125] gguf-py : fix some metadata name extraction edge
 cases (#8591)

* gguf-py : fix some metadata name extraction edge cases

* convert_lora : use the lora dir for the model card path

* gguf-py : more metadata edge cases fixes

Multiple finetune versions are now joined together,
and the removal of the basename annotation on trailing versions
is more robust.

* gguf-py : add more name metadata extraction tests

* convert_lora : fix default filename

The default filename was previously hardcoded.

* convert_hf : Model.fname_out can no longer be None

* gguf-py : do not use title case for naming convention

Some models use acronyms in lowercase,
which can't be title-cased like other words,
so it's best to simply use the same case
as in the original model name.

Note that the size label still has an uppercased suffix
to make it distinguishable from the context size of a finetune.
---
 convert_hf_to_gguf.py          | 38 ++++++++++++-------------
 convert_lora_to_gguf.py        | 26 +++++++++++------
 gguf-py/gguf/metadata.py       | 35 +++++++++++++++++------
 gguf-py/gguf/utility.py        |  6 ++--
 gguf-py/tests/test_metadata.py | 51 ++++++++++++++++++++++++++++++++--
 5 files changed, 112 insertions(+), 44 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index fba8dbbedebbd2..139a92801fabe6 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -48,7 +48,7 @@ class Model:
 
     dir_model: Path
     ftype: gguf.LlamaFileType
-    fname_out: Path | None
+    fname_out: Path
     is_big_endian: bool
     endianess: gguf.GGUFEndian
     use_temp_file: bool
@@ -62,11 +62,12 @@ class Model:
     gguf_writer: gguf.GGUFWriter
     model_name: str | None
     metadata_override: Path | None
+    dir_model_card: Path
 
     # subclasses should define this!
     model_arch: gguf.MODEL_ARCH
 
-    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path | None, is_big_endian: bool = False,
+    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool = False,
                  use_temp_file: bool = False, eager: bool = False,
                  metadata_override: Path | None = None, model_name: str | None = None,
                  split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False):
@@ -90,6 +91,7 @@ def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path |
         self.tensor_names = None
         self.metadata_override = metadata_override
         self.model_name = model_name
+        self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
 
         # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type
         if self.ftype == gguf.LlamaFileType.GUESSED:
@@ -345,7 +347,7 @@ def prepare_metadata(self, vocab_only: bool):
 
         total_params, shared_params, expert_params, expert_count = self.gguf_writer.get_total_parameter_count()
 
-        self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model, self.model_name, total_params)
+        self.metadata = gguf.Metadata.load(self.metadata_override, self.dir_model_card, self.model_name, total_params)
 
         # Fallback to model directory name if metadata name is still missing
         if self.metadata.name is None:
@@ -359,27 +361,22 @@ def prepare_metadata(self, vocab_only: bool):
         output_type: str = self.ftype.name.partition("_")[2]
 
         # Filename Output
-        # Note: `not is_dir()` is used because `.is_file()` will not detect
-        #       file template strings as it doesn't actually exist as a file
-        if self.fname_out is not None and not self.fname_out.is_dir():
-            # Output path is a custom defined templated filename
-
-            # Process templated file name with the output ftype, useful with the "auto" ftype
-            self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)
-        else:
+        if self.fname_out.is_dir():
             # Generate default filename based on model specification and available metadata
             if not vocab_only:
                 fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, self.metadata.size_label, output_type, model_type="LoRA" if total_params < 0 else None)
             else:
                 fname_default: str = gguf.naming_convention(self.metadata.name, self.metadata.basename, self.metadata.finetune, self.metadata.version, size_label=None, output_type=None, model_type="vocab")
 
-            # Check if preferred output directory path was provided
-            if self.fname_out is not None and self.fname_out.is_dir():
-                # output path is a directory
-                self.fname_out = self.fname_out / f"{fname_default}.gguf"
-            else:
-                # output in the same directory as the model by default
-                self.fname_out = self.dir_model / f"{fname_default}.gguf"
+            # Use the default filename
+            self.fname_out = self.fname_out / f"{fname_default}.gguf"
+        else:
+            # Output path is a custom defined templated filename
+            # Note: `not is_dir()` is used because `.is_file()` will not detect
+            #       file template strings as it doesn't actually exist as a file
+
+            # Process templated file name with the output ftype, useful with the "auto" ftype
+            self.fname_out = self.fname_out.parent / gguf.fill_templated_filename(self.fname_out.name, output_type)
 
         self.set_type()
 
@@ -3634,10 +3631,10 @@ def main() -> None:
         logger.error("Error: Cannot use temp file when splitting")
         sys.exit(1)
 
-    fname_out = None
-
     if args.outfile is not None:
         fname_out = args.outfile
+    else:
+        fname_out = dir_model
 
     logger.info(f"Loading model: {dir_model.name}")
 
@@ -3668,7 +3665,6 @@ def main() -> None:
         else:
             logger.info("Exporting model...")
             model_instance.write()
-            assert model_instance.fname_out is not None
             out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out
             logger.info(f"Model successfully exported to {out_path}")
 
diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py
index 66e8da37cba7cf..a88d0d4a978a9d 100755
--- a/convert_lora_to_gguf.py
+++ b/convert_lora_to_gguf.py
@@ -290,7 +290,7 @@ def parse_args() -> argparse.Namespace:
         fname_out = args.outfile
     else:
         # output in the same directory as the model by default
-        fname_out = dir_lora / 'ggml-lora-{ftype}.gguf'
+        fname_out = dir_lora
 
     if os.path.exists(input_model):
         # lazy import load_file only if lora is in safetensors format.
@@ -304,12 +304,6 @@ def parse_args() -> argparse.Namespace:
     # load base model
     logger.info(f"Loading base model: {dir_base_model.name}")
     hparams = Model.load_hparams(dir_base_model)
-
-    with open(lora_config, "r") as f:
-        lparams: dict[str, Any] = json.load(f)
-
-    alpha: float = lparams["lora_alpha"]
-
     with torch.inference_mode():
         try:
             model_class = Model.from_model_architecture(hparams["architectures"][0])
@@ -320,12 +314,21 @@ def parse_args() -> argparse.Namespace:
         class LoraModel(model_class):
             model_arch = model_class.model_arch
 
+            lora_alpha: float
+
+            def __init__(self, *args, dir_lora_model: Path, lora_alpha: float, **kwargs):
+
+                super().__init__(*args, **kwargs)
+
+                self.dir_model_card = dir_lora_model
+                self.lora_alpha = float(lora_alpha)
+
             def set_type(self):
                 self.gguf_writer.add_type(gguf.GGUFType.ADAPTER)
                 self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
 
             def set_gguf_parameters(self):
-                self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, float(alpha))
+                self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
                 super().set_gguf_parameters()
 
             def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
@@ -368,6 +371,11 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
                     yield (dest_name + ".lora_a", lora_a)
                     yield (dest_name + ".lora_b", lora_b)
 
+        with open(lora_config, "r") as f:
+            lparams: dict[str, Any] = json.load(f)
+
+        alpha: float = lparams["lora_alpha"]
+
         model_instance = LoraModel(
             dir_base_model,
             ftype,
@@ -376,6 +384,8 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
             use_temp_file=False,
             eager=args.no_lazy,
             dry_run=args.dry_run,
+            dir_lora_model=dir_lora,
+            lora_alpha=alpha,
         )
 
         logger.info("Exporting model...")
diff --git a/gguf-py/gguf/metadata.py b/gguf-py/gguf/metadata.py
index bac6ebfb3777a0..15189f71775008 100644
--- a/gguf-py/gguf/metadata.py
+++ b/gguf-py/gguf/metadata.py
@@ -54,6 +54,7 @@ def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Pat
 
         model_card = Metadata.load_model_card(model_path)
         hf_params = Metadata.load_hf_parameters(model_path)
+        # TODO: load adapter_config.json when possible, it usually contains the base model of the LoRA adapter
 
         # heuristics
         metadata = Metadata.apply_metadata_heuristic(metadata, model_card, hf_params, model_path, total_params)
@@ -177,6 +178,12 @@ def get_model_id_components(model_id: Optional[str] = None, total_params: int =
             org_component = None
 
         name_parts: list[str] = model_full_name_component.split('-')
+
+        # Remove empty parts
+        for i in reversed(range(len(name_parts))):
+            if len(name_parts[i]) == 0:
+                del name_parts[i]
+
         name_types: list[
             set[Literal["basename", "size_label", "finetune", "version", "type"]]
         ] = [set() for _ in name_parts]
@@ -223,9 +230,19 @@ def get_model_id_components(model_id: Optional[str] = None, total_params: int =
                 name_parts[i] = part
             # Some easy to recognize finetune names
             elif i > 0 and re.fullmatch(r'chat|instruct|vision|lora', part, re.IGNORECASE):
-                name_types[i].add("finetune")
-                if part.lower() == "lora":
-                    name_parts[i] = "LoRA"
+                if total_params < 0 and part.lower() == "lora":
+                    # ignore redundant "lora" in the finetune part when the output is a lora adapter
+                    name_types[i].add("type")
+                else:
+                    name_types[i].add("finetune")
+
+        # Ignore word-based size labels when there is at least a number-based one present
+        # TODO: should word-based size labels always be removed instead?
+        if any(c.isdecimal() for n, t in zip(name_parts, name_types) if "size_label" in t for c in n):
+            for n, t in zip(name_parts, name_types):
+                if "size_label" in t:
+                    if all(c.isalpha() for c in n):
+                        t.remove("size_label")
 
         at_start = True
         # Find the basename through the annotated name
@@ -240,18 +257,18 @@ def get_model_id_components(model_id: Optional[str] = None, total_params: int =
 
         # Remove the basename annotation from trailing version
         for part, t in zip(reversed(name_parts), reversed(name_types)):
-            if "basename" in t:
-                if len(t) > 1:
-                    t.remove("basename")
+            if "basename" in t and len(t) > 1:
+                t.remove("basename")
             else:
                 break
 
         basename = "-".join(n for n, t in zip(name_parts, name_types) if "basename" in t) or None
-        size_label = "-".join(s for s, t in zip(name_parts, name_types) if "size_label" in t) or None
+        # Deduplicate size labels using order-preserving 'dict' ('set' seems to sort the keys)
+        size_label = "-".join(dict.fromkeys(s for s, t in zip(name_parts, name_types) if "size_label" in t).keys()) or None
         finetune = "-".join(f for f, t in zip(name_parts, name_types) if "finetune" in t) or None
         # TODO: should the basename version always be excluded?
-        # TODO: should multiple versions be joined together?
-        version = ([v for v, t, in zip(name_parts, name_types) if "version" in t and "basename" not in t] or [None])[-1]
+        # NOTE: multiple finetune versions are joined together
+        version = "-".join(v for v, t, in zip(name_parts, name_types) if "version" in t and "basename" not in t) or None
 
         if size_label is None and finetune is None and version is None:
             # Too ambiguous, output nothing
diff --git a/gguf-py/gguf/utility.py b/gguf-py/gguf/utility.py
index ef76831b521eef..40d59b75ee04ec 100644
--- a/gguf-py/gguf/utility.py
+++ b/gguf-py/gguf/utility.py
@@ -50,15 +50,15 @@ def naming_convention(model_name: str | None, base_name: str | None, finetune_st
     # Reference: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#gguf-naming-convention
 
     if base_name is not None:
-        name = base_name.strip().title().replace(' ', '-').replace('/', '-')
+        name = base_name.strip().replace(' ', '-').replace('/', '-')
     elif model_name is not None:
-        name = model_name.strip().title().replace(' ', '-').replace('/', '-')
+        name = model_name.strip().replace(' ', '-').replace('/', '-')
     else:
         name = "ggml-model"
 
     parameters = f"-{size_label}" if size_label is not None else ""
 
-    finetune = f"-{finetune_string.strip().title().replace(' ', '-')}" if finetune_string is not None else ""
+    finetune = f"-{finetune_string.strip().replace(' ', '-')}" if finetune_string is not None else ""
 
     version = f"-{version_string.strip().replace(' ', '-')}" if version_string is not None else ""
 
diff --git a/gguf-py/tests/test_metadata.py b/gguf-py/tests/test_metadata.py
index 3fac8218883f1f..81a2a30ae60f40 100755
--- a/gguf-py/tests/test_metadata.py
+++ b/gguf-py/tests/test_metadata.py
@@ -54,7 +54,7 @@ def test_get_model_id_components(self):
         self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Meta-Llama-3-8B"),
                          ('Meta-Llama-3-8B', "NousResearch", 'Meta-Llama-3', None, None, '8B'))
 
-        # Can't detect all non standard form in a heuristically safe way... best to err in caution and output nothing...
+        # Non standard naming
         self.assertEqual(gguf.Metadata.get_model_id_components("Qwen1.5-MoE-A2.7B-Chat"),
                          ('Qwen1.5-MoE-A2.7B-Chat', None, 'Qwen1.5-MoE', 'Chat', None, 'A2.7B'))
 
@@ -71,7 +71,7 @@ def test_get_model_id_components(self):
         self.assertEqual(gguf.Metadata.get_model_id_components("delphi-suite/stories-llama2-50k", 50 * 10**3),
                          ('stories-llama2-50k', 'delphi-suite', 'stories-llama2', None, None, '50K'))
 
-        # None standard and not easy to disambiguate
+        # Non standard and not easy to disambiguate
         self.assertEqual(gguf.Metadata.get_model_id_components("DeepSeek-Coder-V2-Lite-Instruct"),
                          ('DeepSeek-Coder-V2-Lite-Instruct', None, 'DeepSeek-Coder-V2-Lite', 'Instruct', None, None))
 
@@ -123,6 +123,51 @@ def test_get_model_id_components(self):
         self.assertEqual(gguf.Metadata.get_model_id_components("bigscience/bloom-7b1-petals"),
                          ('bloom-7b1-petals', 'bigscience', 'bloom', 'petals', None, '7.1B'))
 
+        # Ignore full-text size labels when there are number-based ones, and deduplicate size labels
+        self.assertEqual(gguf.Metadata.get_model_id_components("MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1"),
+                         ('GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1', 'MaziyarPanahi', 'GreenNode-mini', 'multilingual-v1olet-Mistral-Instruct', 'v0.1', '7B'))
+
+        # Instruct in a name without a size label
+        self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/Mistral-Nemo-Instruct-2407"),
+                         ('Mistral-Nemo-Instruct-2407', 'mistralai', 'Mistral-Nemo', 'Instruct', '2407', None))
+
+        # Non-obvious splitting relying on 'chat' keyword
+        self.assertEqual(gguf.Metadata.get_model_id_components("deepseek-ai/DeepSeek-V2-Chat-0628"),
+                         ('DeepSeek-V2-Chat-0628', 'deepseek-ai', 'DeepSeek-V2', 'Chat', '0628', None))
+
+        # Multiple versions
+        self.assertEqual(gguf.Metadata.get_model_id_components("OpenGVLab/Mini-InternVL-Chat-2B-V1-5"),
+                         ('Mini-InternVL-Chat-2B-V1-5', 'OpenGVLab', 'Mini-InternVL', 'Chat', 'V1-5', '2B'))
+
+        # TODO: DPO in the name
+        self.assertEqual(gguf.Metadata.get_model_id_components("jondurbin/bagel-dpo-2.8b-v0.2"),
+                         ('bagel-dpo-2.8b-v0.2', 'jondurbin', 'bagel-dpo', None, 'v0.2', '2.8B'))
+
+        # DPO in name, but can't be used for the finetune to keep 'LLaMA-3' in the basename
+        self.assertEqual(gguf.Metadata.get_model_id_components("voxmenthe/SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized"),
+                         ('SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized', 'voxmenthe', 'SFR-Iterative-DPO-LLaMA-3', 'R-unquantized', None, '8B'))
+
+        # Too ambiguous
+        # TODO: should "base" be a 'finetune' or 'size_label'?
+        # (in this case it should be a size label, but other models use it to signal that they are not finetuned)
+        self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Florence-2-base"),
+                         ('Florence-2-base', 'microsoft', None, None, None, None))
+
+        ## Invalid cases ##
+
+        # Start with a dash and has dashes in rows
+        self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/-Mistral--Nemo-Base-2407-"),
+                         ('-Mistral--Nemo-Base-2407-', 'mistralai', 'Mistral-Nemo-Base', None, '2407', None))
+
+        ## LoRA ##
+
+        self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B"),
+                         ('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration-LoRA', None, '8B'))
+
+        # Negative size --> output is a LoRA adaper --> prune "LoRA" out of the name to avoid redundancy with the suffix
+        self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B", -1234),
+                         ('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration', None, '8B'))
+
     def test_apply_metadata_heuristic_from_model_card(self):
         model_card = {
             'tags': ['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'],
@@ -134,7 +179,7 @@ def test_apply_metadata_heuristic_from_model_card(self):
         }
         got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None)
         expect = gguf.Metadata()
-        expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': 'v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}]
+        expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': '14-v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}]
         expect.tags=['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl']
         expect.languages=['en']
         expect.datasets=['teknium/OpenHermes-2.5']

From 22f281aa16f44d8f6ec2c180a0685ff27e04e714 Mon Sep 17 00:00:00 2001
From: M-A <maruel@gmail.com>
Date: Sat, 20 Jul 2024 22:09:17 -0400
Subject: [PATCH 090/125] examples : Rewrite
 pydantic_models_to_grammar_examples.py (#8493)

Changes:

- Move each example into its own function. This makes the code much
  easier to read and understand.
- Make the program easy to only run one test by commenting out function
  calls in main().
- Make the output easy to parse by indenting the output for each example.
- Add shebang and +x bit to make it clear it's an executable.
- Make the host configurable via --host with a default 127.0.0.1:8080.
- Make the code look in the tools list to call the registered tool,
  instead of hardcoding the returned values. This makes the code more
  copy-pastable.
- Add error checking, so that the program exits 1 if the LLM didn't
  returned expected values. It's super useful to check for correctness.

Testing:

- Tested with Mistral-7B-Instruct-v0.3 in F16 and Q5_K_M and
  Meta-Llama-3-8B-Instruct in F16 and Q5_K_M.
  - I did not observe a failure even once in Mistral-7B-Instruct-v0.3.
  - Llama-3 failed about a third of the time in example_concurrent: it
    only returned one call instead of 3. Even for F16.

Potential follow ups:

- Do not fix the prompt encoding yet. Surprisingly it mostly works even
  if the prompt encoding is not model optimized.
- Add chained answer and response.

Test only change.
---
 .../pydantic_models_to_grammar_examples.py    | 359 +++++++++++-------
 1 file changed, 222 insertions(+), 137 deletions(-)
 mode change 100644 => 100755 examples/pydantic_models_to_grammar_examples.py

diff --git a/examples/pydantic_models_to_grammar_examples.py b/examples/pydantic_models_to_grammar_examples.py
old mode 100644
new mode 100755
index 504ed98df64f29..eb000d5ccba24b
--- a/examples/pydantic_models_to_grammar_examples.py
+++ b/examples/pydantic_models_to_grammar_examples.py
@@ -1,8 +1,15 @@
-# Function calling example using pydantic models.
+#!/usr/bin/env python3
+
+"""Function calling example using pydantic models."""
+
 from __future__ import annotations
 
+import argparse
 import datetime
 import json
+import logging
+import textwrap
+import sys
 from enum import Enum
 from typing import Optional, Union
 
@@ -12,30 +19,54 @@
                                         create_dynamic_model_from_function, generate_gbnf_grammar_and_documentation)
 
 
-# Function to get completion on the llama.cpp server with grammar.
-def create_completion(prompt, grammar):
-    headers = {"Content-Type": "application/json"}
-    data = {"prompt": prompt, "grammar": grammar}
-
-    response = requests.post("http://127.0.0.1:8080/completion", headers=headers, json=data)
-    data = response.json()
+def create_completion(host, prompt, gbnf_grammar):
+    """Calls the /completion API on llama-server.
 
+    See
+    https://github.com/ggerganov/llama.cpp/tree/HEAD/examples/server#api-endpoints
+    """
+    print(f"  Request:\n    Grammar:\n{textwrap.indent(gbnf_grammar, '      ')}\n    Prompt:\n{textwrap.indent(prompt.rstrip(), '      ')}")
+    headers = {"Content-Type": "application/json"}
+    data = {"prompt": prompt, "grammar": gbnf_grammar}
+    result = requests.post(f"http://{host}/completion", headers=headers, json=data).json()
     assert data.get("error") is None, data
-
-    print(data["content"])
-    return data["content"]
+    logging.info("Result: %s", result)
+    content = result["content"]
+    print(f"  Model: {result['model']}")
+    print(f"  Result:\n{textwrap.indent(json.dumps(json.loads(content), indent=2), '    ')}")
+    return content
 
 
 # A function for the agent to send a message to the user.
 class SendMessageToUser(BaseModel):
-    """
-    Send a message to the User.
-    """
+    """Send a message to the User."""
     chain_of_thought: str = Field(..., description="Your chain of thought while sending the message.")
     message: str = Field(..., description="Message you want to send to the user.")
 
     def run(self):
-        print(self.message)
+        print(f"SendMessageToUser: {self.message}")
+
+
+def example_rce(host):
+    """Minimal test case where the LLM call an arbitrary python function."""
+    print("- example_rce")
+    tools = [SendMessageToUser]
+    gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
+        pydantic_model_list=tools, outer_object_name="function",
+        outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters")
+    system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation
+    user_message = "What is 42 * 42?"
+    prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
+    text = create_completion(host, prompt, gbnf_grammar)
+    json_data = json.loads(text)
+    tools_map = {tool.__name__:tool for tool in tools}
+    # This finds "SendMessageToUser":
+    tool = tools_map.get(json_data["function"])
+    if not tool:
+        print(f"Error: unknown tool {json_data['function']}")
+        return 1
+    tool(**json_data["function_parameters"]).run()
+    return 0
 
 
 # Enum for the calculator tool.
@@ -46,11 +77,11 @@ class MathOperation(Enum):
     DIVIDE = "divide"
 
 
-# Simple pydantic calculator tool for the agent that can add, subtract, multiply, and divide. Docstring and description of fields will be used in system prompt.
+# Simple pydantic calculator tool for the agent that can add, subtract,
+# multiply, and divide. Docstring and description of fields will be used in
+# system prompt.
 class Calculator(BaseModel):
-    """
-    Perform a math operation on two numbers.
-    """
+    """Perform a math operation on two numbers."""
     number_one: Union[int, float] = Field(..., description="First number.")
     operation: MathOperation = Field(..., description="Math operation to perform.")
     number_two: Union[int, float] = Field(..., description="Second number.")
@@ -68,55 +99,61 @@ def run(self):
             raise ValueError("Unknown operation.")
 
 
-# Here the grammar gets generated by passing the available function models to generate_gbnf_grammar_and_documentation function. This also generates a documentation usable by the LLM.
-# pydantic_model_list is the list of pydanitc models
-# outer_object_name is an optional name for an outer object around the actual model object. Like a "function" object with "function_parameters" which contains the actual model object. If None, no outer object will be generated
-# outer_object_content is the name of outer object content.
-# model_prefix is the optional prefix for models in the documentation. (Default="Output Model")
-# fields_prefix is the prefix for the model fields in the documentation. (Default="Output Fields")
-gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
-    pydantic_model_list=[SendMessageToUser, Calculator], outer_object_name="function",
-    outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters")
-
-print(gbnf_grammar)
-print(documentation)
-
-system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation
+def example_calculator(host):
+    """Have the LLM ask to get a calculation done.
 
-user_message = "What is 42 * 42?"
-prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
+    Here the grammar gets generated by passing the available function models to
+    generate_gbnf_grammar_and_documentation function. This also generates a
+    documentation usable by the LLM.
 
-text = create_completion(prompt=prompt, grammar=gbnf_grammar)
-# This should output something like this:
-# {
-#     "function": "calculator",
-#     "function_parameters": {
-#         "number_one": 42,
-#         "operation": "multiply",
-#         "number_two": 42
-#     }
-# }
-function_dictionary = json.loads(text)
-if function_dictionary["function"] == "calculator":
-    function_parameters = {**function_dictionary["function_parameters"]}
+    pydantic_model_list is the list of pydantic models outer_object_name is an
+    optional name for an outer object around the actual model object. Like a
+    "function" object with "function_parameters" which contains the actual model
+    object. If None, no outer object will be generated outer_object_content is
+    the name of outer object content.
 
-    print(Calculator(**function_parameters).run())
-    # This should output: 1764
+    model_prefix is the optional prefix for models in the documentation. (Default="Output Model")
+    fields_prefix is the prefix for the model fields in the documentation. (Default="Output Fields")
+    """
+    print("- example_calculator")
+    tools = [SendMessageToUser, Calculator]
+    gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
+        pydantic_model_list=tools, outer_object_name="function",
+        outer_object_content="function_parameters", model_prefix="Function", fields_prefix="Parameters")
+    system_message = "You are an advanced AI, tasked to assist the user by calling functions in JSON format. The following are the available functions and their parameters and types:\n\n" + documentation
+    user_message1 = "What is 42 * 42?"
+    prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message1}<|im_end|>\n<|im_start|>assistant"
+    text = create_completion(host, prompt, gbnf_grammar)
+    json_data = json.loads(text)
+    expected = {
+        "function": "Calculator",
+        "function_parameters": {
+            "number_one": 42,
+            "operation": "multiply",
+            "number_two": 42
+        }
+    }
+    if json_data != expected:
+        print("  Result is not as expected!")
+    tools_map = {tool.__name__:tool for tool in tools}
+    # This finds "Calculator":
+    tool = tools_map.get(json_data["function"])
+    if not tool:
+        print(f"Error: unknown tool {json_data['function']}")
+        return 1
+    result = tool(**json_data["function_parameters"]).run()
+    print(f"  Call {json_data['function']} gave result {result}")
+    return 0
 
 
-# A example structured output based on pydantic models. The LLM will create an entry for a Book database out of an unstructured text.
 class Category(Enum):
-    """
-    The category of the book.
-    """
+    """The category of the book."""
     Fiction = "Fiction"
     NonFiction = "Non-Fiction"
 
 
 class Book(BaseModel):
-    """
-    Represents an entry about a book.
-    """
+    """Represents an entry about a book."""
     title: str = Field(..., description="Title of the book.")
     author: str = Field(..., description="Author of the book.")
     published_year: Optional[int] = Field(..., description="Publishing year of the book.")
@@ -125,33 +162,42 @@ class Book(BaseModel):
     summary: str = Field(..., description="Summary of the book.")
 
 
-# We need no additional parameters other than our list of pydantic models.
-gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation([Book])
-
-system_message = "You are an advanced AI, tasked to create a dataset entry in JSON for a Book. The following is the expected output model:\n\n" + documentation
-
-text = """The Feynman Lectures on Physics is a physics textbook based on some lectures by Richard Feynman, a Nobel laureate who has sometimes been called "The Great Explainer". The lectures were presented before undergraduate students at the California Institute of Technology (Caltech), during 1961–1963. The book's co-authors are Feynman, Robert B. Leighton, and Matthew Sands."""
-prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
+def example_struct(host):
+    """A example structured output based on pydantic models.
 
-text = create_completion(prompt=prompt, grammar=gbnf_grammar)
-
-json_data = json.loads(text)
+    The LLM will create an entry for a Book database out of an unstructured
+    text. We need no additional parameters other than our list of pydantic
+    models.
+    """
+    print("- example_struct")
+    tools = [Book]
+    gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(pydantic_model_list=tools)
+    system_message = "You are an advanced AI, tasked to create a dataset entry in JSON for a Book. The following is the expected output model:\n\n" + documentation
+    text = """The Feynman Lectures on Physics is a physics textbook based on some lectures by Richard Feynman, a Nobel laureate who has sometimes been called "The Great Explainer". The lectures were presented before undergraduate students at the California Institute of Technology (Caltech), during 1961–1963. The book's co-authors are Feynman, Robert B. Leighton, and Matthew Sands."""
+    prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
+    text = create_completion(host, prompt, gbnf_grammar)
+    json_data = json.loads(text)
+    # In this case, there's no function nor function_parameters.
+    # Here the result will vary based on the LLM used.
+    keys = sorted(["title", "author", "published_year", "keywords", "category", "summary"])
+    if keys != sorted(json_data.keys()):
+        print(f"Unexpected result: {sorted(json_data.keys())}")
+        return 1
+    book = Book(**json_data)
+    print(f"  As a Book object: %s" % book)
+    return 0
 
-print(Book(**json_data))
-# An example for parallel function calling with a Python function, a pydantic function model and an OpenAI like function definition.
 
 def get_current_datetime(output_format: Optional[str] = None):
-    """
-    Get the current date and time in the given format.
+    """Get the current date and time in the given format.
+
     Args:
          output_format: formatting string for the date and time, defaults to '%Y-%m-%d %H:%M:%S'
     """
-    if output_format is None:
-        output_format = '%Y-%m-%d %H:%M:%S'
-    return datetime.datetime.now().strftime(output_format)
+    return datetime.datetime.now().strftime(output_format or "%Y-%m-%d %H:%M:%S")
 
 
-# Example function to get the weather
+# Example function to get the weather.
 def get_current_weather(location, unit):
     """Get the current weather in a given location"""
     if "London" in location:
@@ -160,68 +206,107 @@ def get_current_weather(location, unit):
         return json.dumps({"location": "New York", "temperature": "24", "unit": unit.value})
     elif "North Pole" in location:
         return json.dumps({"location": "North Pole", "temperature": "-42", "unit": unit.value})
-    else:
-        return json.dumps({"location": location, "temperature": "unknown"})
-
-
-# Here is a function definition in OpenAI style
-current_weather_tool = {
-    "type": "function",
-    "function": {
-        "name": "get_current_weather",
-        "description": "Get the current weather in a given location",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "location": {
-                    "type": "string",
-                    "description": "The city and state, e.g. San Francisco, CA",
+    return json.dumps({"location": location, "temperature": "unknown"})
+
+
+def example_concurrent(host):
+    """An example for parallel function calling with a Python function, a pydantic
+    function model and an OpenAI like function definition.
+    """
+    print("- example_concurrent")
+    # Function definition in OpenAI style.
+    current_weather_tool = {
+        "type": "function",
+        "function": {
+            "name": "get_current_weather",
+            "description": "Get the current weather in a given location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA",
+                    },
+                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                 },
-                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                "required": ["location"],
             },
-            "required": ["location"],
         },
-    },
-}
-
-# Convert OpenAI function definition into pydantic model
-current_weather_tool_model = convert_dictionary_to_pydantic_model(current_weather_tool)
-# Add the actual function to a pydantic model
-current_weather_tool_model = add_run_method_to_dynamic_model(current_weather_tool_model, get_current_weather)
-
-# Convert normal Python function to a pydantic model
-current_datetime_model = create_dynamic_model_from_function(get_current_datetime)
-
-tool_list = [SendMessageToUser, Calculator, current_datetime_model, current_weather_tool_model]
-
-
-gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
-    pydantic_model_list=tool_list, outer_object_name="function",
-    outer_object_content="params", model_prefix="Function", fields_prefix="Parameters", list_of_outputs=True)
-
-system_message = "You are an advanced AI assistant. You are interacting with the user and with your environment by calling functions. You call functions by writing JSON objects, which represent specific function calls.\nBelow is a list of your available function calls:\n\n" + documentation
-
-
-text = """Get the date and time, get the current weather in celsius in London and solve the following calculation: 42 * 42"""
-prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
-
-text = create_completion(prompt=prompt, grammar=gbnf_grammar)
-
-json_data = json.loads(text)
-
-print(json_data)
-# Should output something like this:
-# [{'function': 'get_current_datetime', 'params': {'output_format': '%Y-%m-%d %H:%M:%S'}}, {'function': 'get_current_weather', 'params': {'location': 'London', 'unit': 'celsius'}}, {'function': 'Calculator', 'params': {'number_one': 42, 'operation': 'multiply', 'number_two': 42}}]
-
-
-for call in json_data:
-    if call["function"] == "Calculator":
-        print(Calculator(**call["params"]).run())
-    elif call["function"] == "get_current_datetime":
-        print(current_datetime_model(**call["params"]).run())  # pyright: ignore[reportAttributeAccessIssue]
-    elif call["function"] == "get_current_weather":
-        print(current_weather_tool_model(**call["params"]).run())  # pyright: ignore[reportAttributeAccessIssue]
-# Should output something like this:
-# 2024-01-14 13:36:06
-# {"location": "London", "temperature": "42", "unit": "celsius"}
-# 1764
+    }
+    # Convert OpenAI function definition into pydantic model.
+    current_weather_tool_model = convert_dictionary_to_pydantic_model(current_weather_tool)
+    # Add the actual function to a pydantic model.
+    current_weather_tool_model = add_run_method_to_dynamic_model(current_weather_tool_model, get_current_weather)
+
+    # Convert normal Python function to a pydantic model.
+    current_datetime_model = create_dynamic_model_from_function(get_current_datetime)
+
+    tools = [SendMessageToUser, Calculator, current_datetime_model, current_weather_tool_model]
+    gbnf_grammar, documentation = generate_gbnf_grammar_and_documentation(
+        pydantic_model_list=tools, outer_object_name="function",
+        outer_object_content="params", model_prefix="Function", fields_prefix="Parameters", list_of_outputs=True)
+    system_message = "You are an advanced AI assistant. You are interacting with the user and with your environment by calling functions. You call functions by writing JSON objects, which represent specific function calls.\nBelow is a list of your available function calls:\n\n" + documentation
+    text = """Get the date and time, get the current weather in celsius in London and solve the following calculation: 42 * 42"""
+    prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{text}<|im_end|>\n<|im_start|>assistant"
+    text = create_completion(host, prompt, gbnf_grammar)
+    json_data = json.loads(text)
+    expected = [
+      {
+        "function": "get_current_datetime",
+        "params": {
+          "output_format": "%Y-%m-%d %H:%M:%S"
+        }
+      },
+      {
+        "function": "get_current_weather",
+        "params": {
+          "location": "London",
+          "unit": "celsius"
+        }
+      },
+      {
+        "function": "Calculator",
+        "params": {
+          "number_one": 42,
+          "operation": "multiply",
+          "number_two": 42
+        }
+      }
+    ]
+    res = 0
+    if json_data != expected:
+        print("  Result is not as expected!")
+        print("  This can happen on highly quantized models")
+        res = 1
+    tools_map = {tool.__name__:tool for tool in tools}
+    for call in json_data:
+      tool = tools_map.get(call["function"])
+      if not tool:
+          print(f"Error: unknown tool {call['function']}")
+          return 1
+      result = tool(**call["params"]).run()
+      print(f"  Call {call['function']} returned {result}")
+    # Should output something like this:
+    #   Call get_current_datetime returned 2024-07-15 09:50:38
+    #   Call get_current_weather returned {"location": "London", "temperature": "42", "unit": "celsius"}
+    #   Call Calculator returned 1764
+    return res
+
+
+def main():
+    parser = argparse.ArgumentParser(description=sys.modules[__name__].__doc__)
+    parser.add_argument("--host", default="localhost:8080", help="llama.cpp server")
+    parser.add_argument("-v", "--verbose", action="store_true", help="enables logging")
+    args = parser.parse_args()
+    logging.basicConfig(level=logging.INFO if args.verbose else logging.ERROR)
+    ret = 0
+    # Comment out below to only run the example you want.
+    ret = ret or example_rce(args.host)
+    ret = ret or example_calculator(args.host)
+    ret = ret or example_struct(args.host)
+    ret = ret or example_concurrent(args.host)
+    return ret
+
+
+if __name__ == "__main__":
+    sys.exit(main())

From 45f2c19cc57286eead7b232ce8028273a817aa4d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 21 Jul 2024 16:45:10 +0300
Subject: [PATCH 091/125] flake.lock: Update (#8610)

---
 flake.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/flake.lock b/flake.lock
index 3e5125dde3b14d..940cda6a444168 100644
--- a/flake.lock
+++ b/flake.lock
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1720768451,
-        "narHash": "sha256-EYekUHJE2gxeo2pM/zM9Wlqw1Uw2XTJXOSAO79ksc4Y=",
+        "lastModified": 1721379653,
+        "narHash": "sha256-8MUgifkJ7lkZs3u99UDZMB4kbOxvMEXQZ31FO3SopZ0=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "7e7c39ea35c5cdd002cd4588b03a3fb9ece6fad9",
+        "rev": "1d9c2c9b3e71b9ee663d11c5d298727dace8d374",
         "type": "github"
       },
       "original": {

From b7c11d36e605b35206901d0e21905f1b99508e33 Mon Sep 17 00:00:00 2001
From: devojony <61173062+devojony@users.noreply.github.com>
Date: Mon, 22 Jul 2024 14:54:42 +0800
Subject: [PATCH 092/125] examples: fix android example cannot be generated
 continuously (#8621)

When generation ends `completion_loop()` should return a NULL, not the empty string
---
 examples/llama.android/llama/src/main/cpp/llama-android.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/llama.android/llama/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
index 92a6b16b1a00cf..2aafe23167557f 100644
--- a/examples/llama.android/llama/src/main/cpp/llama-android.cpp
+++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp
@@ -409,7 +409,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
 
     const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
     if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
-        return env->NewStringUTF("");
+        return nullptr;
     }
 
     auto new_token_chars = llama_token_to_piece(context, new_token_id);

From 04bab6b7da0ed2b0a57174a57784d602aabb6c10 Mon Sep 17 00:00:00 2001
From: Mark Zhuang <zhuangqiubin@gmail.com>
Date: Mon, 22 Jul 2024 15:56:45 +0800
Subject: [PATCH 093/125] ggml: fix compile error for RISC-V (#8623)

---
 ggml/src/ggml-quants.c | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-quants.c b/ggml/src/ggml-quants.c
index 1defed3caad391..47418597c00d8f 100644
--- a/ggml/src/ggml-quants.c
+++ b/ggml/src/ggml-quants.c
@@ -4748,7 +4748,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
 
         int sumi = __riscv_vmv_x_s_i32m1_i32(vs2);
 
-        sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi;
+        sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi;
     }
 
 #elif defined(__POWER9_VECTOR__)

From 628154492a0b2dcc954a3af99e5c267097568eae Mon Sep 17 00:00:00 2001
From: Jan Boon <jan.boon@kaetemi.be>
Date: Mon, 22 Jul 2024 16:02:09 +0800
Subject: [PATCH 094/125] server : update doc to clarify n_keep when there is
 bos token (#8619)

---
 examples/server/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index e477d1501f976f..ff4074517f9f55 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -444,7 +444,7 @@ node index.js
 
     `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity.
 
-    `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded.
+    `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token.
     By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
 
     `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.

From 50e05353e88d50b644688caa91f5955e8bdb9eb9 Mon Sep 17 00:00:00 2001
From: Douglas Hanley <thesecretaryofwar@gmail.com>
Date: Mon, 22 Jul 2024 03:06:17 -0500
Subject: [PATCH 095/125] llama : add Mistral Nemo inference support (#8604)

---
 convert_hf_to_gguf.py | 11 ++++++++++-
 src/llama.cpp         |  8 ++++----
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 139a92801fabe6..59410d6ce00ddb 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -239,6 +239,10 @@ def set_gguf_parameters(self):
             self.gguf_writer.add_expert_used_count(n_experts_used)
             logger.info(f"gguf: experts used count = {n_experts_used}")
 
+        if (head_dim := self.hparams.get("head_dim")) is not None:
+            self.gguf_writer.add_key_length(head_dim)
+            self.gguf_writer.add_value_length(head_dim)
+
         self.gguf_writer.add_file_type(self.ftype)
         logger.info(f"gguf: file type = {self.ftype}")
 
@@ -1481,7 +1485,12 @@ def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-        self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
+
+        if "head_dim" in hparams:
+            rope_dim = hparams["head_dim"]
+        else:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
 
         if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
             if self.hparams["rope_scaling"].get("type") == "linear":
diff --git a/src/llama.cpp b/src/llama.cpp
index 80a0dd0f4fb5e5..6046e5615e39e8 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -6137,10 +6137,10 @@ static bool llm_load_tensors(
 
                         layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
-                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
-                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
-                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
 
                         // optional bias tensors
                         layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);

From e093dd238282a980d248db0786063f8174400f70 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 22 Jul 2024 13:32:49 +0300
Subject: [PATCH 096/125] tests : re-enable tokenizer tests (#8611)

* models : remove duplicated gpt-2 vocab

* models : remove old stablelm vocab

* tests : re-enable MPT tokenizer tests

* tests : re-enable DeepSeek tokenizer tests

* cmake : sort

ggml-ci
---
 models/ggml-vocab-gpt2.gguf     | Bin 1766799 -> 0 bytes
 models/ggml-vocab-stablelm.gguf | Bin 1768581 -> 0 bytes
 tests/CMakeLists.txt            |  32 ++++++++++++++------------------
 3 files changed, 14 insertions(+), 18 deletions(-)
 delete mode 100644 models/ggml-vocab-gpt2.gguf
 delete mode 100644 models/ggml-vocab-stablelm.gguf

diff --git a/models/ggml-vocab-gpt2.gguf b/models/ggml-vocab-gpt2.gguf
deleted file mode 100644
index 1fbc72c1e4d9e210e5c5689b31e1debfa33d4b6a..0000000000000000000000000000000000000000
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literal 0
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diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index cfa70731534862..0207e3a5943c94 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -70,21 +70,19 @@ add_executable(test-tokenizer-0 test-tokenizer-0.cpp)
 target_link_libraries(test-tokenizer-0 PRIVATE common)
 install(TARGETS test-tokenizer-0 RUNTIME)
 
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-phi-3.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-falcon            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-bert-bge          ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bert-bge.gguf)
-# TODO: enable when fixed
-#       https://github.com/ggerganov/llama.cpp/pull/7036
-#llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt               ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
-#llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm      ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-llm.gguf)
-#llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-coder.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
-llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-command-r         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-command-r.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-coder.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm      ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-llm.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-falcon            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt               ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-phi-3.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-qwen2.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
 
 # build test-tokenizer-1-bpe target once and add many tests
 add_executable(test-tokenizer-1-bpe test-tokenizer-1-bpe.cpp)
@@ -92,16 +90,14 @@ target_link_libraries(test-tokenizer-1-bpe PRIVATE common)
 install(TARGETS test-tokenizer-1-bpe RUNTIME)
 
 # TODO: disabled due to slowness
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf --ignore-merges)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-aquila    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt       ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-stablelm  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-stablelm.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-2     ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf)
 #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt-neox  ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-neox.gguf)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf --ignore-merges)
+#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt       ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
 #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-refact    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
 #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-gpt2      ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt2.gguf)
-#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-bloom     ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf)
 
 # build test-tokenizer-1-spm target once and add many tests
 add_executable(test-tokenizer-1-spm test-tokenizer-1-spm.cpp)

From 6f11a83e4e7700fdf353ed4a29599cb662c792f6 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 22 Jul 2024 13:33:22 +0300
Subject: [PATCH 097/125] llama : allow overrides for tokenizer flags (#8614)

ggml-ci
---
 src/llama.cpp | 26 ++++++++------------------
 1 file changed, 8 insertions(+), 18 deletions(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index 6046e5615e39e8..4f3789b2a8c142 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -3707,7 +3707,7 @@ struct llama_model_loader {
         }
 
         if (param_overrides_p != nullptr) {
-            for (const struct llama_model_kv_override *p = param_overrides_p; p->key[0] != 0; p++) {
+            for (const struct llama_model_kv_override * p = param_overrides_p; p->key[0] != 0; p++) {
                 kv_overrides.insert({std::string(p->key), *p});
             }
         }
@@ -3875,7 +3875,7 @@ struct llama_model_loader {
             ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
 
             {
-                const int kid = gguf_find_key(meta, "general.file_type");
+                const int kid = gguf_find_key(meta, "general.file_type"); // TODO: use LLM_KV
                 if (kid >= 0) {
                     ftype = (llama_ftype) gguf_get_val_u32(meta, kid);
                 }
@@ -5369,6 +5369,7 @@ static void llm_load_vocab(
             if (merges_keyidx == -1) {
                 throw std::runtime_error("cannot find tokenizer merges in model file\n");
             }
+
             const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
             for (int i = 0; i < n_merges; i++) {
                 const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
@@ -5407,16 +5408,6 @@ static void llm_load_vocab(
             vocab.special_cls_id  = -1;
             vocab.special_mask_id = -1;
 
-            const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
-            if (add_space_prefix_keyidx != -1) {
-                vocab.tokenizer_add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
-            } // The default value of add_space_prefix is true.
-
-            const int remove_extra_whitespaces_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS).c_str());
-            if (remove_extra_whitespaces_keyidx != -1) {
-                vocab.tokenizer_remove_extra_whitespaces = gguf_get_val_bool(ctx, remove_extra_whitespaces_keyidx);
-            } // The default value of remove_extra_whitespaces is false.
-
             const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
             if (precompiled_charsmap_keyidx != -1) {
                 size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
@@ -5553,10 +5544,8 @@ static void llm_load_vocab(
             vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
         }
 
-        const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
-        if (add_space_prefix_keyidx != -1) {
-            vocab.tokenizer_add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
-        }
+        ml.get_key(LLM_KV_TOKENIZER_ADD_PREFIX,      vocab.tokenizer_add_space_prefix,         false);
+        ml.get_key(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.tokenizer_remove_extra_whitespaces, false);
     }
 
     const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
@@ -18288,8 +18277,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
 
     // copy the KV pairs from the input file
     gguf_set_kv     (ctx_out, ml.meta);
-    gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
-    gguf_set_val_u32(ctx_out, "general.file_type", ftype);
+    gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
+    gguf_set_val_u32(ctx_out, "general.file_type", ftype); // TODO: use LLM_KV
+
     // Remove split metadata
     gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
     gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str());

From 566daa5a5b38018b2727950bbd280239adb981b6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ji=C5=99=C3=AD=20Podiv=C3=ADn?=
 <66251151+jpodivin@users.noreply.github.com>
Date: Mon, 22 Jul 2024 15:44:53 +0200
Subject: [PATCH 098/125] *.py: Stylistic adjustments for python (#8233)

* Superflous parens in conditionals were removed.
* Unused args in function were removed.
* Replaced unused `idx` var with `_`
* Initializing file_format and format_version attributes
* Renaming constant to capitals
* Preventing redefinition of the `f` var

Signed-off-by: Jiri Podivin <jpodivin@redhat.com>
---
 convert_hf_to_gguf.py         | 12 ++++++------
 convert_hf_to_gguf_update.py  | 12 ++++++------
 convert_llama_ggml_to_gguf.py |  6 +++++-
 3 files changed, 17 insertions(+), 13 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 59410d6ce00ddb..f9e89d8e5219f0 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -737,7 +737,7 @@ def _create_vocab_sentencepiece(self):
                 added_tokens_json = json.load(f)
                 for key in added_tokens_json:
                     token_id = added_tokens_json[key]
-                    if (token_id >= vocab_size):
+                    if token_id >= vocab_size:
                         logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                         continue
 
@@ -2005,7 +2005,7 @@ def set_vocab(self):
 
                 for key in added_tokens_json:
                     token_id = added_tokens_json[key]
-                    if (token_id >= vocab_size):
+                    if token_id >= vocab_size:
                         logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                         continue
 
@@ -2081,7 +2081,7 @@ def set_gguf_parameters(self):
 
         # write rope scaling for long context (128k) model
         rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if (rope_scaling is None):
+        if rope_scaling is None:
             return
 
         scale = max_pos_embds / orig_max_pos_embds
@@ -2728,7 +2728,7 @@ def get_tensors(self):
 
             yield name, data
 
-    def set_vocab(self, *args, **kwargs):
+    def set_vocab(self):
         tokenizer_class = 'BertTokenizer'
         with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
             tokenizer_class = json.load(f)['tokenizer_class']
@@ -2876,7 +2876,7 @@ def set_vocab(self):
                     added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"]
                     for token_id, token_json in added_tokens_decoder.items():
                         token_id = int(token_id)
-                        if (token_id >= vocab_size):
+                        if token_id >= vocab_size:
                             logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                             continue
 
@@ -3125,7 +3125,7 @@ def set_vocab(self):
                 added_tokens_json = json.load(f)
                 for key in added_tokens_json:
                     token_id = added_tokens_json[key]
-                    if (token_id >= vocab_size):
+                    if token_id >= vocab_size:
                         logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
                         continue
 
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 29942333704ee8..5e58c8409fa035 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -50,7 +50,7 @@ class TOKENIZER_TYPE(IntEnum):
 
 # TODO: this string has to exercise as much pre-tokenizer functionality as possible
 #       will be updated with time - contributions welcome
-chktxt = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
+CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
 
 if len(sys.argv) == 2:
     token = sys.argv[1]
@@ -100,8 +100,8 @@ def download_file_with_auth(url, token, save_path):
     response = sess.get(url, headers=headers)
     response.raise_for_status()
     os.makedirs(os.path.dirname(save_path), exist_ok=True)
-    with open(save_path, 'wb') as f:
-        f.write(response.content)
+    with open(save_path, 'wb') as downloaded_file:
+        downloaded_file.write(response.content)
     logger.info(f"File {save_path} downloaded successfully")
 
 
@@ -160,7 +160,7 @@ def download_model(model):
         logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
         continue  # Skip to the next model if the tokenizer can't be loaded
 
-    chktok = tokenizer.encode(chktxt)
+    chktok = tokenizer.encode(CHK_TXT)
     chkhsh = sha256(str(chktok).encode()).hexdigest()
 
     logger.info(f"model: {name}")
@@ -192,7 +192,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
         # use in llama.cpp to implement the same pre-tokenizer
 
-        chktxt = {repr(chktxt)}
+        chktxt = {repr(CHK_TXT)}
 
         chktok = tokenizer.encode(chktxt)
         chkhsh = sha256(str(chktok).encode()).hexdigest()
@@ -288,7 +288,7 @@ def get_vocab_base_pre(self, tokenizer) -> str:
     "333333333",
     "Cửa Việt", # llama-bpe fails on this
     " discards",
-    chktxt,
+    CHK_TXT,
 ]
 
 # write the tests to ./models/ggml-vocab-{name}.gguf.inp
diff --git a/convert_llama_ggml_to_gguf.py b/convert_llama_ggml_to_gguf.py
index 95ea831a50a9c0..7b00b4398178b6 100755
--- a/convert_llama_ggml_to_gguf.py
+++ b/convert_llama_ggml_to_gguf.py
@@ -132,6 +132,10 @@ def load(self, data, offset):
 
 
 class GGMLModel:
+
+    file_format: GGMLFormat
+    format_version: int
+
     def __init__(self):
         self.hyperparameters = None
         self.vocab = None
@@ -290,7 +294,7 @@ def add_vocab(self, gguf_writer):
         if self.vocab_override is not None:
             vo = self.vocab_override
             logger.info('* Adding vocab item(s)')
-            for (idx, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
+            for (_, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
                 tokens.append(vbytes)
                 scores.append(score)
                 toktypes.append(ttype)

From d94c6e0ccbd29ee1ba4f44e9caa8682ad94df9fa Mon Sep 17 00:00:00 2001
From: Jason Stillerman <jason.t.stillerman@gmail.com>
Date: Mon, 22 Jul 2024 10:43:01 -0400
Subject: [PATCH 099/125] llama : add support for SmolLm pre-tokenizer (#8609)

* Adding SmolLM Pre Tokenizer

* Update convert_hf_to_gguf_update.py

Co-authored-by: compilade <git@compilade.net>

* Update src/llama.cpp

Co-authored-by: compilade <git@compilade.net>

* handle regex

* removed .inp and out .out ggufs

---------

Co-authored-by: compilade <git@compilade.net>
---
 convert_hf_to_gguf.py        | 3 +++
 convert_hf_to_gguf_update.py | 1 +
 include/llama.h              | 1 +
 src/llama.cpp                | 5 +++++
 4 files changed, 10 insertions(+)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index f9e89d8e5219f0..5912ecad3b8565 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -597,6 +597,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "63b97e4253352e6f357cc59ea5b583e3a680eaeaf2632188c2b952de2588485e":
             # ref: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407
             res = "tekken"
+        if chkhsh == "855059429035d75a914d1eda9f10a876752e281a054a7a3d421ef0533e5b6249":
+            # ref: https://huggingface.co/HuggingFaceTB/SmolLM-135M
+            res = "smollm"
 
         if res is None:
             logger.warning("\n")
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 5e58c8409fa035..535433bae5f2b0 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -92,6 +92,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "jais",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
     {"name": "t5",             "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
     {"name": "tekken",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
+    {"name": "smollm",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
 ]
 
 
diff --git a/include/llama.h b/include/llama.h
index 93fd77ca6b03d4..a435956149a003 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -93,6 +93,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_VIKING         = 18,
         LLAMA_VOCAB_PRE_TYPE_JAIS           = 19,
         LLAMA_VOCAB_PRE_TYPE_TEKKEN         = 20,
+        LLAMA_VOCAB_PRE_TYPE_SMOLLM         = 21,
     };
 
     // note: these values should be synchronized with ggml_rope
diff --git a/src/llama.cpp b/src/llama.cpp
index 4f3789b2a8c142..8fe51971c92836 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -5521,6 +5521,10 @@ static void llm_load_vocab(
                 vocab.tokenizer_clean_spaces = false;
                 vocab.tokenizer_ignore_merges = true;
                 vocab.tokenizer_add_bos = true;
+            } else if (
+                tokenizer_pre == "smollm") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMOLLM;
+                vocab.tokenizer_clean_spaces = false;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -15543,6 +15547,7 @@ struct llm_tokenizer_bpe {
             case LLAMA_VOCAB_PRE_TYPE_STARCODER:
             case LLAMA_VOCAB_PRE_TYPE_REFACT:
             case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
+            case LLAMA_VOCAB_PRE_TYPE_SMOLLM:
                 regex_exprs = {
                     "\\p{N}",
                     "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",

From 081fe431aa8fb6307145c4feb3eed4f48cab19f8 Mon Sep 17 00:00:00 2001
From: Keke Han <hankeke303@163.com>
Date: Tue, 23 Jul 2024 00:43:43 +0800
Subject: [PATCH 100/125] llama : fix codeshell support (#8599)

* llama : fix codeshell support

* llama : move codeshell after smollm below to respect the enum order
---
 convert_hf_to_gguf.py        | 3 +++
 convert_hf_to_gguf_update.py | 1 +
 include/llama.h              | 1 +
 src/llama.cpp                | 8 ++++++--
 4 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 5912ecad3b8565..dde4fa9c80ca3a 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -594,6 +594,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901":
             # ref: https://huggingface.co/core42/jais-13b
             res = "jais"
+        if chkhsh == "7b3e7548e4308f52a76e8229e4e6cc831195d0d1df43aed21ac6c93da05fec5f":
+            # ref: https://huggingface.co/WisdomShell/CodeShell-7B
+            res = "codeshell"
         if chkhsh == "63b97e4253352e6f357cc59ea5b583e3a680eaeaf2632188c2b952de2588485e":
             # ref: https://huggingface.co/mistralai/Mistral-Nemo-Base-2407
             res = "tekken"
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 535433bae5f2b0..d5a2d925eaef55 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -91,6 +91,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "gemma-2",        "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
     {"name": "jais",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
     {"name": "t5",             "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
+    {"name": "codeshell",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", },
     {"name": "tekken",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
     {"name": "smollm",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
 ]
diff --git a/include/llama.h b/include/llama.h
index a435956149a003..bf27614679cb09 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -94,6 +94,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_JAIS           = 19,
         LLAMA_VOCAB_PRE_TYPE_TEKKEN         = 20,
         LLAMA_VOCAB_PRE_TYPE_SMOLLM         = 21,
+        LLAMA_VOCAB_PRE_TYPE_CODESHELL      = 22,
     };
 
     // note: these values should be synchronized with ggml_rope
diff --git a/src/llama.cpp b/src/llama.cpp
index 8fe51971c92836..99a6d8b6651686 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -5007,7 +5007,7 @@ static void llm_load_hparams(
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 switch (hparams.n_layer) {
-                    case 42: model.type = e_model::MODEL_SMALL; break;
+                    case 42: model.type = e_model::MODEL_7B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
@@ -5525,6 +5525,9 @@ static void llm_load_vocab(
                 tokenizer_pre == "smollm") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMOLLM;
                 vocab.tokenizer_clean_spaces = false;
+            } else if (
+                tokenizer_pre == "codeshell") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CODESHELL;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -15548,6 +15551,7 @@ struct llm_tokenizer_bpe {
             case LLAMA_VOCAB_PRE_TYPE_REFACT:
             case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
             case LLAMA_VOCAB_PRE_TYPE_SMOLLM:
+            case LLAMA_VOCAB_PRE_TYPE_CODESHELL:
                 regex_exprs = {
                     "\\p{N}",
                     "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
@@ -19447,7 +19451,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_BAICHUAN:
         case LLM_ARCH_STARCODER:
         case LLM_ARCH_PLAMO:
-        case LLM_ARCH_CODESHELL:
         case LLM_ARCH_ORION:
         case LLM_ARCH_INTERNLM2:
         case LLM_ARCH_MINICPM:
@@ -19477,6 +19480,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_STARCODER2:
         case LLM_ARCH_OPENELM:
         case LLM_ARCH_GPTNEOX:
+        case LLM_ARCH_CODESHELL:
             return LLAMA_ROPE_TYPE_NEOX;
 
         // all model arches should be listed explicitly here

From 063d99ad11f1295046610ce5b97e105849a4b573 Mon Sep 17 00:00:00 2001
From: luoyu-intel <yu.luo@intel.com>
Date: Tue, 23 Jul 2024 07:43:28 +0000
Subject: [PATCH 101/125] [SYCL] fix scratch size of softmax (#8642)

---
 ggml/src/ggml-sycl/softmax.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp
index c5d9a837eb7941..17a542e4903627 100644
--- a/ggml/src/ggml-sycl/softmax.cpp
+++ b/ggml/src/ggml-sycl/softmax.cpp
@@ -152,7 +152,8 @@ static void soft_max_f32_sycl(const float * x, const float * mask,
 
     const sycl::range<3> block_dims(1, 1, nth);
     const sycl::range<3> block_nums(1, 1, nrows_x);
-    const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE);
+    const size_t n_val_tmp = nth / WARP_SIZE;
+    const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + n_val_tmp);
 
     const uint32_t n_head_kv   = nrows_x/nrows_y;
     const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));

From e7e6487ba06634edf58dfdf9673bad9df41b445a Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 23 Jul 2024 11:28:38 +0300
Subject: [PATCH 102/125] contrib : clarify PR squashing + module names (#8630)

* contrib : clarify PR squashing

* contrib : fix typo + add list of modules
---
 CONTRIBUTING.md | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 48f9914af8819c..b688f78ecf195a 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,12 +1,17 @@
-# Pull requests
+# Pull requests (for contributors)
 
-- Always squash-merge the PR before merging
-- Use the following format for your final commit: `<module> : <commit title> (#<issue_number>)`. For example: `utils : fix typo in utils.py (#1234)`
 - Test your changes:
   - Using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the GGML library
   - Execute [the full CI locally on your machine](ci/README.md) before publishing
 - Please rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs.
-  - The PR template has a series of review complexity checkboxes `[ ]` that [you can mark as](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/about-task-lists) `[X]` for your conveience
+  - The PR template has a series of review complexity checkboxes `[ ]` that [you can mark as](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/about-task-lists) `[X]` for your convenience
+- If your PR becomes stale, don't hesitate to ping the maintainers in the comments
+
+# Pull requests (for collaborators)
+
+- Squash-merge PRs
+- Use the following format for the squashed commit title: `<module> : <commit title> (#<issue_number>)`. For example: `utils : fix typo in utils.py (#1234)`
+- Optionally, pick a `<module>` from here: https://github.com/ggerganov/llama.cpp/wiki/Modules
 
 # Coding guidelines
 

From 46e47417aa4f18c08738afd4d9a3e838e97ca03f Mon Sep 17 00:00:00 2001
From: Jeroen Mostert <jeroen.mostert@cm.com>
Date: Tue, 23 Jul 2024 10:50:40 +0200
Subject: [PATCH 103/125] Allow all RDNA2 archs to use sdot4 intrinsic (#8629)

The check gating the use of `__builtin_amdgc_sdot4` specifically checks for gfx1030. This causes a severe perf regression for anything gfx103? that's not gfx1030 and not using `HSA_OVERRIDE_GFX_VERSION` (if you've built ROCm to support it). We already have a generic RDNA2 define, let's use it.
---
 ggml/src/ggml-cuda/common.cuh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 26d9412a23eb6f..1c2e00c1ee42e5 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -459,7 +459,7 @@ static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half
 
 static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) {
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
+#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(RDNA2)
     c = __builtin_amdgcn_sdot4(a, b, c, false);
 #elif defined(RDNA3)
     c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);

From 751fcfc6c33ea5f43cadd4d976f8fb176871df5e Mon Sep 17 00:00:00 2001
From: 0cc4m <picard12@live.de>
Date: Tue, 23 Jul 2024 10:56:49 +0200
Subject: [PATCH 104/125] Vulkan IQ4_NL Support (#8613)

* Fix Vulkan matmul tests compile errors

* Add Vulkan IQ4_NL support

* Fix Vulkan DeepSeek-Coder-V2-Lite MoE support
---
 ggml/src/ggml-vulkan.cpp                      | 322 ++++++++----------
 ggml/src/vulkan-shaders/dequant_funcs.comp    |   8 +
 ggml/src/vulkan-shaders/dequant_iq4_nl.comp   |  30 ++
 ggml/src/vulkan-shaders/dequant_q4_0.comp     |  10 +-
 ggml/src/vulkan-shaders/mul_mm.comp           |  15 +-
 ggml/src/vulkan-shaders/types.comp            |  21 ++
 .../src/vulkan-shaders/vulkan-shaders-gen.cpp |   3 +-
 7 files changed, 219 insertions(+), 190 deletions(-)
 create mode 100644 ggml/src/vulkan-shaders/dequant_iq4_nl.comp

diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp
index 8efe32329693ee..6bcd81a7b6cf12 100644
--- a/ggml/src/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan.cpp
@@ -38,8 +38,6 @@
 #define VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI 1
 #define VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE 2
 
-#define VK_NUM_TYPES 16
-
 #define GGML_VK_MAX_NODES 8192
 
 #define MAX_VK_BUFFERS 256
@@ -162,23 +160,23 @@ struct vk_device_struct {
     vk_matmul_pipeline pipeline_matmul_f16_f32;
     vk_pipeline pipeline_matmul_split_k_reduce;
 
-    vk_matmul_pipeline pipeline_dequant_mul_mat_mat[VK_NUM_TYPES];
+    vk_matmul_pipeline pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT];
 
     vk_matmul_pipeline pipeline_matmul_id_f32;
     vk_matmul_pipeline pipeline_matmul_id_f16;
     vk_matmul_pipeline pipeline_matmul_id_f16_f32;
 
-    vk_matmul_pipeline pipeline_dequant_mul_mat_mat_id[VK_NUM_TYPES];
+    vk_matmul_pipeline pipeline_dequant_mul_mat_mat_id[GGML_TYPE_COUNT];
 
-    vk_pipeline pipeline_dequant[VK_NUM_TYPES];
-    vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[VK_NUM_TYPES];
-    vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[VK_NUM_TYPES];
-    vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[VK_NUM_TYPES];
+    vk_pipeline pipeline_dequant[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_COUNT];
 
     vk_pipeline pipeline_mul_mat_vec_p021_f16_f32;
     vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
-    vk_pipeline pipeline_get_rows[VK_NUM_TYPES];
-    vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES];
+    vk_pipeline pipeline_get_rows[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_get_rows_f32[GGML_TYPE_COUNT];
     vk_pipeline pipeline_mul_f32;
     vk_pipeline pipeline_div_f32;
     vk_pipeline pipeline_add_f32;
@@ -1059,25 +1057,6 @@ static void ggml_vk_wait_events(vk_context * ctx, std::vector<vk::Event>&& event
     );
 }
 
-static bool ggml_vk_build_shader(ggml_type type) {
-    switch(type) {
-    case GGML_TYPE_F16:
-    case GGML_TYPE_Q4_0:
-    case GGML_TYPE_Q4_1:
-    case GGML_TYPE_Q5_0:
-    case GGML_TYPE_Q5_1:
-    case GGML_TYPE_Q8_0:
-    case GGML_TYPE_Q2_K:
-    case GGML_TYPE_Q3_K:
-    case GGML_TYPE_Q4_K:
-    case GGML_TYPE_Q5_K:
-    case GGML_TYPE_Q6_K:
-        return true;
-    default:
-        return false;
-    }
-}
-
 static void ggml_vk_load_shaders(vk_device& device) {
     VK_LOG_DEBUG("ggml_vk_load_shaders(" << device->name << ")");
 
@@ -1112,6 +1091,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K] = std::make_shared<vk_matmul_pipeline_struct>();
     device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>();
     device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL] = std::make_shared<vk_matmul_pipeline_struct>();
 
     device->pipeline_matmul_id_f32 = std::make_shared<vk_matmul_pipeline_struct>();
     device->pipeline_matmul_id_f16_f32 = std::make_shared<vk_matmul_pipeline_struct>();
@@ -1126,6 +1106,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K] = std::make_shared<vk_matmul_pipeline_struct>();
     device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>();
     device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();
+    device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL] = std::make_shared<vk_matmul_pipeline_struct>();
 
     if (device->fp16) {
         ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
@@ -1226,6 +1207,13 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
 
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->l, "matmul_iq4_nl_f32_l", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->m, "matmul_iq4_nl_f32_m", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->s, "matmul_iq4_nl_f32_s", matmul_iq4_nl_f32_len, matmul_iq4_nl_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_l, "matmul_iq4_nl_f32_aligned_l", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_m, "matmul_iq4_nl_f32_aligned_m", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_s, "matmul_iq4_nl_f32_aligned_s", matmul_iq4_nl_f32_aligned_len, matmul_iq4_nl_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
         ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1);
         ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1);
         ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1);
@@ -1316,6 +1304,13 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->l, "matmul_id_iq4_nl_f32_l", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->m, "matmul_id_iq4_nl_f32_m", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->s, "matmul_id_iq4_nl_f32_s", matmul_id_iq4_nl_f32_len, matmul_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_l, "matmul_id_iq4_nl_f32_aligned_l", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_m, "matmul_id_iq4_nl_f32_aligned_m", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_s, "matmul_id_iq4_nl_f32_aligned_s", matmul_id_iq4_nl_f32_aligned_len, matmul_id_iq4_nl_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
     } else {
         ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1);
         ggml_vk_create_pipeline(device, device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1);
@@ -1415,6 +1410,13 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
 
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->l, "matmul_iq4_nl_f32_l", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->m, "matmul_iq4_nl_f32_m", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->s, "matmul_iq4_nl_f32_s", matmul_iq4_nl_f32_fp32_len, matmul_iq4_nl_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_l, "matmul_iq4_nl_f32_aligned_l", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_m, "matmul_iq4_nl_f32_aligned_m", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL]->a_s, "matmul_iq4_nl_f32_aligned_s", matmul_iq4_nl_f32_aligned_fp32_len, matmul_iq4_nl_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
         ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1);
         ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1);
         ggml_vk_create_pipeline(device, device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1);
@@ -1505,6 +1507,13 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
         ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->l, "matmul_id_iq4_nl_f32_l", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->m, "matmul_id_iq4_nl_f32_m", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->s, "matmul_id_iq4_nl_f32_s", matmul_id_iq4_nl_f32_fp32_len, matmul_id_iq4_nl_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_l, "matmul_id_iq4_nl_f32_aligned_l", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_m, "matmul_id_iq4_nl_f32_aligned_m", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL]->a_s, "matmul_id_iq4_nl_f32_aligned_s", matmul_id_iq4_nl_f32_aligned_fp32_len, matmul_id_iq4_nl_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align);
     }
 
     // mul mat vec
@@ -1520,6 +1529,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32",  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32",  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
@@ -1533,6 +1543,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
@@ -1546,6 +1557,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
     // dequant shaders
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
@@ -1559,6 +1571,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_k", dequant_q4_k_len, dequant_q4_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_k", dequant_q5_k_len, dequant_q5_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_k", dequant_q6_k_len, dequant_q6_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL], "dequant_iq4_nl", dequant_iq4_nl_len, dequant_iq4_nl_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
 
     // get_rows
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32",  get_rows_f32_len,  get_rows_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -1568,6 +1581,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl", get_rows_iq4_nl_len, get_rows_iq4_nl_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -1576,6 +1590,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl_f32", get_rows_iq4_nl_f32_len, get_rows_iq4_nl_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1);
 
@@ -2087,6 +2102,7 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
             break;
         default:
             return nullptr;
@@ -2123,6 +2139,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
             break;
         default:
             return nullptr;
@@ -2148,6 +2165,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
             break;
         default:
             return nullptr;
@@ -2181,6 +2199,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
             break;
         default:
             return nullptr;
@@ -2206,6 +2225,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context
         case GGML_TYPE_Q4_K:
         case GGML_TYPE_Q5_K:
         case GGML_TYPE_Q6_K:
+        case GGML_TYPE_IQ4_NL:
             break;
         default:
             return nullptr;
@@ -3431,7 +3451,7 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context *
 
     const uint64_t nei0 = ids->ne[0];
     const uint64_t nei1 = ids->ne[1];
-    GGML_ASSERT(nei0 * nei1 <= 2048);
+    GGML_ASSERT(nei0 * nei1 <= 3072);
 
     const uint32_t nbi1 = ids->nb[1];
     const uint32_t nbi2 = ids->nb[2];
@@ -3443,8 +3463,6 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context *
 
     const uint64_t n_as = ne02;
 
-    GGML_ASSERT(n_as <= 8);
-
     ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
     ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
     ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra;
@@ -4623,22 +4641,22 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
         }
     }
 
-    ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it);
+    ggml_pipeline_allocate_descriptor_sets(ctx->device, p, num_it);
     if (split_k > 1) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
+        ggml_pipeline_allocate_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it);
 
         if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
             // Resize buffer
             if (ctx->prealloc_split_k != nullptr) {
                 ggml_vk_destroy_buffer(ctx->prealloc_split_k);
             }
-            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
+            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
         }
     }
 
-    vk_buffer d_X = ggml_vk_create_buffer_check(ctx, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer d_Y = ggml_vk_create_buffer_check(ctx, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer d_D = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_X = ggml_vk_create_buffer_check(ctx->device, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_Y = ggml_vk_create_buffer_check(ctx->device, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_D = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
 
     X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne);
     Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne);
@@ -4665,12 +4683,12 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
         }
     }
 
-    ggml_vk_buffer_write(ctx, d_X, 0, x, sizeof(X_TYPE) * k * m * batch);
-    ggml_vk_buffer_write(ctx, d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch);
+    ggml_vk_buffer_write(d_X, 0, x, sizeof(X_TYPE) * k * m * batch);
+    ggml_vk_buffer_write(d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch);
 
     vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
     for (size_t i = 0; i < num_it; i++) {
-        ggml_vk_ctx_begin(ctx, subctx);
+        ggml_vk_ctx_begin(ctx->device, subctx);
         ggml_vk_matmul(
             ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k),
             m, n, k,
@@ -4689,7 +4707,7 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
     double time = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
 
     // copy dst to host
-    ggml_vk_buffer_read(ctx, d_D, 0, d, sizeof(float) * d_ne);
+    ggml_vk_buffer_read(d_D, 0, d, sizeof(float) * d_ne);
 
     float * d_chk = (float *) malloc(sizeof(float) * d_ne);
 
@@ -4765,7 +4783,7 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
 
         if (split_k > 1) {
             float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
-            ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
+            ggml_vk_buffer_read(ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
 
             std::cerr << "d_buf0: " << std::endl << std::endl;
             ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
@@ -4785,8 +4803,8 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
 
     free(d_chk);
 
-    ggml_vk_queue_cleanup(ctx, ctx->device->transfer_queue);
-    ggml_vk_queue_cleanup(ctx, ctx->device->compute_queue);
+    ggml_vk_queue_cleanup(ctx->device, ctx->device->transfer_queue);
+    ggml_vk_queue_cleanup(ctx->device, ctx->device->compute_queue);
 
     ggml_vk_destroy_buffer(d_X);
     ggml_vk_destroy_buffer(d_Y);
@@ -4834,88 +4852,21 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1
     }
 }
 
-static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool pinned) {
-    VK_LOG_DEBUG("ggml_vk_test_transfer(" << ne << ")");
-    // Check transfers are correct
-    vk_buffer buffer = ggml_vk_create_buffer_check(ctx, sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal);
-
-    float * x;
-    float * y;
-    if (pinned) {
-        x = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne);
-        y = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne);
-    } else {
-        x = (float *) malloc(sizeof(float) * ne);
-        y = (float *) malloc(sizeof(float) * ne);
-    }
-
-    for (size_t i = 0; i < ne; i++) {
-        x[i] = rand() / (float)RAND_MAX;
-    }
-
-    vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
-    ggml_vk_ctx_begin(ctx, subctx);
-
-    auto begin = std::chrono::high_resolution_clock::now();
-
-    ggml_vk_buffer_write_async(ctx, subctx, buffer, 0, x, sizeof(float) * ne);
-
-    for (auto& cpy : subctx->in_memcpys) {
-        memcpy(cpy.dst, cpy.src, cpy.n);
-    }
-    subctx->in_memcpys.clear();
-
-    ggml_vk_ctx_end(subctx);
-    ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
-    ctx->device->device.resetFences({ ctx->fence });
-
-    auto end = std::chrono::high_resolution_clock::now();
-
-    double ms_to_gpu = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
-
-    ggml_vk_ctx_begin(ctx, subctx);
-
-    begin = std::chrono::high_resolution_clock::now();
-
-    ggml_vk_buffer_read_async(ctx, subctx, buffer, 0, y, sizeof(float) * ne);
-
-    ggml_vk_ctx_end(subctx);
-    ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences");
-    ctx->device->device.resetFences({ ctx->fence });
-
-    for (auto& cpy : subctx->out_memcpys) {
-        memcpy(cpy.dst, cpy.src, cpy.n);
-    }
-    subctx->out_memcpys.clear();
-
-    end = std::chrono::high_resolution_clock::now();
-
-    double ms_from_gpu = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
+static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) {
+    ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr);
+}
 
-    double avg_err = 0.0;
-    for (size_t i = 0; i < ne; i++) {
-        avg_err += std::fabs(x[i] - y[i]);
+static void ggml_vk_dequantize_data(const void * from, float * to, size_t ne, ggml_type quant) {
+    if (quant == GGML_TYPE_F32) {
+        memcpy(to, from, sizeof(float) * ne);
+        return;
     }
 
-    double kb = ne * sizeof(float) / 1024.0;
-
-    std::cerr << "TEST TRANSFER " << kb << " KB to_gpu " << ms_to_gpu << "ms (" << kb / ms_to_gpu * 1000.0 / 1024.0 << " MB/s) from_gpu " << ms_from_gpu << "ms (" << kb / ms_from_gpu * 1000.0 / 1024.0 << " MB/s) avg_err=" << avg_err / ne << std::endl;
-
-    ggml_vk_destroy_buffer(buffer);
+    ggml_type_traits_t tt = ggml_internal_get_type_traits(quant);
 
-    if (pinned) {
-        ggml_vk_host_free(ctx, x);
-        ggml_vk_host_free(ctx, y);
-    } else {
-        free(x);
-        free(y);
-    }
-}
+    ggml_to_float_t dequant_fn = tt.to_float;
 
-static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) {
-    ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr);
+    dequant_fn(from, to, ne);
 }
 
 static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) {
@@ -4925,24 +4876,26 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
     const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant);
     float * x = (float *) malloc(x_sz);
     void * qx = malloc(qx_sz);
-    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer x_buf = ggml_vk_create_buffer_check(ctx, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    float * x_ref = (float *) malloc(x_sz);
     ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16);
 
     for (size_t i = 0; i < ne; i++) {
         x[i] = rand() / (float)RAND_MAX;
     }
 
-    vk_pipeline p = ctx->device->pipeline_dequant[quant];
+    vk_pipeline p = ggml_vk_get_to_fp16(ctx, quant);
 
     ggml_vk_quantize_data(x, qx, ne, quant);
+    ggml_vk_dequantize_data(qx, x_ref, ne, quant);
 
-    ggml_pipeline_allocate_descriptor_sets(ctx, p, 1);
+    ggml_pipeline_allocate_descriptor_sets(ctx->device, p, 1);
 
-    ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz);
+    ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz);
 
     vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
-    ggml_vk_ctx_begin(ctx, subctx);
+    ggml_vk_ctx_begin(ctx->device, subctx);
     const std::vector<uint32_t> pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne };
     ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1});
     ggml_vk_ctx_end(subctx);
@@ -4956,13 +4909,13 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
     auto end = std::chrono::high_resolution_clock::now();
 
     double ms_dequant = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
-    ggml_vk_buffer_read(ctx, x_buf, 0, x_chk, x_sz_f16);
+    ggml_vk_buffer_read(x_buf, 0, x_chk, x_sz_f16);
 
     int first_err = -1;
 
     double avg_err = 0.0;
     for (size_t i = 0; i < ne; i++) {
-        double error = std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i]));
+        double error = std::fabs(x_ref[i] - ggml_fp16_to_fp32(x_chk[i]));
         avg_err += error;
 
         if (first_err < 0 && error > 0.05) {
@@ -4982,7 +4935,7 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
         }
         std::cerr << std::endl << "Expected result: " << std::endl << std::endl;
         for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) {
-            std::cerr << x[i] << ", ";
+            std::cerr << x_ref[i] << ", ";
         }
         std::cerr << std::endl;
     }
@@ -4992,6 +4945,7 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
 
     free(x);
     free(qx);
+    free(x_ref);
     free(x_chk);
 }
 
@@ -5040,9 +4994,9 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
     float * x = (float *) malloc(x_sz);
     float * y = (float *) malloc(y_sz);
     void * qx = malloc(qx_sz);
-    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer y_buf = ggml_vk_create_buffer_check(ctx, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
-    vk_buffer d_buf = ggml_vk_create_buffer_check(ctx, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer y_buf = ggml_vk_create_buffer_check(ctx->device, y_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
+    vk_buffer d_buf = ggml_vk_create_buffer_check(ctx->device, d_sz, vk::MemoryPropertyFlagBits::eDeviceLocal);
     float * d = (float *) malloc(d_sz);
     float * d_chk = (float *) malloc(d_sz);
 
@@ -5057,25 +5011,25 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
         y[i] = (i % k == i / k) ? 1.0f : 0.0f;
     }
 
-    ggml_pipeline_allocate_descriptor_sets(ctx, p, num_it);
+    ggml_pipeline_allocate_descriptor_sets(ctx->device, p, num_it);
     if (split_k > 1) {
-        ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it);
+        ggml_pipeline_allocate_descriptor_sets(ctx->device, ctx->device->pipeline_matmul_split_k_reduce, num_it);
 
         if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) {
             // Resize buffer
             if (ctx->prealloc_split_k != nullptr) {
                 ggml_vk_destroy_buffer(ctx->prealloc_split_k);
             }
-            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
+            ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal);
         }
     }
 
-    ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz);
-    ggml_vk_buffer_write(ctx, y_buf, 0, y, y_sz);
+    ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz);
+    ggml_vk_buffer_write(y_buf, 0, y, y_sz);
 
     vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
     for (size_t i = 0; i < num_it; i++) {
-        ggml_vk_ctx_begin(ctx, subctx);
+        ggml_vk_ctx_begin(ctx->device, subctx);
         ggml_vk_matmul(
             ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k),
             m, n, k,
@@ -5094,7 +5048,7 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
     auto end = std::chrono::high_resolution_clock::now();
 
     double time_ms = std::chrono::duration_cast<std::chrono::microseconds>(end-begin).count() / 1000.0;
-    ggml_vk_buffer_read(ctx, d_buf, 0, d, d_sz);
+    ggml_vk_buffer_read(d_buf, 0, d, d_sz);
 
     ggml_init_params iparams = {
         /*.mem_size   =*/ 1024*1024*1024,
@@ -5149,7 +5103,7 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
 
         if (split_k > 1) {
             float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k);
-            ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
+            ggml_vk_buffer_read(ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k);
 
             std::cerr << "d_buf0: " << std::endl << std::endl;
             ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b);
@@ -5302,12 +5256,9 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm
 
 static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
 #if defined(GGML_VULKAN_RUN_TESTS)
-    ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul,
+    ctx->staging = ggml_vk_create_buffer_check(ctx->device, 100ul * 1024ul * 1024ul,
         vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
         vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
-    ggml_vk_test_transfer(ctx, 8192 * 1000, false);
-    ggml_vk_test_transfer(ctx, 8192 * 1000, true);
-
     ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_F32);
     ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_0);
     ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_1);
@@ -5319,85 +5270,90 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
     ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q4_K);
     ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_K);
     ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q6_K);
+    ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_IQ4_NL);
 
     ggml_vk_test_matmul<ggml_fp16_t, ggml_fp16_t>(ctx, 512, 512, 100, 32, 100, 1, 2);
 
     ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 0);
     ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 1);
     ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 2);
-    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 0);
-    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 1);
-    ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 2);
+    // ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 0);
+    // ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 1);
+    // ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 4, 2);
 
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_0);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_0);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_0);
 
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_1);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_1);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_1);
 
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_0);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_0);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_0);
 
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_1);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_1);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_1);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_1);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_1);
 
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q8_0);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q8_0);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q8_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q8_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q8_0);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q8_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q8_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q8_0);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q8_0);
 
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q2_K);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q2_K);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q2_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q2_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q2_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q2_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q2_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q2_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q2_K);
 
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q3_K);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q3_K);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q3_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q3_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q3_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q3_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q3_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q3_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q3_K);
 
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q4_K);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q4_K);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q4_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q4_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q4_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q4_K);
 
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q5_K);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q5_K);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q5_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q5_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q5_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q5_K);
 
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_Q6_K);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_Q6_K);
     ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_Q6_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q6_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q6_K);
-    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q6_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 0, GGML_TYPE_Q6_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 1, GGML_TYPE_Q6_K);
+    // ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 4, 2, GGML_TYPE_Q6_K);
+
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 0, GGML_TYPE_IQ4_NL);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 1, GGML_TYPE_IQ4_NL);
+    ggml_vk_test_dequant_matmul(ctx, 128, 512, 512, 2, 100, 1, 2, GGML_TYPE_IQ4_NL);
 
     std::cerr << std::endl;
 
@@ -5429,9 +5385,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
         ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0);
         ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1);
         ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2);
-        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0);
-        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1);
-        ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2);
+        // ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0);
+        // ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1);
+        // ggml_vk_test_matmul<ggml_fp16_t, float>(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2);
         std::cerr << std::endl;
     }
 
@@ -6263,6 +6219,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
                     case GGML_TYPE_Q4_K:
                     case GGML_TYPE_Q5_K:
                     case GGML_TYPE_Q6_K:
+                    case GGML_TYPE_IQ4_NL:
                         break;
                     default:
                         return false;
@@ -6291,6 +6248,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
                     case GGML_TYPE_Q5_0:
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_IQ4_NL:
                         return true;
                     default:
                         return false;
diff --git a/ggml/src/vulkan-shaders/dequant_funcs.comp b/ggml/src/vulkan-shaders/dequant_funcs.comp
index 35d424d182f0cc..d5b989735bc0bd 100644
--- a/ggml/src/vulkan-shaders/dequant_funcs.comp
+++ b/ggml/src/vulkan-shaders/dequant_funcs.comp
@@ -58,3 +58,11 @@ vec2 dequantize(uint ib, uint iqs, uint a_offset) {
     return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d;
 }
 #endif
+
+#if defined(DATA_A_IQ4_NL)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d;
+}
+#endif
diff --git a/ggml/src/vulkan-shaders/dequant_iq4_nl.comp b/ggml/src/vulkan-shaders/dequant_iq4_nl.comp
new file mode 100644
index 00000000000000..34ef3da30b82ce
--- /dev/null
+++ b/ggml/src/vulkan-shaders/dequant_iq4_nl.comp
@@ -0,0 +1,30 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_iq4_nl data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint q_idx = 8*il;
+    const uint b_idx = 1024*i + 32*ir + q_idx;
+
+    const float d = float(data_a[ib].d);
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * kvalues_iq4nl[data_a[ib].qs[q_idx + l] & 0xF]);
+        data_b[b_idx + l + 16] = D_TYPE(d * kvalues_iq4nl[data_a[ib].qs[q_idx + l] >>  4]);
+    }
+}
diff --git a/ggml/src/vulkan-shaders/dequant_q4_0.comp b/ggml/src/vulkan-shaders/dequant_q4_0.comp
index 11e07e66bc9374..408185327255b4 100644
--- a/ggml/src/vulkan-shaders/dequant_q4_0.comp
+++ b/ggml/src/vulkan-shaders/dequant_q4_0.comp
@@ -18,15 +18,13 @@ void main() {
         return;
     }
 
-    const uint b_idx = 1024*i + 32*ir + 8*il;
+    const uint q_idx = 8*il;
+    const uint b_idx = 1024*i + 32*ir + q_idx;
 
     const float d = float(data_a[ib].d);
-    const float dm = -8.0f * d;
-
-    const uint q_idx = 8*il;
 
     [[unroll]] for (uint l = 0; l < 8; ++l) {
-        data_b[b_idx + l +  0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + dm);
-        data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >>  4) + dm);
+        data_b[b_idx + l +  0] = D_TYPE(d * ((data_a[ib].qs[q_idx + l] & 0xF) - 8.0f));
+        data_b[b_idx + l + 16] = D_TYPE(d * ((data_a[ib].qs[q_idx + l] >>  4) - 8.0f));
     }
 }
diff --git a/ggml/src/vulkan-shaders/mul_mm.comp b/ggml/src/vulkan-shaders/mul_mm.comp
index 7c2b45cce44c4b..5fe9d5241381e5 100644
--- a/ggml/src/vulkan-shaders/mul_mm.comp
+++ b/ggml/src/vulkan-shaders/mul_mm.comp
@@ -71,7 +71,7 @@ shared FLOAT_TYPE buf_a[BM * (BK+1)];
 shared FLOAT_TYPE buf_b[BN * (BK+1)];
 
 #ifdef MUL_MAT_ID
-shared u16vec2 row_ids[2048];
+shared u16vec2 row_ids[3072];
 #endif
 
 void main() {
@@ -380,6 +380,19 @@ void main() {
 
             buf_a[buf_idx    ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32));
             buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));
+#elif defined(DATA_A_IQ4_NL)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
 #endif
         }
         [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) {
diff --git a/ggml/src/vulkan-shaders/types.comp b/ggml/src/vulkan-shaders/types.comp
index 815fcbecde8ac8..d24c172cad3fca 100644
--- a/ggml/src/vulkan-shaders/types.comp
+++ b/ggml/src/vulkan-shaders/types.comp
@@ -177,3 +177,24 @@ struct block_q6_K
 
 #define A_TYPE block_q6_K
 #endif
+
+// IQuants
+
+#if defined(DATA_A_IQ4_NL)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_iq4_nl
+{
+    float16_t d;
+    uint8_t qs[QUANT_K/2];
+};
+
+#define A_TYPE block_iq4_nl
+
+const int8_t kvalues_iq4nl[16] = {
+    int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10),
+    int8_t(1), int8_t(13), int8_t(25), int8_t(38), int8_t(53), int8_t(69), int8_t(89), int8_t(113)
+};
+#endif
diff --git a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
index 3038d647f10f35..c5be3754bfed38 100644
--- a/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -52,7 +52,8 @@ const std::vector<std::string> type_names = {
     "q3_k",
     "q4_k",
     "q5_k",
-    "q6_k"
+    "q6_k",
+    "iq4_nl"
 };
 
 void execute_command(const std::string& command, std::string& stdout_str, std::string& stderr_str) {

From 938943cdbf4dd79005a394d732bc226f9e34e0ff Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 23 Jul 2024 13:10:17 +0300
Subject: [PATCH 105/125] llama : move vocab, grammar and sampling into
 separate files (#8508)

* llama : move sampling code into llama-sampling

ggml-ci

* llama : move grammar code into llama-grammar

ggml-ci

* cont

ggml-ci

* cont : pre-fetch rules

* cont

ggml-ci

* llama : deprecate llama_sample_grammar

* llama : move tokenizers into llama-vocab

ggml-ci

* make : update llama.cpp deps [no ci]

* llama : redirect external API to internal APIs

ggml-ci

* llama : suffix the internal APIs with "_impl"

ggml-ci

* llama : clean-up
---
 Makefile                                   |   32 +-
 Package.swift                              |    3 +
 common/sampling.cpp                        |    6 +-
 examples/gbnf-validator/gbnf-validator.cpp |   15 +-
 include/llama.h                            |   76 +-
 src/CMakeLists.txt                         |    3 +
 src/llama-grammar.cpp                      |  539 +++
 src/llama-grammar.h                        |   41 +
 src/llama-impl.h                           |   26 +
 src/llama-sampling.cpp                     |  635 ++++
 src/llama-sampling.h                       |   56 +
 src/llama-vocab.cpp                        | 1721 ++++++++++
 src/llama-vocab.h                          |  130 +
 src/llama.cpp                              | 3424 +++-----------------
 src/unicode.cpp                            |    6 +
 src/unicode.h                              |    3 +
 tests/test-grammar-integration.cpp         |   23 +-
 tests/test-llama-grammar.cpp               |   20 +-
 18 files changed, 3656 insertions(+), 3103 deletions(-)
 create mode 100644 src/llama-grammar.cpp
 create mode 100644 src/llama-grammar.h
 create mode 100644 src/llama-impl.h
 create mode 100644 src/llama-sampling.cpp
 create mode 100644 src/llama-sampling.h
 create mode 100644 src/llama-vocab.cpp
 create mode 100644 src/llama-vocab.h

diff --git a/Makefile b/Makefile
index 4584594af4174f..52b55dd89da8bf 100644
--- a/Makefile
+++ b/Makefile
@@ -876,6 +876,9 @@ OBJ_GGML += \
 
 OBJ_LLAMA = \
 	src/llama.o \
+	src/llama-vocab.o \
+	src/llama-grammar.o \
+	src/llama-sampling.o \
 	src/unicode.o \
 	src/unicode-data.o
 
@@ -1055,6 +1058,10 @@ src/unicode-data.o: \
 
 src/llama.o: \
 	src/llama.cpp \
+	src/llama-impl.h \
+	src/llama-vocab.h \
+	src/llama-grammar.h \
+	src/llama-sampling.h \
 	src/unicode.h \
 	include/llama.h \
 	ggml/include/ggml-cuda.h \
@@ -1064,6 +1071,29 @@ src/llama.o: \
 	ggml/include/ggml-backend.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
+src/llama-vocab.o: \
+	src/llama-vocab.cpp \
+	src/llama-vocab.h \
+	src/llama-impl.h \
+	include/llama.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+src/llama-grammar.o: \
+	src/llama-grammar.cpp \
+	src/llama-grammar.h \
+	src/llama-impl.h \
+	src/llama-vocab.h \
+	src/llama-sampling.h \
+	include/llama.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+src/llama-sampling.o: \
+	src/llama-sampling.cpp \
+	src/llama-sampling.h \
+	src/llama-impl.h \
+	include/llama.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
 $(LIB_LLAMA): \
 	$(OBJ_LLAMA) \
 	$(LIB_GGML)
@@ -1439,7 +1469,7 @@ run-benchmark-matmult: llama-benchmark-matmult
 .PHONY: run-benchmark-matmult swift
 
 tests/test-llama-grammar: tests/test-llama-grammar.cpp \
-	$(OBJ_GGML) $(OBJ_COMMON) src/unicode.o src/unicode-data.o
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
diff --git a/Package.swift b/Package.swift
index d40a48385f8c7a..1d90b47bfa3da9 100644
--- a/Package.swift
+++ b/Package.swift
@@ -4,6 +4,9 @@ import PackageDescription
 
 var sources = [
     "src/llama.cpp",
+    "src/llama-vocab.cpp",
+    "src/llama-grammar.cpp",
+    "src/llama-sampling.cpp",
     "src/unicode.cpp",
     "src/unicode-data.cpp",
     "ggml/src/ggml.c",
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 6a483c81545740..079e405168dff2 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -330,7 +330,7 @@ static llama_token llama_sampling_sample_impl(
         llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
 
         // Apply grammar constraints to the single token
-        llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar);
+        llama_grammar_sample(ctx_sampling->grammar, ctx_main, &single_token_data_array);
 
         // Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
         bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
@@ -421,7 +421,7 @@ static llama_token_data_array llama_sampling_prepare_impl(
 
     // apply grammar checks before sampling logic
     if (apply_grammar && ctx_sampling->grammar != NULL) {
-        llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
+        llama_grammar_sample(ctx_sampling->grammar, ctx_main, &cur_p);
     }
 
     return cur_p;
@@ -455,6 +455,6 @@ void llama_sampling_accept(
     ctx_sampling->prev.push_back(id);
 
     if (ctx_sampling->grammar != NULL && apply_grammar) {
-        llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id);
+        llama_grammar_accept_token(ctx_sampling->grammar, ctx_main, id);
     }
 }
diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp
index dd53ba9b1d5510..48a705e15cea99 100644
--- a/examples/gbnf-validator/gbnf-validator.cpp
+++ b/examples/gbnf-validator/gbnf-validator.cpp
@@ -16,20 +16,25 @@ static bool llama_sample_grammar_string(struct llama_grammar * grammar, const st
     auto decoded = decode_utf8(input_str, {});
     const auto & code_points = decoded.first;
 
+    const llama_grammar_rules  & rules      = llama_grammar_get_rules (grammar);
+          llama_grammar_stacks & cur_stacks = llama_grammar_get_stacks(grammar);
+
     size_t pos = 0;
     for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
-        auto prev_stacks = grammar->stacks;
-        llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks);
-        if (grammar->stacks.empty()) {
+        const llama_grammar_stacks prev_stacks = llama_grammar_get_stacks(grammar); // copy
+
+        llama_grammar_accept(rules, prev_stacks, *it, cur_stacks);
+
+        if (cur_stacks.empty()) {
             error_pos = pos;
             error_msg = "Unexpected character '" + unicode_cpt_to_utf8(*it) + "'";
-            grammar->stacks = prev_stacks;
+            cur_stacks = prev_stacks;
             return false;
         }
         ++pos;
     }
 
-    for (const auto & stack : grammar->stacks) {
+    for (const auto & stack : cur_stacks) {
         if (stack.empty()) {
             return true;
         }
diff --git a/include/llama.h b/include/llama.h
index bf27614679cb09..e68cd807e63bde 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -906,10 +906,10 @@ extern "C" {
     LLAMA_API llama_token llama_token_pad(const struct llama_model * model); // padding
 
     // Returns -1 if unknown, 1 for true or 0 for false.
-    LLAMA_API int32_t         llama_add_bos_token(const struct llama_model * model);
+    LLAMA_API int32_t llama_add_bos_token(const struct llama_model * model);
 
     // Returns -1 if unknown, 1 for true or 0 for false.
-    LLAMA_API int32_t         llama_add_eos_token(const struct llama_model * model);
+    LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model);
 
     // Codellama infill tokens
     LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
@@ -965,6 +965,10 @@ extern "C" {
                             bool   remove_special,
                             bool   unparse_special);
 
+    //
+    // Chat templates
+    //
+
     /// Apply chat template. Inspired by hf apply_chat_template() on python.
     /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
     /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
@@ -1003,6 +1007,23 @@ extern "C" {
 
     LLAMA_API struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar);
 
+    /// @details Apply constraints from grammar
+    LLAMA_API void llama_grammar_sample(
+            const struct llama_grammar * grammar,
+            const struct llama_context * ctx,
+                llama_token_data_array * candidates);
+    LLAMA_API DEPRECATED(void llama_sample_grammar(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+      const struct llama_grammar * grammar),
+        "use llama_grammar_sample instead");
+
+    /// @details Accepts the sampled token into the grammar
+    LLAMA_API void llama_grammar_accept_token(
+            struct llama_grammar * grammar,
+            struct llama_context * ctx,
+                     llama_token   token);
+
     //
     // Sampling functions
     //
@@ -1084,12 +1105,6 @@ extern "C" {
           llama_token_data_array * candidates,
                            float   temp);
 
-    /// @details Apply constraints from grammar
-    LLAMA_API void llama_sample_grammar(
-            struct llama_context * ctx,
-          llama_token_data_array * candidates,
-      const struct llama_grammar * grammar);
-
     /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
     /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
     /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
@@ -1127,12 +1142,6 @@ extern "C" {
             struct llama_context * ctx,
           llama_token_data_array * candidates);
 
-    /// @details Accepts the sampled token into the grammar
-    LLAMA_API void llama_grammar_accept_token(
-            struct llama_context * ctx,
-            struct llama_grammar * grammar,
-                     llama_token   token);
-
     //
     // Model split
     //
@@ -1175,38 +1184,45 @@ extern "C" {
 
 struct ggml_tensor;
 
+const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
+    struct llama_context * ctx
+);
+
 struct llama_partial_utf8 {
     uint32_t value;    // bit value so far (unshifted)
     int      n_remain; // num bytes remaining; -1 indicates invalid sequence
 };
 
-struct llama_grammar {
-    const std::vector<std::vector<llama_grammar_element>>   rules;
-    std::vector<std::vector<const llama_grammar_element *>> stacks;
-
-    // buffer for partially generated UTF-8 sequence from accepted tokens
-    llama_partial_utf8                                      partial_utf8;
-};
-
 struct llama_grammar_candidate {
     size_t               index;
     const uint32_t     * code_points;
     llama_partial_utf8   partial_utf8;
 };
 
-const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
-    struct llama_context * ctx
-);
+using llama_grammar_rule  = std::vector<      llama_grammar_element>;
+using llama_grammar_stack = std::vector<const llama_grammar_element *>;
+
+using llama_grammar_rules      = std::vector<llama_grammar_rule>;
+using llama_grammar_stacks     = std::vector<llama_grammar_stack>;
+using llama_grammar_candidates = std::vector<llama_grammar_candidate>;
+
+const llama_grammar_rules  & llama_grammar_get_rules (const struct llama_grammar * grammar);
+      llama_grammar_stacks & llama_grammar_get_stacks(      struct llama_grammar * grammar);
 
 void llama_grammar_accept(
-        const std::vector<std::vector<llama_grammar_element>>         & rules,
-        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
-        const uint32_t                                                  chr,
-        std::vector<std::vector<const llama_grammar_element *>>       & new_stacks);
+        const llama_grammar_rules  & rules,
+        const llama_grammar_stacks & stacks,
+        const uint32_t chr,
+              llama_grammar_stacks & new_stacks);
+
+std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
+        const llama_grammar_rules & rules,
+        const llama_grammar_stack & stack,
+        const llama_grammar_candidates & candidates);
 
 std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
         const std::string & src,
-        llama_partial_utf8   partial_start);
+        llama_partial_utf8 partial_start);
 
 // Randomly selects a token from the candidates based on their probabilities using given std::mt19937.
 // This is a temporary workaround in order to fix race conditions when sampling with multiple sequences.
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index c2049df79c212b..46a6ad56202f7f 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -14,6 +14,9 @@ endif()
 add_library(llama
             ../include/llama.h
             llama.cpp
+            llama-vocab.cpp
+            llama-grammar.cpp
+            llama-sampling.cpp
             unicode.h
             unicode.cpp
             unicode-data.cpp
diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp
new file mode 100644
index 00000000000000..bd9322e2fab09b
--- /dev/null
+++ b/src/llama-grammar.cpp
@@ -0,0 +1,539 @@
+#include "llama-grammar.h"
+
+#include "llama-vocab.h"
+#include "llama-sampling.h"
+
+#include <algorithm>
+
+// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
+// pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
+std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
+        const std::string & src,
+        llama_partial_utf8 partial_start) {
+    static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
+    const char          * pos      = src.c_str();
+    std::vector<uint32_t> code_points;
+
+    // common english strings have the same number of codepoints and bytes. `+ 1` for the terminating 0.
+    code_points.reserve(src.size() + 1);
+    uint32_t value    = partial_start.value;
+    int      n_remain = partial_start.n_remain;
+
+    // continue previous decode, if applicable
+    while (*pos != 0 && n_remain > 0) {
+        uint8_t next_byte = static_cast<uint8_t>(*pos);
+        if ((next_byte >> 6) != 2) {
+            // invalid sequence, abort
+            code_points.push_back(0);
+            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
+        }
+        value = (value << 6) + (next_byte & 0x3F);
+        ++pos;
+        --n_remain;
+    }
+
+    if (partial_start.n_remain > 0 && n_remain == 0) {
+        code_points.push_back(value);
+    }
+
+    // decode any subsequent utf-8 sequences, which may end in an incomplete one
+    while (*pos != 0) {
+        uint8_t first_byte = static_cast<uint8_t>(*pos);
+        uint8_t highbits   = first_byte >> 4;
+                n_remain   = lookup[highbits] - 1;
+
+        if (n_remain < 0) {
+            // invalid sequence, abort
+            code_points.clear();
+            code_points.push_back(0);
+            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
+        }
+
+        uint8_t mask  = (1 << (7 - n_remain)) - 1;
+                value = first_byte & mask;
+
+        ++pos;
+        while (*pos != 0 && n_remain > 0) {
+            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
+            ++pos;
+            --n_remain;
+        }
+        if (n_remain == 0) {
+            code_points.push_back(value);
+        }
+    }
+    code_points.push_back(0);
+
+    return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
+}
+
+const llama_grammar_rules & llama_grammar_get_rules(const struct llama_grammar * grammar) {
+    return grammar->rules;
+}
+
+llama_grammar_stacks & llama_grammar_get_stacks(struct llama_grammar * grammar) {
+    return grammar->stacks;
+}
+
+// returns true iff pos points to the end of one of the definitions of a rule
+static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
+    switch (pos->type) {
+        case LLAMA_GRETYPE_END: return true;  // NOLINT
+        case LLAMA_GRETYPE_ALT: return true;  // NOLINT
+        default:                return false;
+    }
+}
+
+// returns true iff chr satisfies the char range at pos (regular or inverse range)
+// asserts that pos is pointing to a char range element
+static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
+        const llama_grammar_element * pos,
+        const uint32_t                chr) {
+
+    bool found            = false;
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
+
+    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT
+
+    do {
+        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
+            // inclusive range, e.g. [a-z]
+            found = found || (pos->value <= chr && chr <= pos[1].value);
+            pos += 2;
+        } else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
+            // Any character matches "."
+            found = true;
+            pos += 1;
+        } else {
+            // exact char match, e.g. [a] or "a"
+            found = found || pos->value == chr;
+            pos += 1;
+        }
+    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
+
+    return std::make_pair(found == is_positive_char, pos);
+}
+
+// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
+// range at pos (regular or inverse range)
+// asserts that pos is pointing to a char range element
+static bool llama_grammar_match_partial_char(
+        const llama_grammar_element * pos,
+        const llama_partial_utf8      partial_utf8) {
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
+    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
+
+    uint32_t partial_value = partial_utf8.value;
+    int      n_remain      = partial_utf8.n_remain;
+
+    // invalid sequence or 7-bit char split across 2 bytes (overlong)
+    if (n_remain < 0 || (n_remain == 1 && partial_value < 2)) {
+        return false;
+    }
+
+    // range of possible code points this partial UTF-8 sequence could complete to
+    uint32_t low  = partial_value << (n_remain * 6);
+    uint32_t high = low | ((1 << (n_remain * 6)) - 1);
+
+    if (low == 0) {
+        if (n_remain == 2) {
+            low = 1 << 11;
+        } else if (n_remain == 3) {
+            low = 1 << 16;
+        }
+    }
+
+    do {
+        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
+            // inclusive range, e.g. [a-z]
+            if (pos->value <= high && low <= pos[1].value) {
+                return is_positive_char;
+            }
+            pos += 2;
+        } else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
+            // Any character matches "."
+            return true;
+        } else {
+            // exact char match, e.g. [a] or "a"
+            if (low <= pos->value && pos->value <= high) {
+                return is_positive_char;
+            }
+            pos += 1;
+        }
+    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
+
+    return !is_positive_char;
+}
+
+// transforms a grammar pushdown stack into N possible stacks, all ending
+// at a character range (terminal element)
+static void llama_grammar_advance_stack(
+        const llama_grammar_rules  & rules,
+        const llama_grammar_stack  & stack,
+              llama_grammar_stacks & new_stacks) {
+    if (stack.empty()) {
+        if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
+            new_stacks.emplace_back(stack);
+        }
+        return;
+    }
+
+    const llama_grammar_element * pos = stack.back();
+
+    switch (pos->type) {
+        case LLAMA_GRETYPE_RULE_REF: {
+            const size_t                  rule_id = static_cast<size_t>(pos->value);
+            const llama_grammar_element * subpos  = rules[rule_id].data();
+            do {
+                // init new stack without the top (pos)
+                llama_grammar_stack new_stack(stack.begin(), stack.end() - 1);
+                if (!llama_grammar_is_end_of_sequence(pos + 1)) {
+                    // if this rule ref is followed by another element, add that to stack
+                    new_stack.push_back(pos + 1);
+                }
+                if (!llama_grammar_is_end_of_sequence(subpos)) {
+                    // if alternate is nonempty, add to stack
+                    new_stack.push_back(subpos);
+                }
+                llama_grammar_advance_stack(rules, new_stack, new_stacks);
+                while (!llama_grammar_is_end_of_sequence(subpos)) {
+                    // scan to end of alternate def
+                    subpos++;
+                }
+                if (subpos->type == LLAMA_GRETYPE_ALT) {
+                    // there's another alternate def of this rule to process
+                    subpos++;
+                } else {
+                    break;
+                }
+            } while (true);
+            break;
+        }
+        case LLAMA_GRETYPE_CHAR:
+        case LLAMA_GRETYPE_CHAR_NOT:
+        case LLAMA_GRETYPE_CHAR_ANY:
+            if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
+                // only add the stack if it's not a duplicate of one we already have
+                new_stacks.emplace_back(stack);
+            }
+            break;
+        default:
+            // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
+            // (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
+            // those
+            GGML_ASSERT(false);
+    }
+}
+
+// takes a set of possible pushdown stacks on a grammar, which are required to
+// be positioned at a character range (see `llama_grammar_advance_stack`), and
+// produces the N possible stacks if the given char is accepted at those
+// positions
+void llama_grammar_accept(
+        const llama_grammar_rules  & rules,
+        const llama_grammar_stacks & stacks,
+        const uint32_t               chr,
+              llama_grammar_stacks & new_stacks) {
+    new_stacks.clear();
+
+    for (const auto & stack : stacks) {
+        if (stack.empty()) {
+            continue;
+        }
+
+        auto match = llama_grammar_match_char(stack.back(), chr);
+        if (match.first) {
+            const llama_grammar_element * pos = match.second;
+
+            // update top of stack to next element, if any
+            llama_grammar_stack new_stack(stack.begin(), stack.end() - 1);
+            if (!llama_grammar_is_end_of_sequence(pos)) {
+                new_stack.push_back(pos);
+            }
+            llama_grammar_advance_stack(rules, new_stack, new_stacks);
+        }
+    }
+}
+
+static llama_grammar_candidates llama_grammar_reject_candidates(
+        const llama_grammar_rules  & rules,
+        const llama_grammar_stacks & stacks,
+        const llama_grammar_candidates & candidates) {
+    GGML_ASSERT(!stacks.empty()); // REVIEW
+
+    if (candidates.empty()) {
+        return {};
+    }
+
+    auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);
+
+    for (size_t i = 1, size = stacks.size(); i < size; ++i) {
+        rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
+    }
+    return rejects;
+}
+
+llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
+        const llama_grammar_rules      & rules,
+        const llama_grammar_stack      & stack,
+        const llama_grammar_candidates & candidates) {
+
+    llama_grammar_candidates rejects;
+    rejects.reserve(candidates.size());
+
+    if (stack.empty()) {
+        for (const auto & tok : candidates) {
+            if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) {
+                rejects.push_back(tok);
+            }
+        }
+        return rejects;
+    }
+
+    const llama_grammar_element * stack_pos = stack.back();
+
+    llama_grammar_candidates next_candidates;
+    next_candidates.reserve(candidates.size());
+
+    for (const auto & tok : candidates) {
+        if (*tok.code_points == 0) {
+            // reached end of full codepoints in token, reject iff it ended in a partial sequence
+            // that cannot satisfy this position in grammar
+            if (tok.partial_utf8.n_remain != 0 &&
+                    !llama_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
+                rejects.push_back(tok);
+            }
+        } else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
+            next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
+        } else {
+            rejects.push_back(tok);
+        }
+    }
+
+    const auto * stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;
+
+    // update top of stack to next element, if any
+    llama_grammar_stack stack_after(stack.begin(), stack.end() - 1);
+    if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
+        stack_after.push_back(stack_pos_after);
+    }
+    llama_grammar_stacks next_stacks;
+    llama_grammar_advance_stack(rules, stack_after, next_stacks);
+
+    auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
+    for (const auto & tok : next_rejects) {
+        rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
+    }
+
+    return rejects;
+}
+
+static bool llama_grammar_detect_left_recursion(
+        const llama_grammar_rules & rules,
+        size_t rule_index,
+        std::vector<bool> * rules_visited,
+        std::vector<bool> * rules_in_progress,
+        std::vector<bool> * rules_may_be_empty) {
+    if ((*rules_in_progress)[rule_index]) {
+        return true;
+    }
+
+    (*rules_in_progress)[rule_index] = true;
+
+    const llama_grammar_rule & rule = rules[rule_index];
+
+    // First check if the rule might produce the empty string. This could be done combined with the second
+    // step but it's more readable as two steps.
+    bool at_rule_start = true;
+    for (size_t i = 0; i < rule.size(); i++) {
+        if (llama_grammar_is_end_of_sequence(&rule[i])) {
+            if (at_rule_start) {
+                (*rules_may_be_empty)[rule_index] = true;
+                break;
+            }
+            at_rule_start = true;
+        } else {
+            at_rule_start = false;
+        }
+    }
+
+    // Second, recurse into leftmost nonterminals (or next-leftmost as long as the previous nonterminal may
+    // be empty)
+    bool recurse_into_nonterminal = true;
+    for (size_t i = 0; i < rule.size(); i++) {
+        if (rule[i].type == LLAMA_GRETYPE_RULE_REF && recurse_into_nonterminal) {
+            if (llama_grammar_detect_left_recursion(rules, (size_t)rule[i].value, rules_visited, rules_in_progress, rules_may_be_empty)) {
+                return true;
+            }
+            if (!((*rules_may_be_empty)[(size_t)rule[i].value])) {
+                recurse_into_nonterminal = false;
+            }
+        } else if (llama_grammar_is_end_of_sequence(&rule[i])) {
+            recurse_into_nonterminal = true;
+        } else {
+            recurse_into_nonterminal = false;
+        }
+    }
+
+    (*rules_in_progress)[rule_index] = false;
+    (*rules_visited)[rule_index] = true;
+    return false;
+}
+
+//
+// grammar - external
+//
+
+struct llama_grammar * llama_grammar_init_impl(
+            const llama_grammar_element ** rules,
+                                 size_t    n_rules,
+                                 size_t    start_rule_index) {
+    const llama_grammar_element * pos;
+
+    // copy rule definitions into vectors
+    llama_grammar_rules vec_rules(n_rules);
+    for (size_t i = 0; i < n_rules; i++) {
+        for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
+            vec_rules[i].push_back(*pos);
+        }
+        vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
+    }
+
+    // Check for left recursion
+    std::vector<bool> rules_visited(n_rules);
+    std::vector<bool> rules_in_progress(n_rules);
+    std::vector<bool> rules_may_be_empty(n_rules);
+    for (size_t i = 0; i < n_rules; i++) {
+        if (rules_visited[i]) {
+            continue;
+        }
+        if (llama_grammar_detect_left_recursion(vec_rules, i, &rules_visited, &rules_in_progress, &rules_may_be_empty)) {
+            LLAMA_LOG_ERROR("unsupported grammar, left recursion detected for nonterminal at index %zu", i);
+            return nullptr;
+        }
+    }
+
+    // loop over alternates of start rule to build initial stacks
+    llama_grammar_stacks stacks;
+    pos = vec_rules[start_rule_index].data();
+    do {
+        llama_grammar_stack stack;
+        if (!llama_grammar_is_end_of_sequence(pos)) {
+            // if alternate is nonempty, add to stack
+            stack.push_back(pos);
+        }
+        llama_grammar_advance_stack(vec_rules, stack, stacks);
+        while (!llama_grammar_is_end_of_sequence(pos)) {
+            // scan to end of alternate def
+            pos++;
+        }
+        if (pos->type == LLAMA_GRETYPE_ALT) {
+            // there's another alternate def of this rule to process
+            pos++;
+        } else {
+            break;
+        }
+    } while (true);
+
+    // Important: vec_rules has to be moved here, not copied, because stacks contains
+    // pointers to elements of vec_rules. If vec_rules were copied into llama_grammar
+    // then the pointers would be invalidated when the local vec_rules goes out of scope.
+    return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
+}
+
+void llama_grammar_free_impl(struct llama_grammar * grammar) {
+    delete grammar;
+}
+
+struct llama_grammar * llama_grammar_copy_impl(const struct llama_grammar * grammar) {
+    llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };
+
+    // redirect elements in stacks to point to new rules
+    for (size_t is = 0; is < result->stacks.size(); is++) {
+        for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
+            for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
+                for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
+                    if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
+                         result->stacks[is][ie]  =  &result->rules[ir0][ir1];
+                    }
+                }
+            }
+        }
+    }
+
+    return result;
+}
+
+void llama_grammar_sample_impl(const struct llama_grammar * grammar, const struct llama_vocab * vocab, const struct llama_sampling * smpl, llama_token_data_array * candidates) {
+    GGML_ASSERT(grammar);
+    GGML_ASSERT(vocab);
+
+    int64_t t_start_sample_us = ggml_time_us();
+
+    bool allow_eog = false;
+    for (const auto & stack : grammar->stacks) {
+        if (stack.empty()) {
+            allow_eog = true;
+            break;
+        }
+    }
+
+    std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
+    candidates_decoded.reserve(candidates->size);
+
+    llama_grammar_candidates candidates_grammar;
+    candidates_grammar.reserve(candidates->size);
+
+    for (size_t i = 0; i < candidates->size; ++i) {
+        const llama_token id      = candidates->data[i].id;
+        const std::string & piece = vocab->cache_token_to_piece.at(id);
+
+        if (llama_token_is_eog_impl(*vocab, id)) {
+            if (!allow_eog) {
+                candidates->data[i].logit = -INFINITY;
+            }
+        } else if (piece.empty() || piece[0] == 0) {
+            candidates->data[i].logit = -INFINITY;
+        } else {
+            candidates_decoded.push_back(decode_utf8(piece, grammar->partial_utf8));
+            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
+        }
+    }
+
+    const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
+    for (const auto & reject : rejects) {
+        candidates->data[reject.index].logit = -INFINITY;
+    }
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+}
+
+void llama_grammar_accept_token_impl(struct llama_grammar * grammar, const struct llama_vocab * vocab, const struct llama_sampling * smpl, llama_token token) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    if (llama_token_is_eog_impl(*vocab, token)) {
+        for (const auto & stack : grammar->stacks) {
+            if (stack.empty()) {
+                return;
+            }
+        }
+        GGML_ASSERT(false);
+    }
+
+    const std::string & piece = vocab->cache_token_to_piece.at(token);
+
+    // Note terminating 0 in decoded string
+    const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
+    const auto & code_points = decoded.first;
+
+    llama_grammar_stacks tmp_new_stacks;
+    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
+        llama_grammar_accept(grammar->rules, grammar->stacks, *it, tmp_new_stacks);
+        grammar->stacks = tmp_new_stacks;
+    }
+
+    grammar->partial_utf8 = decoded.second;
+    GGML_ASSERT(!grammar->stacks.empty());
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+}
diff --git a/src/llama-grammar.h b/src/llama-grammar.h
new file mode 100644
index 00000000000000..8e578e09f8c2dc
--- /dev/null
+++ b/src/llama-grammar.h
@@ -0,0 +1,41 @@
+#pragma once
+
+#include "llama-impl.h"
+
+struct llama_vocab;
+struct llama_sampling;
+
+struct llama_grammar {
+    const llama_grammar_rules  rules;
+          llama_grammar_stacks stacks;
+
+    // buffer for partially generated UTF-8 sequence from accepted tokens
+    llama_partial_utf8 partial_utf8;
+};
+
+struct llama_grammar * llama_get_grammar(struct llama_context * ctx);
+
+//
+// internal API
+//
+
+struct llama_grammar * llama_grammar_init_impl(
+            const llama_grammar_element ** rules,
+                                 size_t    n_rules,
+                                 size_t    start_rule_index);
+
+void llama_grammar_free_impl(struct llama_grammar * grammar);
+
+struct llama_grammar * llama_grammar_copy_impl(const struct llama_grammar * grammar);
+
+void llama_grammar_sample_impl(
+        const struct llama_grammar * grammar,
+          const struct llama_vocab * vocab,
+       const struct llama_sampling * smpl,
+            llama_token_data_array * candidates);
+
+void llama_grammar_accept_token_impl(
+              struct llama_grammar * grammar,
+          const struct llama_vocab * vocab,
+       const struct llama_sampling * smpl,
+                       llama_token   token);
diff --git a/src/llama-impl.h b/src/llama-impl.h
new file mode 100644
index 00000000000000..dcc8c1c15a1b1e
--- /dev/null
+++ b/src/llama-impl.h
@@ -0,0 +1,26 @@
+#pragma once
+
+#define LLAMA_API_INTERNAL
+#include "llama.h"
+
+#ifdef __GNUC__
+#ifdef __MINGW32__
+#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
+#else
+#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
+#endif
+#else
+#define LLAMA_ATTRIBUTE_FORMAT(...)
+#endif
+
+//
+// logging
+//
+
+LLAMA_ATTRIBUTE_FORMAT(2, 3)
+void llama_log_internal        (ggml_log_level level, const char * format, ...);
+void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
+
+#define LLAMA_LOG_INFO(...)  llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
+#define LLAMA_LOG_WARN(...)  llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
+#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
new file mode 100644
index 00000000000000..8910f6d6542e91
--- /dev/null
+++ b/src/llama-sampling.cpp
@@ -0,0 +1,635 @@
+#include "llama-sampling.h"
+
+#include <algorithm>
+#include <cstring>
+#include <ctime>
+#include <cfloat>
+#include <numeric>
+#include <unordered_map>
+
+static void llama_log_softmax(float * array, size_t size) {
+    float max_l = *std::max_element(array, array + size);
+    float sum = 0.f;
+    for (size_t i = 0; i < size; ++i) {
+        float p = expf(array[i] - max_l);
+        sum += p;
+        array[i] = p;
+    }
+
+    for (size_t i = 0; i < size; ++i) {
+        array[i] = logf(array[i] / sum);
+    }
+}
+
+void llama_set_rng_seed_impl(struct llama_sampling * smpl, uint32_t seed) {
+    if (seed == LLAMA_DEFAULT_SEED) {
+        seed = time(NULL);
+    }
+
+    smpl->rng.seed(seed);
+}
+
+void llama_sample_softmax_impl(struct llama_sampling * smpl, llama_token_data_array * candidates) {
+    GGML_ASSERT(candidates->size > 0);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Sort the logits in descending order
+    if (!candidates->sorted) {
+        std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+            return a.logit > b.logit;
+        });
+        candidates->sorted = true;
+    }
+
+    float max_l = candidates->data[0].logit;
+    float cum_sum = 0.0f;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        float p = expf(candidates->data[i].logit - max_l);
+        candidates->data[i].p = p;
+        cum_sum += p;
+    }
+    for (size_t i = 0; i < candidates->size; ++i) {
+        candidates->data[i].p /= cum_sum;
+    }
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_top_k_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
+    // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
+    // if (k >= (int32_t)candidates->size) {
+    //     return;
+    // }
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    if (k <= 0) {
+        k = candidates->size;
+    }
+
+    k = std::max(k, (int) min_keep);
+    k = std::min(k, (int) candidates->size);
+
+    // Sort scores in descending order
+    if (!candidates->sorted) {
+        auto comp = [](const llama_token_data & a, const llama_token_data & b) {
+            return a.logit > b.logit;
+        };
+        if (k <= 128) {
+            std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
+        } else {
+            constexpr int   nbuckets     = 128;
+            constexpr float bucket_low   = -10.0f;
+            constexpr float bucket_high  =  10.0f;
+            constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
+            constexpr float bucker_inter = -bucket_low * bucket_scale;
+
+            std::vector<int> bucket_idx(candidates->size);
+            std::vector<int> histo(nbuckets, 0);
+
+            for (int i = 0; i < (int)candidates->size; ++i) {
+                const float val = candidates->data[i].logit;
+                int ib = int(bucket_scale * val + bucker_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
+                ib = std::max(0, std::min(nbuckets-1, ib));
+                bucket_idx[i] = ib;
+                ++histo[ib];
+            }
+            int nhave = 0;
+            int ib = nbuckets - 1;
+            for ( ; ib >= 0; --ib) {
+                nhave += histo[ib];
+                if (nhave >= k) break;
+            }
+            std::vector<llama_token_data> tmp_tokens(nhave);
+            auto ptr = tmp_tokens.data();
+            std::vector<llama_token_data*> bucket_ptrs;
+            bucket_ptrs.reserve(nbuckets - ib);
+            for (int j = nbuckets - 1; j >= ib; --j) {
+                bucket_ptrs.push_back(ptr);
+                ptr += histo[j];
+            }
+            for (int i = 0; i < (int)candidates->size; ++i) {
+                int j = bucket_idx[i];
+                if (j >= ib) {
+                    *bucket_ptrs[nbuckets-1-j]++ = candidates->data[i];
+                }
+            }
+
+            ptr = tmp_tokens.data();
+            int ndone = 0;
+            for (int j = nbuckets-1; j > ib; --j) {
+                std::sort(ptr, ptr + histo[j], comp);
+                ptr += histo[j];
+                ndone += histo[j];
+            }
+            std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp);
+
+            std::memcpy(candidates->data, tmp_tokens.data(), k*sizeof(llama_token_data));
+
+        }
+        candidates->sorted = true;
+    }
+    candidates->size = k;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_top_p_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep) {
+    if (p >= 1.0f) {
+        return;
+    }
+
+    llama_sample_softmax_impl(smpl, candidates);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Compute the cumulative probabilities
+    float cum_sum = 0.0f;
+    size_t last_idx = candidates->size;
+
+    for (size_t i = 0; i < candidates->size; ++i) {
+        cum_sum += candidates->data[i].p;
+
+        // Check if the running sum is at least p or if we have kept at least min_keep tokens
+        // we set the last index to i+1 to indicate that the current iterate should be included in the set
+        if (cum_sum >= p && i + 1 >= min_keep) {
+            last_idx = i + 1;
+            break;
+        }
+    }
+
+    // Resize the output vector to keep only the top-p tokens
+    candidates->size = last_idx;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_min_p_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep) {
+    if (p <= 0.0f || !candidates->size) {
+        return;
+    }
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    bool min_p_applied = false;
+
+    // if the candidates aren't sorted, try the unsorted implementation first
+    if (!candidates->sorted) {
+        std::vector<llama_token_data> filtered_tokens;
+
+        float max_logit = -FLT_MAX;
+        for (size_t i = 0; i < candidates->size; ++i) {
+            max_logit = std::max(max_logit, candidates->data[i].logit);
+        }
+        const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max
+
+        for (size_t i = 0; i < candidates->size; ++i) {
+            if (candidates->data[i].logit >= min_logit) {
+                filtered_tokens.push_back(candidates->data[i]);
+            }
+        }
+
+        // if we have enough values the operation was a success
+        if (filtered_tokens.size() >= min_keep) {
+            memcpy(candidates->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
+            candidates->size = filtered_tokens.size();
+            min_p_applied = true;
+        }
+    }
+
+    // if the candidates are sorted or the unsorted implementation failed, use this implementation
+    if (!min_p_applied) {
+        // Sort the logits in descending order
+        if (!candidates->sorted) {
+            std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+                return a.logit > b.logit;
+            });
+            candidates->sorted = true;
+        }
+
+        const float min_logit = candidates->data[0].logit + logf(p); // min logit for p_i >= p * p_max
+        size_t i = 1; // first token always matches
+
+        for (; i < candidates->size; ++i) {
+            if (candidates->data[i].logit < min_logit && i >= min_keep) {
+                break; // prob too small
+            }
+        }
+
+        // Resize the output vector to keep only the matching tokens
+        candidates->size = i;
+    }
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_tail_free_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float z, size_t min_keep) {
+    if (z >= 1.0f || candidates->size <= 2) {
+        return;
+    }
+
+    llama_sample_softmax_impl((struct llama_sampling *) nullptr, candidates);
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Compute the first and second derivatives
+    std::vector<float> first_derivatives(candidates->size - 1);
+    std::vector<float> second_derivatives(candidates->size - 2);
+
+    for (size_t i = 0; i < first_derivatives.size(); ++i) {
+        first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
+    }
+    for (size_t i = 0; i < second_derivatives.size(); ++i) {
+        second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
+    }
+
+    // Calculate absolute value of second derivatives
+    for (size_t i = 0; i < second_derivatives.size(); ++i) {
+        second_derivatives[i] = std::abs(second_derivatives[i]);
+    }
+
+    // Normalize the second derivatives
+    {
+        const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
+
+        if (second_derivatives_sum > 1e-6f) {
+            for (float & value : second_derivatives) {
+                value /= second_derivatives_sum;
+            }
+        } else {
+            for (float & value : second_derivatives) {
+                value = 1.0f / second_derivatives.size();
+            }
+        }
+    }
+
+    float cum_sum = 0.0f;
+    size_t last_idx = candidates->size;
+    for (size_t i = 0; i < second_derivatives.size(); ++i) {
+        cum_sum += second_derivatives[i];
+
+        // Check if the running sum is greater than z or if we have kept at least min_keep tokens
+        if (cum_sum > z && i >= min_keep) {
+            last_idx = i;
+            break;
+        }
+    }
+
+    // Resize the output vector to keep only the tokens above the tail location
+    candidates->size = last_idx;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_typical_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep) {
+    // Reference implementation:
+    // https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
+    if (p >= 1.0f) {
+        return;
+    }
+
+    // Compute the softmax of logits and calculate entropy
+    llama_sample_softmax_impl((struct llama_sampling *) nullptr, candidates);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    float entropy = 0.0f;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        entropy += -candidates->data[i].p * logf(candidates->data[i].p);
+    }
+
+    // Compute the absolute difference between negative log probability and entropy for each candidate
+    std::vector<float> shifted_scores;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
+        shifted_scores.push_back(shifted_score);
+    }
+
+    // Sort tokens based on the shifted_scores and their corresponding indices
+    std::vector<size_t> indices(candidates->size);
+    std::iota(indices.begin(), indices.end(), 0);
+
+    std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
+        return shifted_scores[a] < shifted_scores[b];
+    });
+
+    // Compute the cumulative probabilities
+    float cum_sum = 0.0f;
+    size_t last_idx = indices.size();
+
+    for (size_t i = 0; i < indices.size(); ++i) {
+        size_t idx = indices[i];
+        cum_sum += candidates->data[idx].p;
+
+        // Check if the running sum is greater than typical or if we have kept at least min_keep tokens
+        if (cum_sum > p && i >= min_keep - 1) {
+            last_idx = i + 1;
+            break;
+        }
+    }
+
+    // Resize the output vector to keep only the locally typical tokens
+    std::vector<llama_token_data> new_candidates;
+    for (size_t i = 0; i < last_idx; ++i) {
+        size_t idx = indices[i];
+        new_candidates.push_back(candidates->data[idx]);
+    }
+
+    // Replace the data in candidates with the new_candidates data
+    std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
+    candidates->size = new_candidates.size();
+    candidates->sorted = false;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_entropy_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float min_temp, float max_temp, float exponent_val) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // no need to do anything if there is only one (or zero) candidates
+    if(candidates->size <= 1) {
+        return;
+    }
+
+    // Calculate maximum possible entropy
+    float max_entropy = -logf(1.0f / candidates->size);
+
+    llama_sample_softmax_impl((struct llama_sampling *) nullptr, candidates);
+
+    // Calculate entropy of the softmax probabilities
+    float entropy = 0.0f;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        float prob = candidates->data[i].p;
+        if (prob > 0.0f) { // Ensure no log(0)
+            entropy -= prob * logf(prob);
+        }
+    }
+
+    // Normalize the entropy (max_entropy cannot be 0 here because we checked candidates->size != 1 above)
+    float normalized_entropy = entropy / max_entropy;
+
+    // Map the normalized entropy to the desired temperature range using the power function
+    float dyn_temp = min_temp + (max_temp - min_temp) * powf(normalized_entropy, exponent_val);
+
+#ifdef DEBUG
+    LLAMA_LOG_INFO("Your text maxtemp value is: %f\n", max_temp);
+    LLAMA_LOG_INFO("Entropy: %f\n", entropy);
+    LLAMA_LOG_INFO("Max Possible Entropy: %f\n", max_entropy);
+    LLAMA_LOG_INFO("Normalized Entropy: %f\n", normalized_entropy);
+    LLAMA_LOG_INFO("Exponent: %f\n", exponent_val);
+    LLAMA_LOG_INFO("Dynamic Temperature (dyn_temp): %f\n", dyn_temp);
+#endif
+
+    // Apply the dynamically calculated temperature scaling
+    for (size_t i = 0; i < candidates->size; ++i) {
+        candidates->data[i].logit /= dyn_temp;
+    }
+
+    // Re-compute softmax probabilities after scaling logits with dynamic temperature
+    double max_l_double = candidates->data[0].logit;
+    double cum_sum_double = 0.0;
+    for (size_t i = 0; i < candidates->size; ++i) {
+        double p = exp(candidates->data[i].logit - max_l_double);
+        candidates->data[i].p = p; // Store the scaled probability
+        cum_sum_double += p;
+    }
+    for (size_t i = 0; i < candidates->size; ++i) {
+        candidates->data[i].p /= cum_sum_double; // Re-normalize the probabilities
+    }
+
+#ifdef DEBUG
+    // Print the updated top 25 probabilities after temperature scaling
+    LLAMA_LOG_INFO("\nUpdated Top 25 Probabilities After Dynamic Temperature Scaling (in percentages):\n");
+    for (size_t i = 0; i < 25 && i < candidates->size; ++i) {
+        LLAMA_LOG_INFO("Token %zu: %f%%\n", i + 1, candidates->data[i].p * 100.0f);
+    }
+#endif
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_temp_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float temp) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    for (size_t i = 0; i < candidates->size; ++i) {
+        candidates->data[i].logit /= temp;
+    }
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_repetition_penalties_impl(
+        struct llama_sampling * smpl,
+       llama_token_data_array * candidates,
+            const llama_token * last_tokens,
+                       size_t   penalty_last_n,
+                       float   penalty_repeat,
+                       float   penalty_freq,
+                       float   penalty_present) {
+    if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) {
+        return;
+    }
+
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Create a frequency map to count occurrences of each token in last_tokens
+    std::unordered_map<llama_token, int> token_count;
+    for (size_t i = 0; i < penalty_last_n; ++i) {
+        token_count[last_tokens[i]]++;
+    }
+
+    // Apply frequency and presence penalties to the candidates
+    for (size_t i = 0; i < candidates->size; ++i) {
+        const auto token_iter = token_count.find(candidates->data[i].id);
+        if (token_iter == token_count.end()) {
+            continue;
+        }
+
+        const int count = token_iter->second;
+
+        // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
+        // This is common fix for this problem, which is to multiply by the penalty instead of dividing.
+        if (candidates->data[i].logit <= 0) {
+            candidates->data[i].logit *= penalty_repeat;
+        } else {
+            candidates->data[i].logit /= penalty_repeat;
+        }
+
+        candidates->data[i].logit -= float(count) * penalty_freq + float(count > 0) * penalty_present;
+    }
+
+    candidates->sorted = false;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+}
+
+void llama_sample_apply_guidance_impl(
+        struct llama_sampling * smpl,
+                        float * logits,
+                        float * logits_guidance,
+                        float   scale) {
+    GGML_ASSERT(smpl);
+
+    const auto t_start_sample_us = ggml_time_us();
+    const auto n_vocab = smpl->n_vocab;
+
+    llama_log_softmax(logits, n_vocab);
+    llama_log_softmax(logits_guidance, n_vocab);
+
+    for (int i = 0; i < n_vocab; ++i) {
+              auto & l = logits[i];
+        const auto & g = logits_guidance[i];
+
+        l = scale * (l - g) + g;
+    }
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+}
+
+llama_token llama_sample_token_mirostat_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
+    GGML_ASSERT(smpl);
+
+    const int32_t n_vocab = float(smpl->n_vocab);
+
+    int64_t t_start_sample_us = ggml_time_us();
+
+    llama_sample_softmax_impl((struct llama_sampling *) nullptr, candidates);
+
+    // Estimate s_hat using the most probable m tokens
+    float s_hat = 0.0;
+    float sum_ti_bi = 0.0;
+    float sum_ti_sq = 0.0;
+    for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
+        float t_i = logf(float(i + 2) / float(i + 1));
+        float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
+        sum_ti_bi += t_i * b_i;
+        sum_ti_sq += t_i * t_i;
+    }
+    s_hat = sum_ti_bi / sum_ti_sq;
+
+    // Compute k from the estimated s_hat and target surprise value
+    float epsilon_hat = s_hat - 1;
+    float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(n_vocab, -epsilon_hat)), 1 / s_hat);
+
+    // Sample the next word X using top-k sampling
+    llama_sample_top_k_impl((struct llama_sampling *) nullptr, candidates, int(k), 1);
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    llama_token X = llama_sample_token_impl(smpl, candidates);
+    t_start_sample_us = ggml_time_us();
+
+    // Compute error as the difference between observed surprise and target surprise value
+    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return candidate.id == X;
+    }));
+    float observed_surprise = -log2f(candidates->data[X_idx].p);
+    float e = observed_surprise - tau;
+
+    // Update mu using the learning rate and error
+    *mu = *mu - eta * e;
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    return X;
+}
+
+llama_token llama_sample_token_mirostat_v2_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float tau, float eta, float * mu) {
+    int64_t t_start_sample_us;
+    t_start_sample_us = ggml_time_us();
+
+    llama_sample_softmax_impl(smpl, candidates);
+
+    // Truncate the words with surprise values greater than mu
+    candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return -log2f(candidate.p) > *mu;
+    }));
+
+    if (candidates->size == 0) {
+        candidates->size = 1;
+    }
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+
+    // Normalize the probabilities of the remaining words
+    llama_sample_softmax_impl(smpl, candidates);
+
+    // Sample the next word X from the remaining words
+    llama_token X = llama_sample_token_impl(smpl, candidates);
+    t_start_sample_us = ggml_time_us();
+
+    // Compute error as the difference between observed surprise and target surprise value
+    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
+        return candidate.id == X;
+    }));
+    float observed_surprise = -log2f(candidates->data[X_idx].p);
+    float e = observed_surprise - tau;
+
+    // Update mu using the learning rate and error
+    *mu = *mu - eta * e;
+
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+    return X;
+}
+
+llama_token llama_sample_token_greedy_impl(struct llama_sampling * smpl, llama_token_data_array * candidates) {
+    const int64_t t_start_sample_us = ggml_time_us();
+
+    // Find max element
+    auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+        return a.logit < b.logit;
+    });
+
+    llama_token result = max_iter->id;
+    if (smpl) {
+        smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+        smpl->n_sample++;
+    }
+    return result;
+}
+
+llama_token llama_sample_token_with_rng_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, std::mt19937 & rng) {
+    GGML_ASSERT(smpl);
+
+    const int64_t t_start_sample_us = ggml_time_us();
+    llama_sample_softmax_impl((struct llama_sampling *) nullptr, candidates);
+
+    std::vector<float> probs;
+    probs.reserve(candidates->size);
+    for (size_t i = 0; i < candidates->size; ++i) {
+        probs.push_back(candidates->data[i].p);
+    }
+
+    std::discrete_distribution<> dist(probs.begin(), probs.end());
+    int idx = dist(rng);
+
+    llama_token result = candidates->data[idx].id;
+
+    smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
+    smpl->n_sample++;
+
+    return result;
+}
+
+llama_token llama_sample_token_impl(struct llama_sampling * smpl, llama_token_data_array * candidates) {
+    return llama_sample_token_with_rng_impl(smpl, candidates, smpl->rng);
+}
diff --git a/src/llama-sampling.h b/src/llama-sampling.h
new file mode 100644
index 00000000000000..f7f8e3ef706bc8
--- /dev/null
+++ b/src/llama-sampling.h
@@ -0,0 +1,56 @@
+#pragma once
+
+#include "llama-impl.h"
+
+struct llama_sampling {
+    llama_sampling(int32_t n_vocab) : n_vocab(n_vocab) {}
+
+    std::mt19937 rng;
+
+    int32_t n_vocab = 0;
+
+    mutable int64_t t_sample_us = 0;
+    mutable int32_t n_sample = 0;
+
+    void reset_timings() const {
+        t_sample_us = 0;
+        n_sample = 0;
+    }
+};
+
+//
+// internal API
+//
+
+void llama_set_rng_seed_impl(struct llama_sampling * smpl, uint32_t seed);
+
+void llama_sample_softmax_impl  (struct llama_sampling * smpl, llama_token_data_array * candidates);
+void llama_sample_top_k_impl    (struct llama_sampling * smpl, llama_token_data_array * candidates, int32_t k, size_t min_keep);
+void llama_sample_top_p_impl    (struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep);
+void llama_sample_min_p_impl    (struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep);
+void llama_sample_tail_free_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float z, size_t min_keep);
+void llama_sample_typical_impl  (struct llama_sampling * smpl, llama_token_data_array * candidates, float p, size_t min_keep);
+void llama_sample_entropy_impl  (struct llama_sampling * smpl, llama_token_data_array * candidates, float min_temp, float max_temp, float exponent_val);
+void llama_sample_temp_impl     (struct llama_sampling * smpl, llama_token_data_array * candidates, float temp);
+
+void llama_sample_repetition_penalties_impl(
+        struct llama_sampling * smpl,
+       llama_token_data_array * candidates,
+            const llama_token * last_tokens,
+                       size_t   penalty_last_n,
+                        float   penalty_repeat,
+                        float   penalty_freq,
+                        float   penalty_present);
+
+void llama_sample_apply_guidance_impl(
+        struct llama_sampling * smpl,
+                        float * logits,
+                        float * logits_guidance,
+                        float   scale);
+
+llama_token llama_sample_token_mirostat_impl   (struct llama_sampling * smpl, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu);
+llama_token llama_sample_token_mirostat_v2_impl(struct llama_sampling * smpl, llama_token_data_array * candidates, float tau, float eta, float * mu);
+llama_token llama_sample_token_greedy_impl     (struct llama_sampling * smpl, llama_token_data_array * candidates);
+llama_token llama_sample_token_with_rng_impl   (struct llama_sampling * smpl, llama_token_data_array * candidates, std::mt19937 & rng);
+llama_token llama_sample_token_impl            (struct llama_sampling * smpl, llama_token_data_array * candidates);
+
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
new file mode 100644
index 00000000000000..c482b36899a1cc
--- /dev/null
+++ b/src/llama-vocab.cpp
@@ -0,0 +1,1721 @@
+#include "llama-vocab.h"
+
+#include "unicode.h"
+
+#include <algorithm>
+#include <cassert>
+#include <cfloat>
+#include <climits>
+#include <cstdarg>
+#include <cstring>
+#include <forward_list>
+#include <queue>
+#include <sstream>
+
+//
+// helpers
+//
+
+static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
+    std::string result;
+    for (size_t pos = 0; ; pos += search.length()) {
+        auto new_pos = s.find(search, pos);
+        if (new_pos == std::string::npos) {
+            result += s.substr(pos, s.size() - pos);
+            break;
+        }
+        result += s.substr(pos, new_pos - pos) + replace;
+        pos = new_pos;
+    }
+    s = std::move(result);
+}
+
+LLAMA_ATTRIBUTE_FORMAT(1, 2)
+static std::string format(const char * fmt, ...) {
+    va_list ap;
+    va_list ap2;
+    va_start(ap, fmt);
+    va_copy(ap2, ap);
+    int size = vsnprintf(NULL, 0, fmt, ap);
+    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
+    std::vector<char> buf(size + 1);
+    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+    GGML_ASSERT(size2 == size);
+    va_end(ap2);
+    va_end(ap);
+    return std::string(buf.data(), size);
+}
+
+struct naive_trie {
+    naive_trie() : has_value(false), value(0) {
+    }
+    void insert(const char * key, size_t len, int32_t value = 0) {
+        if (len == 0) {
+            this->has_value = true;
+            this->value = value;
+            return;
+        }
+        char c = key[0];
+        auto res = children.find(c);
+        if (res != children.end()) {
+            res->second.insert(key + 1, len - 1, value);
+        } else {
+            auto res = children.insert(std::make_pair(c, naive_trie()));
+            res.first->second.insert(key + 1, len - 1, value);
+        }
+    }
+    std::pair<const char *, size_t> get_longest_prefix(const char * key, size_t len, size_t offset = 0) {
+        if (len == 0 || offset == len) {
+            return std::make_pair(key, offset);
+        }
+        char c = key[offset];
+        auto res = children.find(c);
+        if (res != children.end()) {
+            return res->second.get_longest_prefix(key, len, offset + 1);
+        } else {
+            return std::make_pair(key, offset);
+        }
+    }
+    struct naive_trie * traverse(const char c) {
+        auto res = children.find(c);
+        if (res != children.end()) {
+            return &res->second;
+        } else {
+            return NULL;
+        }
+    }
+    std::map<char, struct naive_trie> children;
+    bool has_value;
+    llama_token value;
+};
+
+//
+// impl
+//
+
+int llama_vocab::find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
+    GGML_ASSERT(token_left.find(' ')   == std::string::npos);
+    GGML_ASSERT(token_left.find('\n')  == std::string::npos);
+    GGML_ASSERT(token_right.find(' ')  == std::string::npos);
+    GGML_ASSERT(token_right.find('\n') == std::string::npos);
+
+    auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
+    if (it == bpe_ranks.end()) {
+        return -1;
+    }
+
+    return it->second;
+}
+
+static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
+    return vocab.type;
+}
+
+static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL;
+}
+
+static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN;
+}
+
+static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL;
+}
+
+static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE;
+}
+
+static bool llama_is_user_defined_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
+}
+
+static bool llama_is_unused_token(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNUSED;
+}
+
+static uint8_t llama_token_to_byte(const llama_vocab & vocab, llama_token id) {
+    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
+    GGML_ASSERT(llama_is_byte_token(vocab, id));
+    const auto & token_data = vocab.id_to_token.at(id);
+    switch (llama_vocab_get_type(vocab)) {
+        case LLAMA_VOCAB_TYPE_SPM:
+        case LLAMA_VOCAB_TYPE_UGM: {
+            auto buf = token_data.text.substr(3, 2);
+            return strtol(buf.c_str(), NULL, 16);
+        }
+        case LLAMA_VOCAB_TYPE_BPE: {
+            GGML_ASSERT(false);
+            return unicode_utf8_to_byte(token_data.text); // TODO: why is this here after GGML_ASSERT?
+        }
+        case LLAMA_VOCAB_TYPE_WPM: {
+            GGML_ASSERT(false);
+        }
+        default:
+            GGML_ASSERT(false);
+    }
+}
+
+static void llama_escape_whitespace(std::string & text) {
+    replace_all(text, " ", "\xe2\x96\x81");
+}
+
+static void llama_unescape_whitespace(std::string & word) {
+    replace_all(word, "\xe2\x96\x81", " ");
+}
+
+struct llm_symbol {
+    using index = int;
+    index prev;
+    index next;
+    const char * text;
+    size_t n;
+};
+
+static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");
+
+//
+// SPM tokenizer
+// original implementation:
+// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
+//
+
+struct llm_bigram_spm {
+    struct comparator {
+        bool operator()(llm_bigram_spm & l, llm_bigram_spm & r) {
+            return (l.score < r.score) || (l.score == r.score && l.left > r.left);
+        }
+    };
+    using queue_storage = std::vector<llm_bigram_spm>;
+    using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
+    llm_symbol::index left;
+    llm_symbol::index right;
+    float score;
+    size_t size;
+};
+
+struct llm_tokenizer_spm {
+    llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {}
+
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+        // split string into utf8 chars
+        int index = 0;
+        size_t offs = 0;
+        while (offs < text.size()) {
+            llm_symbol sym;
+            size_t len = unicode_len_utf8(text[offs]);
+            sym.text = text.c_str() + offs;
+            sym.n = std::min(len, text.size() - offs);
+            offs += sym.n;
+            sym.prev = index - 1;
+            sym.next = offs == text.size() ? -1 : index + 1;
+            index++;
+            symbols.emplace_back(sym);
+        }
+
+        // seed the work queue with all possible 2-character tokens.
+        for (size_t i = 1; i < symbols.size(); ++i) {
+            try_add_bigram(i - 1, i);
+        }
+
+        // keep substituting the highest frequency pairs for as long as we can.
+        while (!work_queue.empty()) {
+            auto bigram = work_queue.top();
+            work_queue.pop();
+
+            auto & left_sym = symbols[bigram.left];
+            auto & right_sym = symbols[bigram.right];
+
+            // if one of the symbols already got merged, skip it.
+            if (left_sym.n == 0 || right_sym.n == 0 ||
+                left_sym.n + right_sym.n != bigram.size) {
+                continue;
+            }
+
+            // merge the right sym into the left one
+            left_sym.n += right_sym.n;
+            right_sym.n = 0;
+
+            //LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
+
+            // remove the right sym from the chain
+            left_sym.next = right_sym.next;
+            if (right_sym.next >= 0) {
+                symbols[right_sym.next].prev = bigram.left;
+            }
+
+            // find more substitutions
+            try_add_bigram(left_sym.prev, bigram.left);
+            try_add_bigram(bigram.left, left_sym.next);
+        }
+
+        for (int i = 0; i != -1; i = symbols[i].next) {
+            auto & symbol = symbols[i];
+            resegment(symbol, output);
+        }
+    }
+
+private:
+    void resegment(llm_symbol & symbol, std::vector<llama_vocab::id> & output) {
+        auto text = std::string(symbol.text, symbol.n);
+        auto token = vocab.token_to_id.find(text);
+
+        // Do we need to support is_unused?
+        if (token != vocab.token_to_id.end()) {
+            output.push_back((*token).second);
+            return;
+        }
+
+        const auto p = rev_merge.find(text);
+
+        if (p == rev_merge.end()) {
+            // output any symbols that did not form tokens as bytes.
+            output.reserve(output.size() + symbol.n);
+            for (int j = 0; j < (int)symbol.n; ++j) {
+                llama_vocab::id token_id = llama_byte_to_token_impl(vocab, symbol.text[j]);
+                output.push_back(token_id);
+            }
+            return;
+        }
+
+        resegment(symbols[p->second.first],  output);
+        resegment(symbols[p->second.second], output);
+    }
+
+    void try_add_bigram(int left, int right) {
+        if (left == -1 || right == -1) {
+            return;
+        }
+
+        const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
+        auto token = vocab.token_to_id.find(text);
+
+        if (token == vocab.token_to_id.end()) {
+            return;
+        }
+
+        if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
+            return;
+        }
+
+        const auto & tok_data = vocab.id_to_token[(*token).second];
+
+        llm_bigram_spm bigram;
+        bigram.left  = left;
+        bigram.right = right;
+        bigram.score = tok_data.score;
+        bigram.size  = text.size();
+
+        work_queue.push(bigram);
+
+        // Do we need to support is_unused?
+        rev_merge[text] = std::make_pair(left, right);
+    }
+
+    const llama_vocab & vocab;
+
+    std::vector<llm_symbol> symbols;
+    llm_bigram_spm::queue work_queue;
+
+    std::map<std::string, std::pair<int, int>> rev_merge;
+};
+
+//
+// BPE tokenizer
+// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
+// tried to simplify unicode stuff, so most likely does not work 100% correctly!
+//
+
+// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused
+
+struct llm_bigram_bpe {
+    struct comparator {
+        bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
+            return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
+        }
+    };
+
+    using queue_storage = std::vector<llm_bigram_bpe>;
+    using queue = std::priority_queue<llm_bigram_bpe, queue_storage, comparator>;
+    llm_symbol::index left;
+    llm_symbol::index right;
+    std::string text;
+    int rank;
+    size_t size;
+};
+
+struct llm_tokenizer_bpe {
+    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {
+        GGML_ASSERT(vocab.type == LLAMA_VOCAB_TYPE_BPE);
+        switch (vocab.type_pre) {
+            case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
+                regex_exprs = {
+                    // original regex from tokenizer.json
+                    //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+
+                    // adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_DBRX:
+            case LLAMA_VOCAB_PRE_TYPE_SMAUG:
+                regex_exprs = {
+                    // same as llama3
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM:
+                regex_exprs = {
+                    "[\r\n]",
+                    "\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿDŽ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿff-stﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+",
+                    "\\s?[!-/:-~！-／：-～‘-‟　-。]+",
+                    "\\s+$",
+                    "[一-龥ࠀ-一가-퟿]+",
+                    "\\p{N}+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
+                regex_exprs = {
+                    "[\r\n]",
+                    "\\s?\\p{L}+",
+                    "\\s?\\p{P}+",
+                    "[一-龥ࠀ-一가-퟿]+",
+                    "\\p{N}",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_FALCON:
+                regex_exprs = {
+                    "[\\p{P}\\$\\+<=>\\^~\\|`]+",
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                    "[0-9][0-9][0-9]",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_STARCODER:
+            case LLAMA_VOCAB_PRE_TYPE_REFACT:
+            case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
+            case LLAMA_VOCAB_PRE_TYPE_SMOLLM:
+            case LLAMA_VOCAB_PRE_TYPE_CODESHELL:
+                regex_exprs = {
+                    "\\p{N}",
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_GPT2:
+            case LLAMA_VOCAB_PRE_TYPE_MPT:
+            case LLAMA_VOCAB_PRE_TYPE_OLMO:
+            case LLAMA_VOCAB_PRE_TYPE_JAIS:
+                regex_exprs = {
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
+            case LLAMA_VOCAB_PRE_TYPE_QWEN2:
+                regex_exprs = {
+                    // original regex from tokenizer.json
+                    // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_PORO:
+                regex_exprs = {
+                    " ?[^(\\s|.,!?…。，、।۔،)]+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_CHATGLM4:
+                regex_exprs = {
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_VIKING:
+                regex_exprs = {
+                    " ?[^(\\s|.,!?…。，、।۔،)]+",
+                    "\\p{N}",
+                };
+                break;
+            case LLAMA_VOCAB_PRE_TYPE_TEKKEN:
+                // original regex from tokenizer.json
+                // "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+                regex_exprs = {
+                    "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
+            default:
+                // default regex for BPE tokenization pre-processing
+                regex_exprs = {
+                    "[\\p{P}\\$\\+<=>\\^~\\|]+",
+                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
+                    "\\p{N}+",
+                    "[0-9][0-9][0-9]",
+                };
+                break;
+        }
+    }
+
+    void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output) const {
+        output.push_back(token_id);
+    }
+
+    bool append_bos(std::vector<llama_vocab::id> & output) const {
+        if (vocab.tokenizer_add_bos) {
+            GGML_ASSERT(vocab.special_bos_id != -1);
+            output.push_back(vocab.special_bos_id);
+            return true;
+        }
+        return false;
+    }
+
+    bool append_eos(std::vector<llama_vocab::id> & output) const {
+        if (vocab.tokenizer_add_eos) {
+            GGML_ASSERT(vocab.special_eos_id != -1);
+            output.push_back(vocab.special_eos_id);
+            return true;
+        }
+        return false;
+    }
+
+    void check_double_bos_eos(const std::vector<llama_vocab::id> & output) const {
+        if (vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+            LLAMA_LOG_WARN(
+                "%s: Added a BOS token to the prompt as specified by the model but the prompt "
+                "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
+                "Are you sure this is what you want?\n", __FUNCTION__);
+        }
+        if (vocab.tokenizer_add_eos && output.size() >= 2 && *(output.end()-2) == vocab.special_eos_id) {
+            LLAMA_LOG_WARN(
+                "%s: Added a EOS token to the prompt as specified by the model but the prompt "
+                "also ends with a EOS token. So now the final prompt ends with 2 EOS tokens. "
+                "Are you sure this is what you want?\n", __FUNCTION__);
+        }
+    }
+
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+        int final_prev_index = -1;
+
+        const auto word_collection = unicode_regex_split(text, regex_exprs);
+
+        symbols_final.clear();
+
+        for (auto & word : word_collection) {
+            work_queue = llm_bigram_bpe::queue();
+            symbols.clear();
+
+            int index = 0;
+            size_t offset = 0;
+
+            if (vocab.tokenizer_ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) {
+                symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()});
+                offset = word.size();
+            }
+
+            while (offset < word.size()) {
+                llm_symbol sym;
+                size_t char_len = std::min(word.size() - offset, (size_t) unicode_len_utf8(word[offset]));
+                sym.text = word.c_str() + offset;
+                sym.n = char_len;
+                offset += sym.n;
+                sym.prev = index - 1;
+                sym.next = offset == word.size() ? -1 : index + 1;
+                index++;
+                symbols.emplace_back(sym);
+            }
+            for (size_t i = 1; i < symbols.size(); ++i) {
+                add_new_bigram(i - 1, i);
+            }
+
+            // build token(s)
+            while (!work_queue.empty()) {
+                auto bigram = work_queue.top();
+                work_queue.pop();
+
+                auto & left_symbol = symbols[bigram.left];
+                auto & right_symbol = symbols[bigram.right];
+
+                if (left_symbol.n == 0 || right_symbol.n == 0) {
+                    continue;
+                }
+                std::string left_token = std::string(left_symbol.text, left_symbol.n);
+                std::string right_token = std::string(right_symbol.text, right_symbol.n);
+                if (left_token + right_token != bigram.text) {
+                    continue;  // Skip this bigram if it's outdated
+                }
+
+                // merge the right sym into the left one
+                left_symbol.n += right_symbol.n;
+                right_symbol.n = 0;
+
+                // remove the right sym from the chain
+                left_symbol.next = right_symbol.next;
+                if (right_symbol.next >= 0) {
+                    symbols[right_symbol.next].prev = bigram.left;
+                }
+
+                add_new_bigram(left_symbol.prev, bigram.left);  // left side of current symbol
+                add_new_bigram(bigram.left, left_symbol.next);  // right side of current symbol
+            }
+
+            // add the finished tokens to the final list keeping correct order for next and prev
+            for (auto & sym : symbols) {
+                if (sym.n > 0) {
+                    sym.prev = final_prev_index;
+                    sym.next = -1;
+                    if (final_prev_index != -1) {
+                        symbols_final[final_prev_index].next = symbols_final.size();
+                    }
+                    symbols_final.emplace_back(sym);
+                    final_prev_index = symbols_final.size() - 1;
+                }
+            }
+        }
+
+        symbols = symbols_final;
+
+        if (!symbols.empty()) {
+            for (int i = 0; i != -1; i = symbols[i].next) {
+                auto & symbol = symbols[i];
+                if (symbol.n == 0) {
+                    continue;
+                }
+
+                const std::string str = std::string(symbol.text, symbol.n);
+                const auto token = vocab.token_to_id.find(str);
+
+                if (token == vocab.token_to_id.end()) {
+                    for (auto j = str.begin(); j != str.end(); ++j) {
+                        std::string byte_str(1, *j);
+                        auto token_multibyte = vocab.token_to_id.find(byte_str);
+                        if (token_multibyte != vocab.token_to_id.end()) {
+                            output.push_back(token_multibyte->second);
+                        }
+                    }
+                } else {
+                    output.push_back((*token).second);
+                }
+            }
+        }
+    }
+
+private:
+    void add_new_bigram(int left, int right) {
+        if (left == -1 || right == -1) {
+            return;
+        }
+
+        std::string left_token  = std::string(symbols[left].text,  symbols[left].n);
+        std::string right_token = std::string(symbols[right].text, symbols[right].n);
+
+        int rank_found = -1;
+
+        rank_found = vocab.find_bpe_rank(left_token, right_token);
+
+        if (rank_found < 0) {
+            return;
+        }
+
+        llm_bigram_bpe bigram;
+
+        bigram.left  = left;
+        bigram.right = right;
+        bigram.text  = left_token + right_token;
+        bigram.size  = left_token.size() + right_token.size();
+        bigram.rank  = rank_found;
+
+        work_queue.push(bigram);
+    }
+
+    const llama_vocab & vocab;
+
+    std::vector<std::string> regex_exprs;
+
+    std::vector<llm_symbol> symbols;
+    std::vector<llm_symbol> symbols_final;
+
+    llm_bigram_bpe::queue work_queue;
+};
+
+//
+// WPM tokenizer
+//
+
+struct llm_tokenizer_wpm {
+    llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
+
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) const {
+        const auto & token_map = vocab.token_to_id;
+
+        // normalize and split by whitespace
+        std::vector<std::string> words = preprocess(text);
+
+        // bos token prepended already
+
+        // find the longest tokens that form the words
+        for (const std::string & word : words) {
+            // skip empty words
+            if (word.size() == 0) {
+                continue;
+            }
+
+            // prepend phantom space
+            const std::string word1 = "\xe2\x96\x81" + word;
+            const int n = word1.size();
+
+            const size_t current_tokens = output.size();
+
+            // we're at the start of a new word
+            // move through character position in word
+            for (int i = 0; i < n; ++i) {
+                // loop through possible match length
+                bool match = false;
+                for (int j = std::min(n, i + vocab.max_token_len + 1); j > i; j--) {
+                    auto it = token_map.find(word1.substr(i, j - i));
+                    if (it != token_map.end()) {
+                        output.push_back(it->second);
+                        match = true;
+                        i = j - 1;
+                        break;
+                    }
+                }
+
+                if (!match) { // discard all
+                    output.resize(current_tokens);
+                    break;  // and discard next tokens
+                }
+            }
+
+            // we didn't find any matches for this word
+            if (current_tokens == output.size()) {
+                output.push_back(vocab.special_unk_id);
+            }
+        }
+    }
+
+    // TODO: reduce string copies by using cpts_offs array
+    std::vector<std::string> preprocess(const std::string & text) const {
+        const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
+        std::vector<std::string> words(1, "");
+
+        for (const uint32_t cpt : cpts_nfd) {
+            const auto flags = unicode_cpt_flags(cpt);
+
+            if (flags.is_whitespace) {
+                if (words.back().size()) {  // finish previous word if any
+                    words.emplace_back();
+                }
+                continue;
+            }
+
+            assert (!flags.is_separator);
+            if (cpt == 0 || cpt == 0xFFFD || flags.is_control) {
+                continue;
+            }
+
+            const std::string s = unicode_cpt_to_utf8(unicode_tolower(cpt));
+            if (flags.is_punctuation || ( cpt < 0x7F && flags.is_symbol ) || is_chinese_char(cpt)) {
+                if (words.back().size()) {  // finish previous word if any
+                    words.emplace_back();
+                }
+                words.back() = s;       // single char word
+                words.emplace_back();   // start a new word
+            } else {
+                words.back() += s;  // append char to word
+            }
+        }
+
+        if (!words.back().size()) {
+            words.pop_back();
+        }
+
+        return words;
+    }
+
+    static bool is_chinese_char(uint32_t cpt) {
+        return
+            (cpt >= 0x04E00 && cpt <= 0x09FFF) ||
+            (cpt >= 0x03400 && cpt <= 0x04DBF) ||
+            (cpt >= 0x20000 && cpt <= 0x2A6DF) ||
+            (cpt >= 0x2A700 && cpt <= 0x2B73F) ||
+            (cpt >= 0x2B740 && cpt <= 0x2B81F) ||
+            (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
+            (cpt >= 0x0F900 && cpt <= 0x0FAFF) ||
+            (cpt >= 0x2F800 && cpt <= 0x2FA1F);
+            //(cpt >= 0x3000  && cpt <= 0x303F)  ||
+            //(cpt >= 0xFF00  && cpt <= 0xFFEF);
+    }
+
+    const llama_vocab & vocab;
+};
+
+//
+// UGM tokenizer
+//
+
+struct llm_tokenizer_ugm {
+    llm_tokenizer_ugm(const llama_vocab & vocab) : vocab(vocab) {
+        if (vocab.precompiled_charsmap.size() > 0) {
+            size_t charsmap_offset = 0;
+
+            // First four bytes of precompiled_charsmap contains length of binary
+            // blob containing XOR-compressed compact double array (XCDA) entries
+            uint32_t xcda_blob_size = *(const uint32_t *) &vocab.precompiled_charsmap[0];
+            charsmap_offset += sizeof(xcda_blob_size);
+            if (xcda_blob_size + charsmap_offset >= vocab.precompiled_charsmap.size()) {
+                throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
+            }
+
+            // Next xcda_blob_size bytes contain entries of XOR-compressed compact
+            // double array (XCDA). Each entry is bit-packed into a 32-bit integer.
+            xcda_array = (const uint32_t *) &vocab.precompiled_charsmap[charsmap_offset];
+            xcda_array_size = xcda_blob_size / sizeof(uint32_t);
+            charsmap_offset += xcda_blob_size;
+
+            // Remaining bytes of precompiled charsmap contain null-terminated
+            // replacement strings for prefixes matched by the XCDA.
+            prefix_replacements = &vocab.precompiled_charsmap[charsmap_offset];
+            prefix_replacements_size = vocab.precompiled_charsmap.size() - charsmap_offset;
+        }
+
+        for (unsigned int id = 0; id < vocab.id_to_token.size(); ++id) {
+            const auto &token_data = vocab.id_to_token[id];
+
+            if (llama_is_normal_token(vocab, id)) {
+                min_score = std::min<float>(min_score, token_data.score);
+                max_score = std::max<float>(max_score, token_data.score);
+            }
+
+            if (llama_is_normal_token(vocab, id) ||
+                llama_is_user_defined_token(vocab, id) ||
+                llama_is_unused_token(vocab, id)) {
+                token_matcher.insert(token_data.text.data(), token_data.text.size(), id);
+            }
+
+            if (llama_is_user_defined_token(vocab, id)) {
+                user_defined_token_matcher.insert(token_data.text.data(), token_data.text.size());
+            }
+        }
+
+        unknown_token_score = min_score - unknown_token_score_penalty;
+    }
+
+    /* This implementation is based on SentencePiece optimized Viterbi algorithm for
+     * unigram language models. The general idea is to:
+     * - move along the input sequence in steps of one UTF code point,
+     * - at each step find all possible tokenizations of the prefix by
+     *   traversing the tokens trie,
+     * - for each tokenization store the best one so far (by higher score)
+     * - use the position in sequence after given token as an index to store
+     *   results
+     * - if there was no valid tokenization of the current UTF code point
+     *   then use unknown token with additional score penalty
+     * After processing the whole sequence we backtrack from the end to get
+     * the best tokenization.
+    */
+    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
+        // normalize the input first
+        std::string normalized;
+        normalize(text, &normalized);
+        size_t input_len = normalized.size();
+        if (input_len == 0) {
+            return;
+        }
+
+        // initialize score_sum to -FLT_MAX so it will be always lower than sums of token scores
+        std::vector<struct best_tokenization> tokenization_results(input_len + 1, {vocab.special_unk_id, 0, -FLT_MAX});
+        // at the beginning tokenization score is zero
+        tokenization_results[0] = { vocab.special_unk_id, 0, 0 };
+
+        for (size_t input_offset = 0; input_offset < input_len;) {
+            size_t prefix_offset = input_offset;
+            // calculate how many code units are in the currently processed UTF code point
+            size_t n_utf8_code_units = std::min<size_t>(unicode_len_utf8(normalized[input_offset]), input_len - input_offset);
+
+            // traverse the token matcher trie to find a matching token
+            bool single_codepoint_token_found = false;
+            const struct best_tokenization & current_best = tokenization_results[input_offset];
+            struct naive_trie * node  = token_matcher.traverse(normalized[prefix_offset++]);
+
+            while (prefix_offset <= input_len && node != NULL) {
+                // check if we found valid token in prefix
+                if (node->has_value) {
+                    // check if it corresponds to the whole UTF code point
+                    if (prefix_offset - input_offset == n_utf8_code_units) {
+                        single_codepoint_token_found = true;
+                    }
+                    llama_token token_id = node->value;
+                    const auto & token_data = vocab.id_to_token[token_id];
+
+                    // we set the user-defined token scores to 0 to make them more likely to be selected
+                    // (normal token scores are log probabilities, so they are negative)
+                    // score type is double here to make tokenization results exactly
+                    // the same as in the HF tokenizer using SentencePiece
+                    const double token_score = llama_is_user_defined_token(vocab, token_id) ? 0.0 : token_data.score;
+                    const double challenger_score = current_best.score_sum + token_score;
+                    struct best_tokenization & current_champ = tokenization_results[prefix_offset];
+                    if (challenger_score > current_champ.score_sum) {
+                        struct best_tokenization challenger = { token_id, input_offset, (float) challenger_score };
+                        current_champ = challenger;
+                    }
+                }
+                node = node->traverse(normalized[prefix_offset++]);
+            }
+
+            // if we didn't find a valid token corresponding to the whole UTF code point
+            // then use unknown token as the tokenization of this UTF code point
+            if (!single_codepoint_token_found) {
+                const double challenger_score = current_best.score_sum + unknown_token_score;
+                prefix_offset = input_offset + n_utf8_code_units;
+                struct best_tokenization & current_champ = tokenization_results[prefix_offset];
+                if (challenger_score > current_champ.score_sum) {
+                    struct best_tokenization challenger = { vocab.special_unk_id, input_offset, (float) challenger_score };
+                    current_champ = challenger;
+                }
+            }
+
+            // move to the next UTF code point
+            input_offset += n_utf8_code_units;
+        }
+
+        // now backtrack from the end to gather token ids of the best tokenization
+        // merge sequences of consecutive unknown tokens into single unknown tokens
+        bool is_prev_unknown = false;
+        for (struct best_tokenization & tokenization = tokenization_results[input_len]; ; tokenization = tokenization_results[tokenization.input_offset]) {
+            bool is_unknown = tokenization.token_id == vocab.special_unk_id;
+            if (!(is_prev_unknown && is_unknown)) {
+                output.push_back(tokenization.token_id);
+            }
+            if (tokenization.input_offset == 0) {
+                break;
+            }
+            is_prev_unknown = is_unknown;
+        }
+
+        // reverse the output since we added tokens starting from the end of the input
+        std::reverse(output.begin(), output.end());
+    }
+
+private:
+    const llama_vocab & vocab;
+
+    // helper structure for returning normalization results
+    struct normalization_result {
+        const char * normalized;
+        size_t normalized_len;
+        size_t consumed_input;
+    };
+
+    void normalize(const std::string& input, std::string * normalized) {
+        normalized->clear();
+        normalized->reserve(input.size() * 3);
+
+        const std::string space = vocab.tokenizer_escape_whitespaces ? escaped_space : " ";
+
+        bool shall_prepend_space = !vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
+        bool shall_append_space = vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
+        bool shall_merge_spaces = vocab.tokenizer_remove_extra_whitespaces;
+
+        bool is_space_prepended = false;
+        bool processing_non_ws = false;
+
+        size_t input_len = input.size();
+
+        for (size_t input_offset = 0; input_offset < input_len; ) {
+            auto norm_res = normalize_prefix(input, input_offset);
+            for (size_t i = 0; i < norm_res.normalized_len; i++) {
+                char c = norm_res.normalized[i];
+                if (c != ' ') {
+                    if (!processing_non_ws) {
+                        processing_non_ws = true;
+                        if ((shall_prepend_space && !is_space_prepended) || shall_merge_spaces) {
+                            normalized->append(space);
+                            is_space_prepended = true;
+                        }
+                    }
+                    normalized->push_back(c);
+                } else {
+                    if (processing_non_ws) {
+                        processing_non_ws = false;
+                    }
+                    if (!shall_merge_spaces) {
+                        normalized->append(space);
+                    }
+                }
+            }
+
+            input_offset += norm_res.consumed_input;
+        }
+
+        if (shall_append_space) {
+            normalized->append(space);
+        }
+    }
+
+    /*
+     * This structure is a view wrapper for XOR-compressed double array (XCDA)
+     * See Shunsuke Kanda (2018). Space- and Time-Efficient String Dictionaries.
+     * Eeach bit-packed entry contains:
+     * - BASE array value in bits 10-30
+     * - LCHECK array value in bits 0-7
+     * - LEAF array value in bit 9
+     * Entries containing indexes of replacement sequences have set bit 31
+     */
+    struct xcda_array_view {
+    public:
+        xcda_array_view(const uint32_t * xcda_array, size_t xcda_array_size) : xcda_array(xcda_array), xcda_array_size(xcda_array_size) {
+        }
+        uint32_t get_base(size_t index) {
+            uint32_t packed_node = get_node(index);
+            return (packed_node >> 10) << ((packed_node & (1U << 9)) >> 6);
+        }
+        uint32_t get_lcheck(size_t index) {
+            uint32_t packed_node = get_node(index);
+            return packed_node & ((1U << 31) | 0xff);
+        }
+        bool get_leaf(size_t index) {
+            uint32_t packed_node = get_node(index);
+            return (packed_node >> 8) & 1;
+        }
+        uint32_t get_value(size_t index) {
+            uint32_t packed_node = get_node(index);
+            return packed_node & ((1U << 31) - 1);
+        }
+    private:
+        uint32_t get_node(size_t index) {
+            if (index > xcda_array_size) {
+                throw std::runtime_error("Index out of array bounds in XCDA array!");
+            }
+            return xcda_array[index];
+        }
+        const uint32_t * xcda_array;
+        size_t xcda_array_size;
+    };
+
+    struct normalization_result normalize_prefix(const std::string & input, size_t input_offset) {
+        if (input_offset == input.size()) {
+            return { &input[input_offset], 0, 0 };
+        }
+
+        // if input prefix matches some user-defined token return this token as normalization result
+        auto user_defined_token_match = user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset);
+        if (user_defined_token_match.second > 0) {
+            return { &input[input_offset], user_defined_token_match.second, user_defined_token_match.second };
+        }
+
+        size_t longest_prefix_length = 0;
+        size_t longest_prefix_offset = 0;
+
+        if (xcda_array_size > 0) {
+            struct xcda_array_view xcda_view(xcda_array, xcda_array_size);
+
+            // Find the longest normalized sequence matching the input prefix by walking
+            // the XOR-compressed compact double array (XCDA) starting from the root node
+            // We find the index of the next node by calculating BASE[s] ^ c where s is
+            // the index of the previous node and c is a numerical character value
+            uint32_t node_index = 0;
+            // get BASE of the root node
+            node_index = xcda_view.get_base(node_index);
+            for (size_t prefix_offset = input_offset; prefix_offset < input.size(); prefix_offset++) {
+                unsigned char c = input[prefix_offset];
+                if (c == 0) {
+                    break;
+                }
+                node_index ^= c;
+                // if value of LCHECK is not c it means that this is not a child of
+                // the previous node, so we stop matching
+                if (xcda_view.get_lcheck(node_index) != c) {
+                    break;
+                }
+                bool is_leaf = xcda_view.get_leaf(node_index);
+                // get BASE of the current node
+                node_index ^= xcda_view.get_base(node_index);
+                // if LEAF of the current node is true, it means that its BASE points to the node
+                // containing index of replacement sequence for currently matched input prefix
+                if (is_leaf)
+                {
+                    longest_prefix_length = prefix_offset - input_offset + 1;
+                    // get index of replacement sequence for currently matched input prefix
+                    longest_prefix_offset = xcda_view.get_value(node_index);
+                }
+            }
+        }
+
+        if (longest_prefix_length > 0) {
+            // we have a match, so return the replacement sequence
+            if (longest_prefix_offset >= prefix_replacements_size) {
+                throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
+            }
+            const char * prefix_replacement = &prefix_replacements[longest_prefix_offset];
+            return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length };
+        } else {
+            // check if the input prefix contains a valid sequence of UTF-8 code units
+            try {
+                // if yes, return this sequence unmodified
+                size_t prefix_offset = input_offset;
+                unicode_cpt_from_utf8(input, prefix_offset);
+                return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset };
+            } catch (std::invalid_argument & /*ex*/) {
+                // if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER
+                return { "\xEF\xBF\xBD", 3, 1 };
+            }
+        }
+    }
+
+    // escaped space symbol - U+2581 (Lower One Eighth Block)
+    const std::string escaped_space = "\xE2\x96\x81";
+
+    const char * prefix_replacements = NULL;
+    size_t prefix_replacements_size = 0;
+
+    const uint32_t * xcda_array = NULL;
+    size_t xcda_array_size = 0;
+
+    struct naive_trie user_defined_token_matcher;
+
+    // this structure stores the best tokenization so far at input_offset
+    struct best_tokenization {
+        llama_token token_id;
+        size_t input_offset;
+        float score_sum;
+    };
+
+    float min_score = FLT_MAX;
+    float max_score = -FLT_MAX;
+
+    float unknown_token_score_penalty = 10.0;
+    float unknown_token_score;
+
+    struct naive_trie token_matcher;
+};
+
+//
+// (de-) tokenize
+//
+
+typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
+    FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
+    FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
+} FRAGMENT_BUFFER_VARIANT_TYPE;
+
+struct fragment_buffer_variant {
+    fragment_buffer_variant(llama_vocab::id _token)
+    :
+        type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
+        token(_token),
+        raw_text(_dummy),
+        offset(0),
+        length(0) {}
+
+    fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
+    :
+        type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
+        token((llama_vocab::id) - 1),
+        raw_text(_raw_text),
+        offset(_offset),
+        length(_length){
+            GGML_ASSERT(_offset >= 0);
+            GGML_ASSERT(_length >= 1);
+            GGML_ASSERT(offset + length <= raw_text.length());
+        }
+
+    const FRAGMENT_BUFFER_VARIANT_TYPE type;
+    const llama_vocab::id token;
+    const std::string _dummy;
+    const std::string & raw_text;
+    const uint64_t offset;
+    const uint64_t length;
+};
+
+// #define PRETOKENIZERDEBUG
+
+static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) {
+    // for each special token
+    for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
+        const auto & data = vocab.id_to_token[special_id];
+        const auto & special_token = data.text;
+
+        if (!parse_special && (data.attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_UNKNOWN))) {
+            // Ignore control and unknown tokens when parse_special == false
+            continue;
+            // User-defined tokens are still pre-tokenized before everything else
+            // ref: https://github.com/huggingface/tokenizers/blob/fdd26ba9a3f0c133427aab0423888cbde91362d7/tokenizers/src/tokenizer/mod.rs#L726
+            // This is mostly relevant for neox-style tokenizers (mpt, olmo, stablelm, etc.)
+        }
+
+        // for each text fragment
+        std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
+        while (it != buffer.end()) {
+            auto & fragment = (*it);
+
+            // if a fragment is text ( not yet processed )
+            if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                auto & raw_text = fragment.raw_text;
+
+                auto raw_text_base_offset = fragment.offset;
+                auto raw_text_base_length = fragment.length;
+
+                // loop over the text
+                while (true) {
+                    // find the first occurrence of a given special token in this fragment
+                    //  passing offset argument only limit the "search area" but match coordinates
+                    //  are still relative to the source full raw_text
+                    auto match = raw_text.find(special_token, raw_text_base_offset);
+
+                    // no occurrences found, stop processing this fragment for a given special token
+                    if (match == std::string::npos) break;
+
+                    // check if match is within bounds of offset <-> length
+                    if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;
+
+#ifdef PRETOKENIZERDEBUG
+                    LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
+#endif
+                    auto source = std::distance(buffer.begin(), it);
+
+                    // if match is further than base offset
+                    //  then we have some text to the left of it
+                    if (match > raw_text_base_offset) {
+                        // left
+                        const int64_t left_reminder_offset = raw_text_base_offset + 0;
+                        int64_t left_reminder_length = match - raw_text_base_offset;
+
+                        if (data.attr & LLAMA_TOKEN_ATTR_LSTRIP) {
+                            while (left_reminder_length > 0 && isspace(raw_text[left_reminder_offset + left_reminder_length - 1])) {
+                                left_reminder_length--;
+                            }
+                        }
+
+                        if (left_reminder_length > 0) {
+                            buffer.emplace_after(it, raw_text, left_reminder_offset, left_reminder_length);
+                            it++;
+                        }
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
+#endif
+                    }
+
+                    // special token
+                    buffer.emplace_after(it, special_id);
+                    it++;
+
+                    // right
+                    if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
+                        int64_t right_reminder_offset = match + special_token.length();
+                        int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
+
+                        if (data.attr & LLAMA_TOKEN_ATTR_RSTRIP) {
+                            while (right_reminder_length > 0 && isspace(raw_text[right_reminder_offset])) {
+                                right_reminder_offset++;
+                                right_reminder_length--;
+                            }
+                        }
+
+                        if (right_reminder_length > 0) {
+                            buffer.emplace_after(it, raw_text, right_reminder_offset, right_reminder_length);
+                            it++;
+                        }
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
+#endif
+
+                        if (source == 0) {
+                            buffer.erase_after(buffer.before_begin());
+                        } else {
+                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
+                        }
+
+                        // repeat for the right side
+                        raw_text_base_offset = right_reminder_offset;
+                        raw_text_base_length = right_reminder_length;
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
+#endif
+                    } else {
+                        if (source == 0) {
+                            buffer.erase_after(buffer.before_begin());
+                        } else {
+                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
+                        }
+                        break;
+                    }
+                }
+            }
+            it++;
+        }
+    }
+}
+
+std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) {
+    std::vector<llama_vocab::id> output;
+    std::forward_list<fragment_buffer_variant> fragment_buffer;
+
+    if (!raw_text.empty()) {
+        fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
+        tokenizer_st_partition(vocab, fragment_buffer, parse_special);
+    }
+
+    switch (vocab.type) {
+        case LLAMA_VOCAB_TYPE_SPM:
+            {
+                // OG tokenizer behavior:
+                //
+                // tokenizer.encode('', add_special_tokens=True)  returns [1]
+                // tokenizer.encode('', add_special_tokens=False) returns []
+
+                bool is_prev_special = true;  // prefix with space if first token
+
+                if (add_special && vocab.tokenizer_add_bos) {
+                    GGML_ASSERT(vocab.special_bos_id != -1);
+                    output.push_back(vocab.special_bos_id);
+                    is_prev_special = true;
+                }
+
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+                        // prefix with space if previous is special
+                        if (vocab.tokenizer_add_space_prefix && is_prev_special) {
+                            raw_text = " " + raw_text;
+                        }
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        llm_tokenizer_spm tokenizer(vocab);
+                        llama_escape_whitespace(raw_text);
+                        tokenizer.tokenize(raw_text, output);
+                        is_prev_special = false;
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                        is_prev_special = true;
+                    }
+                }
+
+                if (add_special && vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+                    LLAMA_LOG_WARN(
+                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
+                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
+                        "Are you sure this is what you want?\n", __FUNCTION__);
+                }
+
+                if (add_special && vocab.tokenizer_add_eos) {
+                    GGML_ASSERT(vocab.special_eos_id != -1);
+                    output.push_back(vocab.special_eos_id);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_BPE:
+            {
+                llm_tokenizer_bpe tokenizer(vocab);
+
+                if (add_special) {
+                    tokenizer.append_bos(output);
+                }
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        tokenizer.tokenize(raw_text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        tokenizer.append(fragment.token, output);
+                    }
+                }
+
+                if (add_special) {
+                    tokenizer.append_eos(output);
+                    tokenizer.check_double_bos_eos(output);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_WPM:
+            {
+                if (add_special) {
+                    GGML_ASSERT(vocab.special_cls_id != -1);
+                    output.push_back(vocab.special_cls_id);
+                }
+
+                llm_tokenizer_wpm tokenizer(vocab);
+
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        tokenizer.tokenize(raw_text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                    }
+                }
+
+                if (add_special) {
+                    GGML_ASSERT(vocab.special_sep_id != -1);
+                    output.push_back(vocab.special_sep_id);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_UGM:
+            {
+                llm_tokenizer_ugm tokenizer(vocab);
+
+                if (add_special && vocab.tokenizer_add_bos != 0) {
+                    GGML_ASSERT(vocab.special_bos_id != -1);
+                    output.push_back(vocab.special_bos_id);
+                }
+
+                for (const auto & fragment : fragment_buffer) {
+                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
+                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
+#ifdef PRETOKENIZERDEBUG
+                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
+#endif
+                        tokenizer.tokenize(raw_text, output);
+                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
+                        output.push_back(fragment.token);
+                    }
+                }
+
+                if (add_special && vocab.tokenizer_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) {
+                    LLAMA_LOG_WARN(
+                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
+                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
+                        "Are you sure this is what you want?\n", __FUNCTION__);
+                }
+
+                if (add_special && vocab.tokenizer_add_eos == 1) {
+                    GGML_ASSERT(vocab.special_eos_id != -1);
+                    output.push_back(vocab.special_eos_id);
+                }
+            } break;
+        case LLAMA_VOCAB_TYPE_NONE:
+            GGML_ASSERT(false);
+    }
+
+    return output;
+}
+
+llama_token llama_byte_to_token_impl(const llama_vocab & vocab, uint8_t ch) {
+    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
+    static const char * hex = "0123456789ABCDEF";
+    switch (llama_vocab_get_type(vocab)) {
+        case LLAMA_VOCAB_TYPE_SPM:
+        case LLAMA_VOCAB_TYPE_UGM: {
+            const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
+            auto token = vocab.token_to_id.find(buf);
+            if (token != vocab.token_to_id.end()) {
+                return (*token).second;
+            }
+            // Try to fall back to just the byte as a string
+            const char buf2[2] = { (char)ch, 0 };
+            return vocab.token_to_id.at(buf2);
+        }
+        case LLAMA_VOCAB_TYPE_WPM:
+        case LLAMA_VOCAB_TYPE_BPE: {
+            return vocab.token_to_id.at(unicode_byte_to_utf8(ch));
+        }
+        default:
+            GGML_ASSERT(false);
+    }
+}
+
+const char * llama_token_get_text_impl(const struct llama_vocab & vocab, llama_token token) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[token].text.c_str();
+}
+
+float llama_token_get_score_impl(const struct llama_vocab & vocab, llama_token token) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[token].score;
+}
+
+llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, llama_token token) {
+    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
+    return vocab.id_to_token[token].attr;
+}
+
+bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token) {
+    return token != -1 && (
+        token == llama_token_eos_impl(vocab) ||
+        token == llama_token_eot_impl(vocab)
+    );
+}
+
+bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token) {
+    return llama_is_control_token(vocab, token);
+}
+
+llama_token llama_token_bos_impl(const struct llama_vocab & vocab) {
+    return vocab.special_bos_id;
+}
+
+llama_token llama_token_eos_impl(const struct llama_vocab & vocab) {
+    return vocab.special_eos_id;
+}
+
+llama_token llama_token_cls_impl(const struct llama_vocab & vocab) {
+    return vocab.special_cls_id;
+}
+
+llama_token llama_token_sep_impl(const struct llama_vocab & vocab) {
+    return vocab.special_sep_id;
+}
+
+llama_token llama_token_nl_impl(const struct llama_vocab & vocab) {
+    return vocab.linefeed_id;
+}
+
+llama_token llama_token_pad_impl(const struct llama_vocab & vocab) {
+    return vocab.special_pad_id;
+}
+
+int32_t llama_add_bos_token_impl(const struct llama_vocab & vocab) {
+    return vocab.tokenizer_add_bos;
+}
+
+int32_t llama_add_eos_token_impl(const struct llama_vocab & vocab) {
+    return vocab.tokenizer_add_eos;
+}
+
+llama_token llama_token_prefix_impl(const struct llama_vocab & vocab) {
+    return vocab.special_prefix_id;
+}
+
+llama_token llama_token_middle_impl(const struct llama_vocab & vocab) {
+    return vocab.special_middle_id;
+}
+
+llama_token llama_token_suffix_impl(const struct llama_vocab & vocab) {
+    return vocab.special_suffix_id;
+}
+
+llama_token llama_token_eot_impl(const struct llama_vocab & vocab) {
+    return vocab.special_eot_id;
+}
+
+int32_t llama_tokenize_impl(
+    const struct llama_vocab & vocab,
+                  const char * text,
+                     int32_t   text_len,
+                 llama_token * tokens,
+                     int32_t   n_tokens_max,
+                        bool   add_special,
+                        bool   parse_special) {
+    auto res = llama_tokenize_internal(vocab, std::string(text, text_len), add_special, parse_special);
+    if (n_tokens_max < (int) res.size()) {
+        // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
+        return -((int) res.size());
+    }
+
+    for (size_t i = 0; i < res.size(); i++) {
+        tokens[i] = res[i];
+    }
+
+    return res.size();
+}
+
+static std::string llama_decode_text(const std::string & text) {
+    std::string decoded_text;
+
+    const auto cpts = unicode_cpts_from_utf8(text);
+    for (const auto cpt : cpts) {
+        const auto utf8 = unicode_cpt_to_utf8(cpt);
+        try {
+            decoded_text += unicode_utf8_to_byte(utf8);
+        } catch (const std::out_of_range & /*e*/) {
+            decoded_text += "[UNK_BYTE_0x";
+            for (const auto c : utf8) {
+                decoded_text += format("%02x", (uint8_t) c);
+            }
+            decoded_text += text + "]";
+        }
+    }
+
+    return decoded_text;
+}
+
+// does not write null-terminator to buf
+int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) {
+    // ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843
+    static const int attr_special = LLAMA_TOKEN_ATTR_UNKNOWN | LLAMA_TOKEN_ATTR_CONTROL;
+    const llama_token_attr attr = llama_token_get_attr_impl(vocab, token);
+    if (!special && (attr & attr_special)) {
+        return 0;
+    }
+
+    // copy piece chars to output text buffer
+    // skip up to 'lstrip' leading spaces before copying
+    auto _try_copy = [=] (const char * token, size_t size) -> int32_t {
+        for (int32_t i = 0; i < lstrip && size && *token == ' '; ++i) {
+            token++;
+            size--;
+        }
+        if (length < (int32_t)size) {
+            return -(int32_t) size;
+        }
+        memcpy(buf, token, size);
+        return (int32_t) size;
+    };
+
+    // if we have a cache - use it
+    {
+        const auto & cache = vocab.cache_token_to_piece;
+
+        if (!cache.empty()) {
+            const auto & result = cache.at(token);
+            return _try_copy(result.data(), result.size());
+        }
+    }
+
+    if (0 <= token && token < (int32_t) vocab.id_to_token.size()) {
+        const std::string & token_text = vocab.id_to_token[token].text;
+        switch (llama_vocab_get_type(vocab)) {
+            case LLAMA_VOCAB_TYPE_WPM:
+            case LLAMA_VOCAB_TYPE_SPM:
+            case LLAMA_VOCAB_TYPE_UGM: {
+                // NOTE: we accept all unsupported token types,
+                // suppressing them like CONTROL tokens.
+                if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
+                    return _try_copy(token_text.data(), token_text.size());
+                } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
+                    std::string result = token_text;
+                    llama_unescape_whitespace(result);
+                    return _try_copy(result.data(), result.size());
+                } else if (attr & LLAMA_TOKEN_ATTR_BYTE) {
+                    char byte = (char) llama_token_to_byte(vocab, token);
+                    return _try_copy((char*) &byte, 1);
+                }
+                break;
+            }
+            case LLAMA_VOCAB_TYPE_BPE: {
+                // NOTE: we accept all unsupported token types,
+                // suppressing them like CONTROL tokens.
+                if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
+                    return _try_copy(token_text.data(), token_text.size());
+                } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
+                    std::string result = llama_decode_text(token_text);
+                    return _try_copy(result.data(), result.size());
+                }
+                break;
+            }
+            default:
+                GGML_ASSERT(false);
+        }
+    }
+
+    return 0;
+}
+
+int32_t llama_detokenize_impl(
+        const struct llama_vocab & vocab,
+               const llama_token * tokens,
+                         int32_t   n_tokens,
+                            char * text,
+                         int32_t   text_len_max,
+                            bool   remove_special,
+                            bool   unparse_special) {
+    int32_t avail = text_len_max;
+    int32_t total = 0;
+
+    // remove the leading space
+    bool remove_space = vocab.tokenizer_add_space_prefix;
+
+    if (remove_special && vocab.tokenizer_add_bos) {
+        if (n_tokens > 0 && tokens[0] == vocab.special_bos_id) {
+            remove_space = false;
+            n_tokens--;
+            tokens++;
+        }
+    }
+
+    if (remove_special && vocab.tokenizer_add_eos) {
+        if (n_tokens > 0 && tokens[n_tokens-1] == vocab.special_eos_id) {
+            n_tokens--;
+        }
+    }
+
+    for (int32_t i = 0; i < n_tokens; ++i) {
+        GGML_ASSERT(avail >= 0);
+        int32_t n_chars = llama_token_to_piece_impl(vocab, tokens[i], text, avail, remove_space, unparse_special);
+        remove_space = false;
+        if (n_chars < 0) {
+            avail = 0;
+            total -= n_chars;
+        } else if (n_chars > 0) {
+            avail -= n_chars;
+            text  += n_chars;
+            total += n_chars;
+        }
+    }
+
+    if (total > text_len_max) {
+        return -total;
+    }
+
+    if (vocab.tokenizer_clean_spaces) {
+        text -= total;  // restart text
+
+        // first pass: characters ?!.,  //TODO: where do these characters come from?
+        const int32_t total1 = total;
+        total = total ? 1 : 0;
+        for (int32_t i = 1; i < total1; ++i) {
+            const char x = text[i];
+            if (text[i - 1] == ' ') {
+                if (x == '?' || x == '!' || x == '.' || x == ',') {  // " ?", " !", " .", " ,"
+                    total--;  // remove space
+                }
+            }
+            text[total++] = x;
+        }
+
+        // second pass: strip single apostrophe between spaces
+        const int32_t total2 = total;
+        total = total ? 1 : 0;
+        for (int32_t i = 1; i < total2; ++i) {
+            const char x = text[i];
+            if (x == '\'' && i + 1 < total2 && text[i - 1] == ' ' && text[i + 1] == ' ') {  // " ' "
+                total--;           // remove prev space
+                text[++i] = '\0';  // remove next space
+            }
+            text[total++] = x;
+        }
+
+        // third pass: apostrophe contractions  //NOTE: this makes sense?
+        const int32_t total3 = total;
+        total = total ? 1 : 0;
+        for (int32_t i = 1; i < total3; ++i) {
+            const char x = text[i];
+            if (text[i - 1] == ' ') {
+                if (x == '\'' && i + 1 < total3) {
+                    const char x1 = text[i + 1];
+                    if (x1 == 't' || x1 == 'd') {  // " 't", " 'd"
+                        //total--;  // remove space
+                    } else if (x1 == 's' || x1 == 'm') {  // " 's", " 'm"
+                        total--;  // remove space
+                    } else if (i + 2 < total3) {
+                        const char x2 = text[i + 2];
+                        if ((x1 == 'l' && x2 == 'l')) {  // " 'll"
+                            //total--;  // remove space
+                        } else if ((x1 == 'r' && x2 == 'e') || (x1 == 'v' && x2 == 'e')) {  // " 're", " 've"
+                            total--;  // remove space
+                        } else {
+                            //total--;  // remove space
+                        }
+                    } else {
+                        //total--;  // remove space
+                    }
+                }
+            }
+            text[total++] = x;
+        }
+    }
+
+    return total <= text_len_max ? total : -total;
+}
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
new file mode 100644
index 00000000000000..30b565d55dad5b
--- /dev/null
+++ b/src/llama-vocab.h
@@ -0,0 +1,130 @@
+#pragma once
+
+#include "llama-impl.h"
+
+#include <string>
+#include <vector>
+#include <unordered_map>
+#include <map>
+
+struct llama_vocab {
+    using id    = llama_token;
+    using token = std::string;
+    using tattr = llama_token_attr;
+
+    struct token_data {
+        token text;
+        float score;
+        tattr attr;
+    };
+
+    enum llama_vocab_type     type     = LLAMA_VOCAB_TYPE_SPM;
+    enum llama_vocab_pre_type type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+
+    int max_token_len = 0; // used for optimizing longest token search
+
+    std::unordered_map<token, id> token_to_id;
+    std::vector<token_data>       id_to_token;
+
+    std::vector<id>    cache_special_tokens;
+    std::vector<token> cache_token_to_piece; // llama_token_to_piece(special = true);
+
+    std::map<std::pair<std::string, std::string>, int> bpe_ranks;
+
+    // default LLaMA special tokens
+    id special_bos_id  = 1;
+    id special_eos_id  = 2;
+    id special_unk_id  = 0;
+    id special_sep_id  = -1;
+    id special_pad_id  = -1;
+    id special_cls_id  = -1;
+    id special_mask_id = -1;
+
+    id linefeed_id       = 13;
+    id special_prefix_id = -1;
+    id special_suffix_id = -1;
+    id special_middle_id = -1;
+    id special_eot_id    = -1; // TODO: move above after "eos_id", and here add "file separator" token
+
+    // tokenizer flags
+    bool tokenizer_add_space_prefix = false;
+    bool tokenizer_add_bos          = false;
+    bool tokenizer_add_eos          = false;
+    bool tokenizer_ignore_merges    = false;
+    bool tokenizer_clean_spaces     = false;  // clean_up_tokenization_spaces
+    bool tokenizer_remove_extra_whitespaces   = false;
+    bool tokenizer_escape_whitespaces         = true;
+    bool tokenizer_treat_whitespace_as_suffix = false;
+
+    std::vector<char> precompiled_charsmap;
+
+    int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
+};
+
+const struct llama_vocab * llama_get_vocab(const struct llama_context * ctx);
+
+//
+// internal API
+//
+
+// TODO: rename to llama_tokenize_impl
+// TODO: This should probably be in llama.h
+std::vector<llama_vocab::id> llama_tokenize_internal(
+        const llama_vocab & vocab,
+        std::string raw_text,
+        bool add_special,
+        bool parse_special = false);
+
+llama_token llama_byte_to_token_impl(const llama_vocab & vocab, uint8_t ch);
+
+const char * llama_token_get_text_impl(const struct llama_vocab & vocab, llama_token token);
+
+float llama_token_get_score_impl(const struct llama_vocab & vocab, llama_token token);
+
+llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, llama_token token);
+
+bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token);
+
+bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token);
+
+llama_token llama_token_bos_impl(const struct llama_vocab & vocab);
+llama_token llama_token_eos_impl(const struct llama_vocab & vocab);
+llama_token llama_token_cls_impl(const struct llama_vocab & vocab);
+llama_token llama_token_sep_impl(const struct llama_vocab & vocab);
+llama_token llama_token_nl_impl (const struct llama_vocab & vocab);
+llama_token llama_token_pad_impl(const struct llama_vocab & vocab);
+
+int32_t llama_add_bos_token_impl(const struct llama_vocab & vocab);
+int32_t llama_add_eos_token_impl(const struct llama_vocab & vocab);
+
+llama_token llama_token_prefix_impl(const struct llama_vocab & vocab);
+llama_token llama_token_middle_impl(const struct llama_vocab & vocab);
+llama_token llama_token_suffix_impl(const struct llama_vocab & vocab);
+llama_token llama_token_eot_impl   (const struct llama_vocab & vocab);
+
+int32_t llama_tokenize_impl(
+        const struct llama_vocab & vocab,
+                      const char * text,
+                         int32_t   text_len,
+                     llama_token * tokens,
+                         int32_t   n_tokens_max,
+                            bool   add_special,
+                            bool   parse_special);
+
+// does not write null-terminator to buf
+int32_t llama_token_to_piece_impl(
+        const struct llama_vocab & vocab,
+                     llama_token   token,
+                            char * buf,
+                         int32_t   length,
+                         int32_t   lstrip,
+                            bool   special);
+
+int32_t llama_detokenize_impl(
+        const struct llama_vocab & vocab,
+               const llama_token * tokens,
+                         int32_t   n_tokens,
+                            char * text,
+                         int32_t   text_len_max,
+                            bool   remove_special,
+                            bool   unparse_special);
diff --git a/src/llama.cpp b/src/llama.cpp
index 99a6d8b6651686..40c5e8e8d4b3be 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1,5 +1,7 @@
-#define LLAMA_API_INTERNAL
-#include "llama.h"
+#include "llama-impl.h"
+#include "llama-vocab.h"
+#include "llama-grammar.h"
+#include "llama-sampling.h"
 
 #include "unicode.h"
 
@@ -79,7 +81,6 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
-#include <forward_list>
 #include <fstream>
 #include <functional>
 #include <future>
@@ -89,9 +90,6 @@
 #include <memory>
 #include <mutex>
 #include <numeric>
-#include <queue>
-#include <random>
-#include <regex>
 #include <set>
 #include <sstream>
 #include <thread>
@@ -102,41 +100,26 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-#ifdef __GNUC__
-#ifdef __MINGW32__
-#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
-#else
-#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
-#endif
-#else
-#define LLAMA_ATTRIBUTE_FORMAT(...)
-#endif
-
 // bump if necessary
 #define LLAMA_MAX_NODES   8192
 #define LLAMA_MAX_LAYERS  512
 #define LLAMA_MAX_EXPERTS 160  // DeepSeekV2
 
-//
-// logging
-//
-
-LLAMA_ATTRIBUTE_FORMAT(2, 3)
-static void llama_log_internal        (ggml_log_level level, const char * format, ...);
-static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
-
-#define LLAMA_LOG_INFO(...)  llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
-#define LLAMA_LOG_WARN(...)  llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
-#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
-
 //
 // helpers
 //
 
-static size_t utf8_len(char src) {
-    const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
-    uint8_t highbits = static_cast<uint8_t>(src) >> 4;
-    return lookup[highbits];
+// trim whitespace from the beginning and end of a string
+static std::string trim(const std::string & str) {
+    size_t start = 0;
+    size_t end = str.size();
+    while (start < end && isspace(str[start])) {
+        start += 1;
+    }
+    while (end > start && isspace(str[end - 1])) {
+        end -= 1;
+    }
+    return str.substr(start, end - start);
 }
 
 static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
@@ -2583,72 +2566,6 @@ struct llama_control_vector {
     }
 };
 
-struct llama_vocab {
-    using id    = int32_t;
-    using token = std::string;
-    using tattr = llama_token_attr;
-
-    struct token_data {
-        token text;
-        float score;
-        tattr attr;
-    };
-
-    enum llama_vocab_type     type     = LLAMA_VOCAB_TYPE_SPM;
-    enum llama_vocab_pre_type type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
-
-    int max_token_len = 0; // used for optimizing longest token search
-
-    std::unordered_map<token, id> token_to_id;
-    std::vector<token_data>       id_to_token;
-
-    std::vector<id>    cache_special_tokens;
-    std::vector<token> cache_token_to_piece; // llama_token_to_piece(special = true);
-
-    std::map<std::pair<std::string, std::string>, int> bpe_ranks;
-
-    // default LLaMA special tokens
-    id special_bos_id  = 1;
-    id special_eos_id  = 2;
-    id special_unk_id  = 0;
-    id special_sep_id  = -1;
-    id special_pad_id  = -1;
-    id special_cls_id  = -1;
-    id special_mask_id = -1;
-
-    id linefeed_id       = 13;
-    id special_prefix_id = -1;
-    id special_suffix_id = -1;
-    id special_middle_id = -1;
-    id special_eot_id    = -1; // TODO: move above after "eos_id", and here add "file separator" token
-
-    // tokenizer flags
-    bool tokenizer_add_space_prefix = false;
-    bool tokenizer_add_bos          = false;
-    bool tokenizer_add_eos          = false;
-    bool tokenizer_ignore_merges    = false;
-    bool tokenizer_clean_spaces     = false;  // clean_up_tokenization_spaces
-    bool tokenizer_remove_extra_whitespaces   = false;
-    bool tokenizer_escape_whitespaces         = true;
-    bool tokenizer_treat_whitespace_as_suffix = false;
-
-    std::vector<char> precompiled_charsmap;
-
-    int find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
-        GGML_ASSERT(token_left.find(' ') == std::string::npos);
-        GGML_ASSERT(token_left.find('\n') == std::string::npos);
-        GGML_ASSERT(token_right.find(' ') == std::string::npos);
-        GGML_ASSERT(token_right.find('\n') == std::string::npos);
-
-        auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
-        if (it == bpe_ranks.end()) {
-            return -1;
-        }
-
-        return it->second;
-    }
-};
-
 struct llama_model {
     e_model     type  = MODEL_UNKNOWN;
     llm_arch    arch  = LLM_ARCH_UNKNOWN;
@@ -2737,7 +2654,13 @@ struct llama_model {
 };
 
 struct llama_context {
-    llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {}
+    llama_context(const llama_model & model)
+        : model(model)
+        , sampling(llama_n_vocab(&model))
+        , grammar()
+        , t_start_us(model.t_start_us)
+        , t_load_us(model.t_load_us) {}
+
     ~llama_context() {
         ggml_backend_sched_free(sched);
 
@@ -2748,7 +2671,15 @@ struct llama_context {
         ggml_backend_buffer_free(buf_output);
     }
 
-    llama_cparams cparams;
+    const struct llama_model & model;
+
+    struct llama_cparams        cparams;
+    struct llama_sampling       sampling;
+    struct llama_grammar        grammar;
+    struct llama_kv_cache       kv_self;
+    struct llama_control_vector cvec;
+
+    std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;
 
     std::vector<ggml_backend_t> backends;
 #ifdef GGML_USE_METAL
@@ -2759,26 +2690,16 @@ struct llama_context {
 #endif
     ggml_backend_t backend_cpu = nullptr;
 
-
-    const llama_model & model;
-
-    // key + value cache for the self attention
-    struct llama_kv_cache kv_self;
-
-    std::mt19937 rng;
-
     bool has_evaluated_once = false;
 
     int64_t t_start_us;
     int64_t t_load_us;
-    int64_t t_sample_us = 0;
     int64_t t_p_eval_us = 0;
     int64_t t_eval_us   = 0;
 
     int64_t t_compute_start_us = 0;
     int64_t n_queued_tokens = 0;
 
-    int32_t n_sample = 0; // number of tokens sampled
     int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
     int32_t n_eval   = 0; // number of eval calls
 
@@ -2834,12 +2755,6 @@ struct llama_context {
     struct ggml_tensor * inp_pos_bucket;    // I32 [n_batch|n_kv, n_batch]
     struct ggml_tensor * inp_embd_enc;      // F32 [n_embd, n_outputs_enc]
     struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
-
-    // control vectors
-    struct llama_control_vector cvec;
-
-    // lora adapters and scales
-    std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;
 };
 
 struct llama_lora_weight {
@@ -5302,12 +5217,6 @@ static void llm_load_hparams(
     hparams.rope_type = llama_rope_type(&model);
 }
 
-// TODO: This should probably be in llama.h
-static std::vector<llama_vocab::id> llama_tokenize_internal(
-    const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special = false
-);
-static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);
-
 static void llm_load_vocab(
         llama_model_loader & ml,
         llama_model & model) {
@@ -5644,7 +5553,7 @@ static void llm_load_vocab(
             }
         }
         try {
-            vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
+            vocab.linefeed_id = llama_byte_to_token_impl(vocab, '\n');
         } catch (const std::exception & e) {
             LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
             vocab.linefeed_id = vocab.special_pad_id;
@@ -15234,2702 +15143,189 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) {
 }
 
 //
-// tokenizer
+// quantization
 //
 
-static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
-    return vocab.type;
-}
+struct quantize_state_internal {
+    const llama_model                 & model;
+    const llama_model_quantize_params * params;
 
-static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL;
-}
+    int n_attention_wv    = 0;
+    int n_ffn_down        = 0;
+    int n_ffn_gate        = 0;
+    int n_ffn_up          = 0;
+    int i_attention_wv    = 0;
+    int i_ffn_down        = 0;
+    int i_ffn_gate        = 0;
+    int i_ffn_up          = 0;
 
-static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN;
-}
+    int n_k_quantized     = 0;
+    int n_fallback        = 0;
 
-static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL;
-}
+    bool has_imatrix      = false;
 
-static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE;
-}
+    // used to figure out if a model shares tok_embd with the output weight
+    bool has_output       = false;
 
-static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
-}
+    quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
+        : model(model)
+        , params(params)
+        {}
+};
 
-static bool llama_is_unused_token(const llama_vocab& vocab, llama_token id) {
-    GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNUSED;
-}
+static void llama_tensor_dequantize_internal(
+    struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
+    const size_t nelements, const int nthread
+) {
+    if (output.size() < nelements) {
+        output.resize(nelements);
+    }
+    float * f32_output = (float *) output.data();
 
-static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
-    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
-    GGML_ASSERT(llama_is_byte_token(vocab, id));
-    const auto & token_data = vocab.id_to_token.at(id);
-    switch (llama_vocab_get_type(vocab)) {
-        case LLAMA_VOCAB_TYPE_SPM:
-        case LLAMA_VOCAB_TYPE_UGM: {
-            auto buf = token_data.text.substr(3, 2);
-            return strtol(buf.c_str(), NULL, 16);
-        }
-        case LLAMA_VOCAB_TYPE_BPE: {
-            GGML_ASSERT(false);
-            return unicode_utf8_to_byte(token_data.text); // TODO: why is this here after GGML_ASSERT?
-        }
-        case LLAMA_VOCAB_TYPE_WPM: {
-            GGML_ASSERT(false);
+    ggml_type_traits_t qtype;
+    if (ggml_is_quantized(tensor->type)) {
+        qtype = ggml_internal_get_type_traits(tensor->type);
+        if (qtype.to_float == NULL) {
+            throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
         }
-        default:
-            GGML_ASSERT(false);
+    } else if (tensor->type != GGML_TYPE_F16 &&
+               tensor->type != GGML_TYPE_BF16) {
+        throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
     }
-}
 
-static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
-    GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
-    static const char * hex = "0123456789ABCDEF";
-    switch (llama_vocab_get_type(vocab)) {
-        case LLAMA_VOCAB_TYPE_SPM:
-        case LLAMA_VOCAB_TYPE_UGM: {
-            const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
-            auto token = vocab.token_to_id.find(buf);
-            if (token != vocab.token_to_id.end()) {
-                return (*token).second;
-            }
-            // Try to fall back to just the byte as a string
-            const char buf2[2] = { (char)ch, 0 };
-            return vocab.token_to_id.at(buf2);
-        }
-        case LLAMA_VOCAB_TYPE_WPM:
-        case LLAMA_VOCAB_TYPE_BPE: {
-            return vocab.token_to_id.at(unicode_byte_to_utf8(ch));
+    if (nthread < 2) {
+        if (tensor->type == GGML_TYPE_F16) {
+            ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
+        } else if (tensor->type == GGML_TYPE_BF16) {
+            ggml_bf16_to_fp32_row((ggml_bf16_t *)tensor->data, f32_output, nelements);
+        } else if (ggml_is_quantized(tensor->type)) {
+            qtype.to_float(tensor->data, f32_output, nelements);
+        } else {
+            GGML_ASSERT(false); // unreachable
         }
-        default:
-            GGML_ASSERT(false);
+        return;
     }
-}
-
-static void llama_escape_whitespace(std::string & text) {
-    replace_all(text, " ", "\xe2\x96\x81");
-}
 
-static void llama_unescape_whitespace(std::string & word) {
-    replace_all(word, "\xe2\x96\x81", " ");
-}
+    size_t block_size;
+    if (tensor->type == GGML_TYPE_F16 ||
+        tensor->type == GGML_TYPE_BF16) {
+        block_size = 1;
+    } else {
+        block_size = (size_t)ggml_blck_size(tensor->type);
+    }
 
-struct llm_symbol {
-    using index = int;
-    index prev;
-    index next;
-    const char * text;
-    size_t n;
-};
+    size_t block_size_bytes = ggml_type_size(tensor->type);
 
-static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");
+    GGML_ASSERT(nelements % block_size == 0);
+    size_t nblocks = nelements / block_size;
+    size_t blocks_per_thread = nblocks / nthread;
+    size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
 
-// SPM tokenizer
-// original implementation:
-// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
+    size_t in_buff_offs = 0;
+    size_t out_buff_offs = 0;
 
-struct llm_bigram_spm {
-    struct comparator {
-        bool operator()(llm_bigram_spm & l, llm_bigram_spm & r) {
-            return (l.score < r.score) || (l.score == r.score && l.left > r.left);
-        }
-    };
-    using queue_storage = std::vector<llm_bigram_spm>;
-    using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
-    llm_symbol::index left;
-    llm_symbol::index right;
-    float score;
-    size_t size;
-};
+    for (int tnum = 0; tnum < nthread; tnum++) {
+        size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
+        size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
+        size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
 
-struct llm_tokenizer_spm {
-    llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {}
-
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
-        // split string into utf8 chars
-        int index = 0;
-        size_t offs = 0;
-        while (offs < text.size()) {
-            llm_symbol sym;
-            size_t len = utf8_len(text[offs]);
-            sym.text = text.c_str() + offs;
-            sym.n = std::min(len, text.size() - offs);
-            offs += sym.n;
-            sym.prev = index - 1;
-            sym.next = offs == text.size() ? -1 : index + 1;
-            index++;
-            symbols.emplace_back(sym);
-        }
-
-        // seed the work queue with all possible 2-character tokens.
-        for (size_t i = 1; i < symbols.size(); ++i) {
-            try_add_bigram(i - 1, i);
-        }
-
-        // keep substituting the highest frequency pairs for as long as we can.
-        while (!work_queue.empty()) {
-            auto bigram = work_queue.top();
-            work_queue.pop();
-
-            auto & left_sym = symbols[bigram.left];
-            auto & right_sym = symbols[bigram.right];
-
-            // if one of the symbols already got merged, skip it.
-            if (left_sym.n == 0 || right_sym.n == 0 ||
-                left_sym.n + right_sym.n != bigram.size) {
-                continue;
+        auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
+            if (typ == GGML_TYPE_F16) {
+                ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
+            } else if (typ == GGML_TYPE_BF16) {
+                ggml_bf16_to_fp32_row((ggml_bf16_t *)inbuf, outbuf, nels);
+            } else {
+                qtype.to_float(inbuf, outbuf, nels);
             }
+        };
+        workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
+        in_buff_offs += thr_block_bytes;
+        out_buff_offs += thr_elems;
+    }
+    for (auto & w : workers) { w.join(); }
+    workers.clear();
+}
 
-            // merge the right sym into the left one
-            left_sym.n += right_sym.n;
-            right_sym.n = 0;
+static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
+    const std::string name = ggml_get_name(tensor);
 
-            //LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
+    // TODO: avoid hardcoded tensor names - use the TN_* constants
+    const llm_arch arch = qs.model.arch;
+    const auto       tn = LLM_TN(arch);
 
-            // remove the right sym from the chain
-            left_sym.next = right_sym.next;
-            if (right_sym.next >= 0) {
-                symbols[right_sym.next].prev = bigram.left;
+    auto use_more_bits = [](int i_layer, int n_layers) -> bool {
+        return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2;
+    };
+    const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
+    auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
+        if (n_expert > 1) {
+            // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly
+            // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
+            // for getting the current layer as I initially thought, and we need to resort to parsing the
+            // tensor name.
+            if (sscanf(name, "blk.%d.", &i_layer) != 1) {
+                throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
+            }
+            if (i_layer < 0 || i_layer >= n_layer) {
+                throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer));
             }
-
-            // find more substitutions
-            try_add_bigram(left_sym.prev, bigram.left);
-            try_add_bigram(bigram.left, left_sym.next);
-        }
-
-        for (int i = 0; i != -1; i = symbols[i].next) {
-            auto & symbol = symbols[i];
-            resegment(symbol, output);
         }
-    }
-
-private:
-    void resegment(llm_symbol & symbol, std::vector<llama_vocab::id> & output) {
-        auto text = std::string(symbol.text, symbol.n);
-        auto token = vocab.token_to_id.find(text);
+        return std::make_pair(i_layer, n_layer);
+    };
 
-        // Do we need to support is_unused?
-        if (token != vocab.token_to_id.end()) {
-            output.push_back((*token).second);
-            return;
+    // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
+    // with the quantization of the output tensor
+    if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
+        if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
+            new_type = qs.params->output_tensor_type;
+        } else {
+            int nx = tensor->ne[0];
+            if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
+                new_type = GGML_TYPE_Q8_0;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
+                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S  || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M   ||
+                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+                new_type = GGML_TYPE_Q5_K;
+            }
+            else if (new_type != GGML_TYPE_Q8_0) {
+                new_type = GGML_TYPE_Q6_K;
+            }
         }
-
-        const auto p = rev_merge.find(text);
-
-        if (p == rev_merge.end()) {
-            // output any symbols that did not form tokens as bytes.
-            output.reserve(output.size() + symbol.n);
-            for (int j = 0; j < (int)symbol.n; ++j) {
-                llama_vocab::id token_id = llama_byte_to_token(vocab, symbol.text[j]);
-                output.push_back(token_id);
+    } else if (name == "token_embd.weight") {
+        if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
+            new_type = qs.params->token_embedding_type;
+        } else {
+            if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
+                ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+                new_type = GGML_TYPE_Q2_K;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
+                new_type = GGML_TYPE_IQ3_S;
+            }
+            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+                new_type = GGML_TYPE_IQ3_S;
+            }
+            else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 ||
+                     new_type == GGML_TYPE_Q4_0_8_8) {
+                new_type = GGML_TYPE_Q4_0;
             }
-            return;
         }
-
-        resegment(symbols[p->second.first],  output);
-        resegment(symbols[p->second.second], output);
-    }
-
-    void try_add_bigram(int left, int right) {
-        if (left == -1 || right == -1) {
-            return;
+    } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
+               ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+        if (name.find("attn_v.weight") != std::string::npos) {
+            if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
+            else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            ++qs.i_attention_wv;
         }
-
-        const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
-        auto token = vocab.token_to_id.find(text);
-
-        if (token == vocab.token_to_id.end()) {
-            return;
+        else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
+            new_type = GGML_TYPE_Q4_K;
         }
-
-        if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
-            return;
-        }
-
-        const auto & tok_data = vocab.id_to_token[(*token).second];
-
-        llm_bigram_spm bigram;
-        bigram.left  = left;
-        bigram.right = right;
-        bigram.score = tok_data.score;
-        bigram.size  = text.size();
-
-        work_queue.push(bigram);
-
-        // Do we need to support is_unused?
-        rev_merge[text] = std::make_pair(left, right);
-    }
-
-    const llama_vocab & vocab;
-
-    std::vector<llm_symbol> symbols;
-    llm_bigram_spm::queue work_queue;
-
-    std::map<std::string, std::pair<int, int>> rev_merge;
-};
-
-// BPE tokenizer
-// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
-// tried to simplify unicode stuff, so most likely does not work 100% correctly!
-
-// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused
-
-struct llm_bigram_bpe {
-    struct comparator {
-        bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
-            return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
-        }
-    };
-
-    using queue_storage = std::vector<llm_bigram_bpe>;
-    using queue = std::priority_queue<llm_bigram_bpe, queue_storage, comparator>;
-    llm_symbol::index left;
-    llm_symbol::index right;
-    std::string text;
-    int rank;
-    size_t size;
-};
-
-struct llm_tokenizer_bpe {
-    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {
-        GGML_ASSERT(vocab.type == LLAMA_VOCAB_TYPE_BPE);
-        switch (vocab.type_pre) {
-            case LLAMA_VOCAB_PRE_TYPE_LLAMA3:
-                regex_exprs = {
-                    // original regex from tokenizer.json
-                    //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-
-                    // adapted: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2080233989
-                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_DBRX:
-            case LLAMA_VOCAB_PRE_TYPE_SMAUG:
-                regex_exprs = {
-                    // same as llama3
-                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM:
-                regex_exprs = {
-                    "[\r\n]",
-                    "\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿDŽ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿff-stﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+",
-                    "\\s?[!-/:-~！-／：-～‘-‟　-。]+",
-                    "\\s+$",
-                    "[一-龥ࠀ-一가-퟿]+",
-                    "\\p{N}+",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
-                regex_exprs = {
-                    "[\r\n]",
-                    "\\s?\\p{L}+",
-                    "\\s?\\p{P}+",
-                    "[一-龥ࠀ-一가-퟿]+",
-                    "\\p{N}",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_FALCON:
-                regex_exprs = {
-                    "[\\p{P}\\$\\+<=>\\^~\\|`]+",
-                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                    "[0-9][0-9][0-9]",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_STARCODER:
-            case LLAMA_VOCAB_PRE_TYPE_REFACT:
-            case LLAMA_VOCAB_PRE_TYPE_COMMAND_R:
-            case LLAMA_VOCAB_PRE_TYPE_SMOLLM:
-            case LLAMA_VOCAB_PRE_TYPE_CODESHELL:
-                regex_exprs = {
-                    "\\p{N}",
-                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_GPT2:
-            case LLAMA_VOCAB_PRE_TYPE_MPT:
-            case LLAMA_VOCAB_PRE_TYPE_OLMO:
-            case LLAMA_VOCAB_PRE_TYPE_JAIS:
-                regex_exprs = {
-                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
-            case LLAMA_VOCAB_PRE_TYPE_QWEN2:
-                regex_exprs = {
-                    // original regex from tokenizer.json
-                    // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
-                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_PORO:
-                regex_exprs = {
-                    " ?[^(\\s|.,!?…。，、।۔،)]+",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_CHATGLM4:
-                regex_exprs = {
-                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_VIKING:
-                regex_exprs = {
-                    " ?[^(\\s|.,!?…。，、।۔،)]+",
-                    "\\p{N}",
-                };
-                break;
-            case LLAMA_VOCAB_PRE_TYPE_TEKKEN:
-                    // original regex from tokenizer.json
-                    // "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
-                regex_exprs = {
-                    "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
-                };
-                break;
-            default:
-                // default regex for BPE tokenization pre-processing
-                regex_exprs = {
-                    "[\\p{P}\\$\\+<=>\\^~\\|]+",
-                    "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
-                    "\\p{N}+",
-                    "[0-9][0-9][0-9]",
-                };
-                break;
-        }
-    }
-
-    void append(const llama_vocab::id token_id, std::vector<llama_vocab::id> & output) const {
-        output.push_back(token_id);
-    }
-
-    bool append_bos(std::vector<llama_vocab::id> & output) const {
-        if (vocab.tokenizer_add_bos) {
-            GGML_ASSERT(vocab.special_bos_id != -1);
-            output.push_back(vocab.special_bos_id);
-            return true;
-        }
-        return false;
-    }
-
-    bool append_eos(std::vector<llama_vocab::id> & output) const {
-        if (vocab.tokenizer_add_eos) {
-            GGML_ASSERT(vocab.special_eos_id != -1);
-            output.push_back(vocab.special_eos_id);
-            return true;
-        }
-        return false;
-    }
-
-    void check_double_bos_eos(const std::vector<llama_vocab::id> & output) const {
-        if (vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) {
-            LLAMA_LOG_WARN(
-                "%s: Added a BOS token to the prompt as specified by the model but the prompt "
-                "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
-                "Are you sure this is what you want?\n", __FUNCTION__);
-        }
-        if (vocab.tokenizer_add_eos && output.size() >= 2 && *(output.end()-2) == vocab.special_eos_id) {
-            LLAMA_LOG_WARN(
-                "%s: Added a EOS token to the prompt as specified by the model but the prompt "
-                "also ends with a EOS token. So now the final prompt ends with 2 EOS tokens. "
-                "Are you sure this is what you want?\n", __FUNCTION__);
-        }
-    }
-
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
-        int final_prev_index = -1;
-
-        const auto word_collection = unicode_regex_split(text, regex_exprs);
-
-        symbols_final.clear();
-
-        for (auto & word : word_collection) {
-            work_queue = llm_bigram_bpe::queue();
-            symbols.clear();
-
-            int index = 0;
-            size_t offset = 0;
-
-            if (vocab.tokenizer_ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) {
-                symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()});
-                offset = word.size();
-            }
-
-            while (offset < word.size()) {
-                llm_symbol sym;
-                size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset]));
-                sym.text = word.c_str() + offset;
-                sym.n = char_len;
-                offset += sym.n;
-                sym.prev = index - 1;
-                sym.next = offset == word.size() ? -1 : index + 1;
-                index++;
-                symbols.emplace_back(sym);
-            }
-            for (size_t i = 1; i < symbols.size(); ++i) {
-                add_new_bigram(i - 1, i);
-            }
-
-            // build token(s)
-            while (!work_queue.empty()) {
-                auto bigram = work_queue.top();
-                work_queue.pop();
-
-                auto & left_symbol = symbols[bigram.left];
-                auto & right_symbol = symbols[bigram.right];
-
-                if (left_symbol.n == 0 || right_symbol.n == 0) {
-                    continue;
-                }
-                std::string left_token = std::string(left_symbol.text, left_symbol.n);
-                std::string right_token = std::string(right_symbol.text, right_symbol.n);
-                if (left_token + right_token != bigram.text) {
-                    continue;  // Skip this bigram if it's outdated
-                }
-
-                // merge the right sym into the left one
-                left_symbol.n += right_symbol.n;
-                right_symbol.n = 0;
-
-                // remove the right sym from the chain
-                left_symbol.next = right_symbol.next;
-                if (right_symbol.next >= 0) {
-                    symbols[right_symbol.next].prev = bigram.left;
-                }
-
-                add_new_bigram(left_symbol.prev, bigram.left);  // left side of current symbol
-                add_new_bigram(bigram.left, left_symbol.next);  // right side of current symbol
-            }
-
-            // add the finished tokens to the final list keeping correct order for next and prev
-            for (auto & sym : symbols) {
-                if (sym.n > 0) {
-                    sym.prev = final_prev_index;
-                    sym.next = -1;
-                    if (final_prev_index != -1) {
-                        symbols_final[final_prev_index].next = symbols_final.size();
-                    }
-                    symbols_final.emplace_back(sym);
-                    final_prev_index = symbols_final.size() - 1;
-                }
-            }
-        }
-
-        symbols = symbols_final;
-
-        if (!symbols.empty()) {
-            for (int i = 0; i != -1; i = symbols[i].next) {
-                auto & symbol = symbols[i];
-                if (symbol.n == 0) {
-                    continue;
-                }
-
-                const std::string str = std::string(symbol.text, symbol.n);
-                const auto token = vocab.token_to_id.find(str);
-
-                if (token == vocab.token_to_id.end()) {
-                    for (auto j = str.begin(); j != str.end(); ++j) {
-                        std::string byte_str(1, *j);
-                        auto token_multibyte = vocab.token_to_id.find(byte_str);
-                        if (token_multibyte != vocab.token_to_id.end()) {
-                            output.push_back(token_multibyte->second);
-                        }
-                    }
-                } else {
-                    output.push_back((*token).second);
-                }
-            }
-        }
-    }
-
-private:
-    void add_new_bigram(int left, int right) {
-        if (left == -1 || right == -1) {
-            return;
-        }
-
-        std::string left_token  = std::string(symbols[left].text,  symbols[left].n);
-        std::string right_token = std::string(symbols[right].text, symbols[right].n);
-
-        int rank_found = -1;
-
-        rank_found = vocab.find_bpe_rank(left_token, right_token);
-
-        if (rank_found < 0) {
-            return;
-        }
-
-        llm_bigram_bpe bigram;
-
-        bigram.left  = left;
-        bigram.right = right;
-        bigram.text  = left_token + right_token;
-        bigram.size  = left_token.size() + right_token.size();
-        bigram.rank  = rank_found;
-
-        work_queue.push(bigram);
-    }
-
-    const llama_vocab & vocab;
-
-    std::vector<std::string> regex_exprs;
-
-    std::vector<llm_symbol> symbols;
-    std::vector<llm_symbol> symbols_final;
-
-    llm_bigram_bpe::queue work_queue;
-};
-
-struct llm_tokenizer_wpm {
-    llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
-
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) const {
-        const auto & token_map = vocab.token_to_id;
-
-        // normalize and split by whitespace
-        std::vector<std::string> words = preprocess(text);
-
-        // bos token prepended already
-
-        // find the longest tokens that form the words
-        for (const std::string & word : words) {
-            // skip empty words
-            if (word.size() == 0) {
-                continue;
-            }
-
-            // prepend phantom space
-            const std::string word1 = "\xe2\x96\x81" + word;
-            const int n = word1.size();
-
-            const size_t current_tokens = output.size();
-
-            // we're at the start of a new word
-            // move through character position in word
-            for (int i = 0; i < n; ++i) {
-                // loop through possible match length
-                bool match = false;
-                for (int j = std::min(n, i + vocab.max_token_len + 1); j > i; j--) {
-                    auto it = token_map.find(word1.substr(i, j - i));
-                    if (it != token_map.end()) {
-                        output.push_back(it->second);
-                        match = true;
-                        i = j - 1;
-                        break;
-                    }
-                }
-
-                if (!match) { // discard all
-                    output.resize(current_tokens);
-                    break;  // and discard next tokens
-                }
-            }
-
-            // we didn't find any matches for this word
-            if (current_tokens == output.size()) {
-                output.push_back(vocab.special_unk_id);
-            }
-        }
-    }
-
-    // TODO: reduce string copies by using cpts_offs array
-    std::vector<std::string> preprocess(const std::string & text) const {
-        const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
-        std::vector<std::string> words(1, "");
-
-        for (const uint32_t cpt : cpts_nfd) {
-            const auto flags = unicode_cpt_flags(cpt);
-
-            if (flags.is_whitespace) {
-                if (words.back().size()) {  // finish previous word if any
-                    words.emplace_back();
-                }
-                continue;
-            }
-
-            assert (!flags.is_separator);
-            if (cpt == 0 || cpt == 0xFFFD || flags.is_control) {
-                continue;
-            }
-
-            const std::string s = unicode_cpt_to_utf8(unicode_tolower(cpt));
-            if (flags.is_punctuation || ( cpt < 0x7F && flags.is_symbol ) || is_chinese_char(cpt)) {
-                if (words.back().size()) {  // finish previous word if any
-                    words.emplace_back();
-                }
-                words.back() = s;       // single char word
-                words.emplace_back();   // start a new word
-            } else {
-                words.back() += s;  // append char to word
-            }
-        }
-
-        if (!words.back().size()) {
-            words.pop_back();
-        }
-
-        return words;
-    }
-
-    static bool is_chinese_char(uint32_t cpt) {
-        return
-            (cpt >= 0x04E00 && cpt <= 0x09FFF) ||
-            (cpt >= 0x03400 && cpt <= 0x04DBF) ||
-            (cpt >= 0x20000 && cpt <= 0x2A6DF) ||
-            (cpt >= 0x2A700 && cpt <= 0x2B73F) ||
-            (cpt >= 0x2B740 && cpt <= 0x2B81F) ||
-            (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
-            (cpt >= 0x0F900 && cpt <= 0x0FAFF) ||
-            (cpt >= 0x2F800 && cpt <= 0x2FA1F);
-            //(cpt >= 0x3000  && cpt <= 0x303F)  ||
-            //(cpt >= 0xFF00  && cpt <= 0xFFEF);
-    }
-
-    const llama_vocab & vocab;
-};
-
-struct naive_trie {
-    naive_trie() : has_value(false), value(0) {
-    }
-    void insert(const char * key, size_t len, int32_t value = 0) {
-        if (len == 0) {
-            this->has_value = true;
-            this->value = value;
-            return;
-        }
-        char c = key[0];
-        auto res = children.find(c);
-        if (res != children.end()) {
-            res->second.insert(key + 1, len - 1, value);
-        } else {
-            auto res = children.insert(std::make_pair(c, naive_trie()));
-            res.first->second.insert(key + 1, len - 1, value);
-        }
-    }
-    std::pair<const char *, size_t> get_longest_prefix(const char * key, size_t len, size_t offset = 0) {
-        if (len == 0 || offset == len) {
-            return std::make_pair(key, offset);
-        }
-        char c = key[offset];
-        auto res = children.find(c);
-        if (res != children.end()) {
-            return res->second.get_longest_prefix(key, len, offset + 1);
-        } else {
-            return std::make_pair(key, offset);
-        }
-    }
-    struct naive_trie * traverse(const char c) {
-        auto res = children.find(c);
-        if (res != children.end()) {
-            return &res->second;
-        } else {
-            return NULL;
-        }
-    }
-    std::map<char, struct naive_trie> children;
-    bool has_value;
-    llama_token value;
-};
-
-struct llm_tokenizer_ugm {
-    llm_tokenizer_ugm(const llama_vocab & vocab) : vocab(vocab) {
-        if (vocab.precompiled_charsmap.size() > 0) {
-            size_t charsmap_offset = 0;
-
-            // First four bytes of precompiled_charsmap contains length of binary
-            // blob containing XOR-compressed compact double array (XCDA) entries
-            uint32_t xcda_blob_size = *(const uint32_t *) &vocab.precompiled_charsmap[0];
-            charsmap_offset += sizeof(xcda_blob_size);
-            if (xcda_blob_size + charsmap_offset >= vocab.precompiled_charsmap.size()) {
-                throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
-            }
-
-            // Next xcda_blob_size bytes contain entries of XOR-compressed compact
-            // double array (XCDA). Each entry is bit-packed into a 32-bit integer.
-            xcda_array = (const uint32_t *) &vocab.precompiled_charsmap[charsmap_offset];
-            xcda_array_size = xcda_blob_size / sizeof(uint32_t);
-            charsmap_offset += xcda_blob_size;
-
-            // Remaining bytes of precompiled charsmap contain null-terminated
-            // replacement strings for prefixes matched by the XCDA.
-            prefix_replacements = &vocab.precompiled_charsmap[charsmap_offset];
-            prefix_replacements_size = vocab.precompiled_charsmap.size() - charsmap_offset;
-        }
-
-        for (unsigned int id = 0; id < vocab.id_to_token.size(); ++id) {
-            const auto &token_data = vocab.id_to_token[id];
-
-            if (llama_is_normal_token(vocab, id)) {
-                min_score = std::min<float>(min_score, token_data.score);
-                max_score = std::max<float>(max_score, token_data.score);
-            }
-
-            if (llama_is_normal_token(vocab, id) ||
-                llama_is_user_defined_token(vocab, id) ||
-                llama_is_unused_token(vocab, id)) {
-                token_matcher.insert(token_data.text.data(), token_data.text.size(), id);
-            }
-
-            if (llama_is_user_defined_token(vocab, id)) {
-                user_defined_token_matcher.insert(token_data.text.data(), token_data.text.size());
-            }
-        }
-
-        unknown_token_score = min_score - unknown_token_score_penalty;
-    }
-
-    /* This implementation is based on SentencePiece optimized Viterbi algorithm for
-     * unigram language models. The general idea is to:
-     * - move along the input sequence in steps of one UTF code point,
-     * - at each step find all possible tokenizations of the prefix by
-     *   traversing the tokens trie,
-     * - for each tokenization store the best one so far (by higher score)
-     * - use the position in sequence after given token as an index to store
-     *   results
-     * - if there was no valid tokenization of the current UTF code point
-     *   then use unknown token with additional score penalty
-     * After processing the whole sequence we backtrack from the end to get
-     * the best tokenization.
-    */
-    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
-        // normalize the input first
-        std::string normalized;
-        normalize(text, &normalized);
-        size_t input_len = normalized.size();
-        if (input_len == 0) {
-            return;
-        }
-
-        // initialize score_sum to -FLT_MAX so it will be always lower than sums of token scores
-        std::vector<struct best_tokenization> tokenization_results(input_len + 1, {vocab.special_unk_id, 0, -FLT_MAX});
-        // at the beginning tokenization score is zero
-        tokenization_results[0] = { vocab.special_unk_id, 0, 0 };
-
-        for (size_t input_offset = 0; input_offset < input_len;) {
-            size_t prefix_offset = input_offset;
-            // calculate how many code units are in the currently processed UTF code point
-            size_t n_utf8_code_units = std::min<size_t>(utf8_len(normalized[input_offset]), input_len - input_offset);
-
-            // traverse the token matcher trie to find a matching token
-            bool single_codepoint_token_found = false;
-            const struct best_tokenization & current_best = tokenization_results[input_offset];
-            struct naive_trie * node  = token_matcher.traverse(normalized[prefix_offset++]);
-
-            while (prefix_offset <= input_len && node != NULL) {
-                // check if we found valid token in prefix
-                if (node->has_value) {
-                    // check if it corresponds to the whole UTF code point
-                    if (prefix_offset - input_offset == n_utf8_code_units) {
-                        single_codepoint_token_found = true;
-                    }
-                    llama_token token_id = node->value;
-                    const auto & token_data = vocab.id_to_token[token_id];
-
-                    // we set the user-defined token scores to 0 to make them more likely to be selected
-                    // (normal token scores are log probabilities, so they are negative)
-                    // score type is double here to make tokenization results exactly
-                    // the same as in the HF tokenizer using SentencePiece
-                    const double token_score = llama_is_user_defined_token(vocab, token_id) ? 0.0 : token_data.score;
-                    const double challenger_score = current_best.score_sum + token_score;
-                    struct best_tokenization & current_champ = tokenization_results[prefix_offset];
-                    if (challenger_score > current_champ.score_sum) {
-                        struct best_tokenization challenger = { token_id, input_offset, (float) challenger_score };
-                        current_champ = challenger;
-                    }
-                }
-                node = node->traverse(normalized[prefix_offset++]);
-            }
-
-            // if we didn't find a valid token corresponding to the whole UTF code point
-            // then use unknown token as the tokenization of this UTF code point
-            if (!single_codepoint_token_found) {
-                const double challenger_score = current_best.score_sum + unknown_token_score;
-                prefix_offset = input_offset + n_utf8_code_units;
-                struct best_tokenization & current_champ = tokenization_results[prefix_offset];
-                if (challenger_score > current_champ.score_sum) {
-                    struct best_tokenization challenger = { vocab.special_unk_id, input_offset, (float) challenger_score };
-                    current_champ = challenger;
-                }
-            }
-
-            // move to the next UTF code point
-            input_offset += n_utf8_code_units;
-        }
-
-        // now backtrack from the end to gather token ids of the best tokenization
-        // merge sequences of consecutive unknown tokens into single unknown tokens
-        bool is_prev_unknown = false;
-        for (struct best_tokenization & tokenization = tokenization_results[input_len]; ; tokenization = tokenization_results[tokenization.input_offset]) {
-            bool is_unknown = tokenization.token_id == vocab.special_unk_id;
-            if (!(is_prev_unknown && is_unknown)) {
-                output.push_back(tokenization.token_id);
-            }
-            if (tokenization.input_offset == 0) {
-                break;
-            }
-            is_prev_unknown = is_unknown;
-        }
-
-        // reverse the output since we added tokens starting from the end of the input
-        std::reverse(output.begin(), output.end());
-    }
-
-private:
-    const llama_vocab & vocab;
-
-    // helper structure for returning normalization results
-    struct normalization_result {
-        const char * normalized;
-        size_t normalized_len;
-        size_t consumed_input;
-    };
-
-    void normalize(const std::string& input, std::string * normalized) {
-        normalized->clear();
-        normalized->reserve(input.size() * 3);
-
-        const std::string space = vocab.tokenizer_escape_whitespaces ? escaped_space : " ";
-
-        bool shall_prepend_space = !vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
-        bool shall_append_space = vocab.tokenizer_treat_whitespace_as_suffix && vocab.tokenizer_add_space_prefix;
-        bool shall_merge_spaces = vocab.tokenizer_remove_extra_whitespaces;
-
-        bool is_space_prepended = false;
-        bool processing_non_ws = false;
-
-        size_t input_len = input.size();
-
-        for (size_t input_offset = 0; input_offset < input_len; ) {
-            auto norm_res = normalize_prefix(input, input_offset);
-            for (size_t i = 0; i < norm_res.normalized_len; i++) {
-                char c = norm_res.normalized[i];
-                if (c != ' ') {
-                    if (!processing_non_ws) {
-                        processing_non_ws = true;
-                        if ((shall_prepend_space && !is_space_prepended) || shall_merge_spaces) {
-                            normalized->append(space);
-                            is_space_prepended = true;
-                        }
-                    }
-                    normalized->push_back(c);
-                } else {
-                    if (processing_non_ws) {
-                        processing_non_ws = false;
-                    }
-                    if (!shall_merge_spaces) {
-                        normalized->append(space);
-                    }
-                }
-            }
-
-            input_offset += norm_res.consumed_input;
-        }
-
-        if (shall_append_space) {
-            normalized->append(space);
-        }
-    }
-
-    /*
-     * This structure is a view wrapper for XOR-compressed double array (XCDA)
-     * See Shunsuke Kanda (2018). Space- and Time-Efficient String Dictionaries.
-     * Eeach bit-packed entry contains:
-     * - BASE array value in bits 10-30
-     * - LCHECK array value in bits 0-7
-     * - LEAF array value in bit 9
-     * Entries containing indexes of replacement sequences have set bit 31
-     */
-    struct xcda_array_view {
-    public:
-        xcda_array_view(const uint32_t * xcda_array, size_t xcda_array_size) : xcda_array(xcda_array), xcda_array_size(xcda_array_size) {
-        }
-        uint32_t get_base(size_t index) {
-            uint32_t packed_node = get_node(index);
-            return (packed_node >> 10) << ((packed_node & (1U << 9)) >> 6);
-        }
-        uint32_t get_lcheck(size_t index) {
-            uint32_t packed_node = get_node(index);
-            return packed_node & ((1U << 31) | 0xff);
-        }
-        bool get_leaf(size_t index) {
-            uint32_t packed_node = get_node(index);
-            return (packed_node >> 8) & 1;
-        }
-        uint32_t get_value(size_t index) {
-            uint32_t packed_node = get_node(index);
-            return packed_node & ((1U << 31) - 1);
-        }
-    private:
-        uint32_t get_node(size_t index) {
-            if (index > xcda_array_size) {
-                throw std::runtime_error("Index out of array bounds in XCDA array!");
-            }
-            return xcda_array[index];
-        }
-        const uint32_t * xcda_array;
-        size_t xcda_array_size;
-    };
-
-    struct normalization_result normalize_prefix(const std::string & input, size_t input_offset) {
-        if (input_offset == input.size()) {
-            return { &input[input_offset], 0, 0 };
-        }
-
-        // if input prefix matches some user-defined token return this token as normalization result
-        auto user_defined_token_match = user_defined_token_matcher.get_longest_prefix(&input[input_offset], input.size() - input_offset);
-        if (user_defined_token_match.second > 0) {
-            return { &input[input_offset], user_defined_token_match.second, user_defined_token_match.second };
-        }
-
-        size_t longest_prefix_length = 0;
-        size_t longest_prefix_offset = 0;
-
-        if (xcda_array_size > 0) {
-            struct xcda_array_view xcda_view(xcda_array, xcda_array_size);
-
-            // Find the longest normalized sequence matching the input prefix by walking
-            // the XOR-compressed compact double array (XCDA) starting from the root node
-            // We find the index of the next node by calculating BASE[s] ^ c where s is
-            // the index of the previous node and c is a numerical character value
-            uint32_t node_index = 0;
-            // get BASE of the root node
-            node_index = xcda_view.get_base(node_index);
-            for (size_t prefix_offset = input_offset; prefix_offset < input.size(); prefix_offset++) {
-                unsigned char c = input[prefix_offset];
-                if (c == 0) {
-                    break;
-                }
-                node_index ^= c;
-                // if value of LCHECK is not c it means that this is not a child of
-                // the previous node, so we stop matching
-                if (xcda_view.get_lcheck(node_index) != c) {
-                    break;
-                }
-                bool is_leaf = xcda_view.get_leaf(node_index);
-                // get BASE of the current node
-                node_index ^= xcda_view.get_base(node_index);
-                // if LEAF of the current node is true, it means that its BASE points to the node
-                // containing index of replacement sequence for currently matched input prefix
-                if (is_leaf)
-                {
-                    longest_prefix_length = prefix_offset - input_offset + 1;
-                    // get index of replacement sequence for currently matched input prefix
-                    longest_prefix_offset = xcda_view.get_value(node_index);
-                }
-            }
-        }
-
-        if (longest_prefix_length > 0) {
-            // we have a match, so return the replacement sequence
-            if (longest_prefix_offset >= prefix_replacements_size) {
-                throw std::runtime_error("Index out of array bounds in precompiled charsmap!");
-            }
-            const char * prefix_replacement = &prefix_replacements[longest_prefix_offset];
-            return { prefix_replacement, strlen(prefix_replacement), longest_prefix_length };
-        } else {
-            // check if the input prefix contains a valid sequence of UTF-8 code units
-            try {
-                // if yes, return this sequence unmodified
-                size_t prefix_offset = input_offset;
-                unicode_cpt_from_utf8(input, prefix_offset);
-                return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset };
-            } catch (std::invalid_argument & /*ex*/) {
-                // if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER
-                return { "\xEF\xBF\xBD", 3, 1 };
-            }
-        }
-    }
-
-    // escaped space symbol - U+2581 (Lower One Eighth Block)
-    const std::string escaped_space = "\xE2\x96\x81";
-
-    const char * prefix_replacements = NULL;
-    size_t prefix_replacements_size = 0;
-
-    const uint32_t * xcda_array = NULL;
-    size_t xcda_array_size = 0;
-
-    struct naive_trie user_defined_token_matcher;
-
-    // this structure stores the best tokenization so far at input_offset
-    struct best_tokenization {
-        llama_token token_id;
-        size_t input_offset;
-        float score_sum;
-    };
-
-    float min_score = FLT_MAX;
-    float max_score = -FLT_MAX;
-
-    float unknown_token_score_penalty = 10.0;
-    float unknown_token_score;
-
-    struct naive_trie token_matcher;
-};
-
-
-typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
-    FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
-    FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
-} FRAGMENT_BUFFER_VARIANT_TYPE;
-
-struct fragment_buffer_variant {
-    fragment_buffer_variant(llama_vocab::id _token)
-    :
-        type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
-        token(_token),
-        raw_text(_dummy),
-        offset(0),
-        length(0) {}
-
-    fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
-    :
-        type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
-        token((llama_vocab::id) - 1),
-        raw_text(_raw_text),
-        offset(_offset),
-        length(_length){
-            GGML_ASSERT(_offset >= 0);
-            GGML_ASSERT(_length >= 1);
-            GGML_ASSERT(offset + length <= raw_text.length());
-        }
-
-    const FRAGMENT_BUFFER_VARIANT_TYPE type;
-    const llama_vocab::id token;
-    const std::string _dummy;
-    const std::string & raw_text;
-    const uint64_t offset;
-    const uint64_t length;
-};
-
-// #define PRETOKENIZERDEBUG
-
-static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer, bool parse_special) {
-    // for each special token
-    for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
-        const auto & data = vocab.id_to_token[special_id];
-        const auto & special_token = data.text;
-
-        if (!parse_special && (data.attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_UNKNOWN))) {
-            // Ignore control and unknown tokens when parse_special == false
-            continue;
-            // User-defined tokens are still pre-tokenized before everything else
-            // ref: https://github.com/huggingface/tokenizers/blob/fdd26ba9a3f0c133427aab0423888cbde91362d7/tokenizers/src/tokenizer/mod.rs#L726
-            // This is mostly relevant for neox-style tokenizers (mpt, olmo, stablelm, etc.)
-        }
-
-        // for each text fragment
-        std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
-        while (it != buffer.end()) {
-            auto & fragment = (*it);
-
-            // if a fragment is text ( not yet processed )
-            if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                auto & raw_text = fragment.raw_text;
-
-                auto raw_text_base_offset = fragment.offset;
-                auto raw_text_base_length = fragment.length;
-
-                // loop over the text
-                while (true) {
-                    // find the first occurrence of a given special token in this fragment
-                    //  passing offset argument only limit the "search area" but match coordinates
-                    //  are still relative to the source full raw_text
-                    auto match = raw_text.find(special_token, raw_text_base_offset);
-
-                    // no occurrences found, stop processing this fragment for a given special token
-                    if (match == std::string::npos) break;
-
-                    // check if match is within bounds of offset <-> length
-                    if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;
-
-#ifdef PRETOKENIZERDEBUG
-                    LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
-#endif
-                    auto source = std::distance(buffer.begin(), it);
-
-                    // if match is further than base offset
-                    //  then we have some text to the left of it
-                    if (match > raw_text_base_offset) {
-                        // left
-                        const int64_t left_reminder_offset = raw_text_base_offset + 0;
-                        int64_t left_reminder_length = match - raw_text_base_offset;
-
-                        if (data.attr & LLAMA_TOKEN_ATTR_LSTRIP) {
-                            while (left_reminder_length > 0 && isspace(raw_text[left_reminder_offset + left_reminder_length - 1])) {
-                                left_reminder_length--;
-                            }
-                        }
-
-                        if (left_reminder_length > 0) {
-                            buffer.emplace_after(it, raw_text, left_reminder_offset, left_reminder_length);
-                            it++;
-                        }
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
-#endif
-                    }
-
-                    // special token
-                    buffer.emplace_after(it, special_id);
-                    it++;
-
-                    // right
-                    if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
-                        int64_t right_reminder_offset = match + special_token.length();
-                        int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
-
-                        if (data.attr & LLAMA_TOKEN_ATTR_RSTRIP) {
-                            while (right_reminder_length > 0 && isspace(raw_text[right_reminder_offset])) {
-                                right_reminder_offset++;
-                                right_reminder_length--;
-                            }
-                        }
-
-                        if (right_reminder_length > 0) {
-                            buffer.emplace_after(it, raw_text, right_reminder_offset, right_reminder_length);
-                            it++;
-                        }
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
-#endif
-
-                        if (source == 0) {
-                            buffer.erase_after(buffer.before_begin());
-                        } else {
-                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
-                        }
-
-                        // repeat for the right side
-                        raw_text_base_offset = right_reminder_offset;
-                        raw_text_base_length = right_reminder_length;
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
-#endif
-                    } else {
-                        if (source == 0) {
-                            buffer.erase_after(buffer.before_begin());
-                        } else {
-                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
-                        }
-                        break;
-                    }
-                }
-            }
-            it++;
-        }
-    }
-}
-
-static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) {
-    std::vector<llama_vocab::id> output;
-    std::forward_list<fragment_buffer_variant> fragment_buffer;
-
-    if (!raw_text.empty()) {
-        fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
-        tokenizer_st_partition(vocab, fragment_buffer, parse_special);
-    }
-
-    switch (vocab.type) {
-        case LLAMA_VOCAB_TYPE_SPM:
-            {
-                // OG tokenizer behavior:
-                //
-                // tokenizer.encode('', add_special_tokens=True)  returns [1]
-                // tokenizer.encode('', add_special_tokens=False) returns []
-
-                bool is_prev_special = true;  // prefix with space if first token
-
-                if (add_special && vocab.tokenizer_add_bos) {
-                    GGML_ASSERT(vocab.special_bos_id != -1);
-                    output.push_back(vocab.special_bos_id);
-                    is_prev_special = true;
-                }
-
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-
-                        // prefix with space if previous is special
-                        if (vocab.tokenizer_add_space_prefix && is_prev_special) {
-                            raw_text = " " + raw_text;
-                        }
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-                        llm_tokenizer_spm tokenizer(vocab);
-                        llama_escape_whitespace(raw_text);
-                        tokenizer.tokenize(raw_text, output);
-                        is_prev_special = false;
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
-                        is_prev_special = true;
-                    }
-                }
-
-                if (add_special && vocab.tokenizer_add_bos && output.size() >= 2 && output[1] == vocab.special_bos_id) {
-                    LLAMA_LOG_WARN(
-                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
-                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
-                        "Are you sure this is what you want?\n", __FUNCTION__);
-                }
-
-                if (add_special && vocab.tokenizer_add_eos) {
-                    GGML_ASSERT(vocab.special_eos_id != -1);
-                    output.push_back(vocab.special_eos_id);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_BPE:
-            {
-                llm_tokenizer_bpe tokenizer(vocab);
-
-                if (add_special) {
-                    tokenizer.append_bos(output);
-                }
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-                        tokenizer.tokenize(raw_text, output);
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        tokenizer.append(fragment.token, output);
-                    }
-                }
-
-                if (add_special) {
-                    tokenizer.append_eos(output);
-                    tokenizer.check_double_bos_eos(output);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_WPM:
-            {
-                if (add_special) {
-                    GGML_ASSERT(vocab.special_cls_id != -1);
-                    output.push_back(vocab.special_cls_id);
-                }
-
-                llm_tokenizer_wpm tokenizer(vocab);
-
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-                        tokenizer.tokenize(raw_text, output);
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
-                    }
-                }
-
-                if (add_special) {
-                    GGML_ASSERT(vocab.special_sep_id != -1);
-                    output.push_back(vocab.special_sep_id);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_UGM:
-            {
-                llm_tokenizer_ugm tokenizer(vocab);
-
-                if (add_special && vocab.tokenizer_add_bos != 0) {
-                    GGML_ASSERT(vocab.special_bos_id != -1);
-                    output.push_back(vocab.special_bos_id);
-                }
-
-                for (const auto & fragment : fragment_buffer) {
-                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
-#ifdef PRETOKENIZERDEBUG
-                        LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
-#endif
-                        tokenizer.tokenize(raw_text, output);
-                    } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
-                        output.push_back(fragment.token);
-                    }
-                }
-
-                if (add_special && vocab.tokenizer_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) {
-                    LLAMA_LOG_WARN(
-                        "%s: Added a BOS token to the prompt as specified by the model but the prompt "
-                        "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. "
-                        "Are you sure this is what you want?\n", __FUNCTION__);
-                }
-
-                if (add_special && vocab.tokenizer_add_eos == 1) {
-                    GGML_ASSERT(vocab.special_eos_id != -1);
-                    output.push_back(vocab.special_eos_id);
-                }
-            } break;
-        case LLAMA_VOCAB_TYPE_NONE:
-            GGML_ASSERT(false);
-    }
-
-    return output;
-}
-
-//
-// grammar - internal
-//
-
-
-// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
-// pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
-std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
-        const std::string & src,
-        llama_partial_utf8   partial_start) {
-    static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
-    const char          * pos      = src.c_str();
-    std::vector<uint32_t> code_points;
-    // common english strings have the same number of codepoints and bytes. `+ 1` for the terminating 0.
-    code_points.reserve(src.size() + 1);
-    uint32_t              value    = partial_start.value;
-    int                   n_remain = partial_start.n_remain;
-
-    // continue previous decode, if applicable
-    while (*pos != 0 && n_remain > 0) {
-        uint8_t next_byte = static_cast<uint8_t>(*pos);
-        if ((next_byte >> 6) != 2) {
-            // invalid sequence, abort
-            code_points.push_back(0);
-            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
-        }
-        value = (value << 6) + (next_byte & 0x3F);
-        ++pos;
-        --n_remain;
-    }
-
-    if (partial_start.n_remain > 0 && n_remain == 0) {
-        code_points.push_back(value);
-    }
-
-    // decode any subsequent utf-8 sequences, which may end in an incomplete one
-    while (*pos != 0) {
-        uint8_t  first_byte = static_cast<uint8_t>(*pos);
-        uint8_t  highbits   = first_byte >> 4;
-                 n_remain   = lookup[highbits] - 1;
-
-        if (n_remain < 0) {
-            // invalid sequence, abort
-            code_points.clear();
-            code_points.push_back(0);
-            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
-        }
-
-        uint8_t  mask       = (1 << (7 - n_remain)) - 1;
-                 value      = first_byte & mask;
-        ++pos;
-        while (*pos != 0 && n_remain > 0) {
-            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
-            ++pos;
-            --n_remain;
-        }
-        if (n_remain == 0) {
-            code_points.push_back(value);
-        }
-    }
-    code_points.push_back(0);
-
-    return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
-}
-
-// returns true iff pos points to the end of one of the definitions of a rule
-static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
-    switch (pos->type) {
-        case LLAMA_GRETYPE_END: return true;  // NOLINT
-        case LLAMA_GRETYPE_ALT: return true;  // NOLINT
-        default:                return false;
-    }
-}
-
-// returns true iff chr satisfies the char range at pos (regular or inverse range)
-// asserts that pos is pointing to a char range element
-static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
-        const llama_grammar_element * pos,
-        const uint32_t                chr) {
-
-    bool found            = false;
-    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
-
-    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT
-
-    do {
-        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
-            // inclusive range, e.g. [a-z]
-            found = found || (pos->value <= chr && chr <= pos[1].value);
-            pos += 2;
-        } else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
-            // Any character matches "."
-            found = true;
-            pos += 1;
-        } else {
-            // exact char match, e.g. [a] or "a"
-            found = found || pos->value == chr;
-            pos += 1;
-        }
-    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
-
-    return std::make_pair(found == is_positive_char, pos);
-}
-
-// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
-// range at pos (regular or inverse range)
-// asserts that pos is pointing to a char range element
-static bool llama_grammar_match_partial_char(
-        const llama_grammar_element * pos,
-        const llama_partial_utf8      partial_utf8) {
-
-    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
-    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
-
-    uint32_t partial_value = partial_utf8.value;
-    int      n_remain      = partial_utf8.n_remain;
-
-    // invalid sequence or 7-bit char split across 2 bytes (overlong)
-    if (n_remain < 0 || (n_remain == 1 && partial_value < 2)) {
-        return false;
-    }
-
-    // range of possible code points this partial UTF-8 sequence could complete to
-    uint32_t low  = partial_value << (n_remain * 6);
-    uint32_t high = low | ((1 << (n_remain * 6)) - 1);
-
-    if (low == 0) {
-        if (n_remain == 2) {
-            low = 1 << 11;
-        } else if (n_remain == 3) {
-            low = 1 << 16;
-        }
-    }
-
-    do {
-        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
-            // inclusive range, e.g. [a-z]
-            if (pos->value <= high && low <= pos[1].value) {
-                return is_positive_char;
-            }
-            pos += 2;
-        } else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
-            // Any character matches "."
-            return true;
-        } else {
-            // exact char match, e.g. [a] or "a"
-            if (low <= pos->value && pos->value <= high) {
-                return is_positive_char;
-            }
-            pos += 1;
-        }
-    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
-
-    return !is_positive_char;
-}
-
-
-// transforms a grammar pushdown stack into N possible stacks, all ending
-// at a character range (terminal element)
-static void llama_grammar_advance_stack(
-        const std::vector<std::vector<llama_grammar_element>>   & rules,
-        const std::vector<const llama_grammar_element *>        & stack,
-        std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
-
-    if (stack.empty()) {
-        if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
-            new_stacks.emplace_back(stack);
-        }
-        return;
-    }
-
-    const llama_grammar_element * pos = stack.back();
-
-    switch (pos->type) {
-        case LLAMA_GRETYPE_RULE_REF: {
-            const size_t                  rule_id = static_cast<size_t>(pos->value);
-            const llama_grammar_element * subpos  = rules[rule_id].data();
-            do {
-                // init new stack without the top (pos)
-                std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
-                if (!llama_grammar_is_end_of_sequence(pos + 1)) {
-                    // if this rule ref is followed by another element, add that to stack
-                    new_stack.push_back(pos + 1);
-                }
-                if (!llama_grammar_is_end_of_sequence(subpos)) {
-                    // if alternate is nonempty, add to stack
-                    new_stack.push_back(subpos);
-                }
-                llama_grammar_advance_stack(rules, new_stack, new_stacks);
-                while (!llama_grammar_is_end_of_sequence(subpos)) {
-                    // scan to end of alternate def
-                    subpos++;
-                }
-                if (subpos->type == LLAMA_GRETYPE_ALT) {
-                    // there's another alternate def of this rule to process
-                    subpos++;
-                } else {
-                    break;
-                }
-            } while (true);
-            break;
-        }
-        case LLAMA_GRETYPE_CHAR:
-        case LLAMA_GRETYPE_CHAR_NOT:
-        case LLAMA_GRETYPE_CHAR_ANY:
-            if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
-                // only add the stack if it's not a duplicate of one we already have
-                new_stacks.emplace_back(stack);
-            }
-            break;
-        default:
-            // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
-            // (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
-            // those
-            GGML_ASSERT(false);
-    }
-}
-
-// takes a set of possible pushdown stacks on a grammar, which are required to
-// be positioned at a character range (see `llama_grammar_advance_stack`), and
-// produces the N possible stacks if the given char is accepted at those
-// positions
-void llama_grammar_accept(
-        const std::vector<std::vector<llama_grammar_element>>         & rules,
-        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
-        const uint32_t                                                  chr,
-        std::vector<std::vector<const llama_grammar_element *>>       & new_stacks) {
-
-    new_stacks.clear();
-
-    for (const auto & stack : stacks) {
-        if (stack.empty()) {
-            continue;
-        }
-
-        auto match = llama_grammar_match_char(stack.back(), chr);
-        if (match.first) {
-            const llama_grammar_element * pos = match.second;
-
-            // update top of stack to next element, if any
-            std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
-            if (!llama_grammar_is_end_of_sequence(pos)) {
-                new_stack.push_back(pos);
-            }
-            llama_grammar_advance_stack(rules, new_stack, new_stacks);
-        }
-    }
-}
-
-static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
-        const std::vector<std::vector<llama_grammar_element>>         & rules,
-        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
-        const std::vector<llama_grammar_candidate>                    & candidates);
-
-static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
-        const std::vector<std::vector<llama_grammar_element>> & rules,
-        const std::vector<const llama_grammar_element *>      & stack,
-        const std::vector<llama_grammar_candidate>            & candidates) {
-
-    std::vector<llama_grammar_candidate> rejects;
-    rejects.reserve(candidates.size());
-
-    if (stack.empty()) {
-        for (const auto & tok : candidates) {
-            if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) {
-                rejects.push_back(tok);
-            }
-        }
-        return rejects;
-    }
-
-    const llama_grammar_element * stack_pos = stack.back();
-
-    std::vector<llama_grammar_candidate> next_candidates;
-    next_candidates.reserve(candidates.size());
-
-    for (const auto & tok : candidates) {
-        if (*tok.code_points == 0) {
-            // reached end of full codepoints in token, reject iff it ended in a partial sequence
-            // that cannot satisfy this position in grammar
-            if (tok.partial_utf8.n_remain != 0 &&
-                    !llama_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
-                rejects.push_back(tok);
-            }
-        } else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
-            next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
-        } else {
-            rejects.push_back(tok);
-        }
-    }
-
-    const auto * stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;
-
-    // update top of stack to next element, if any
-    std::vector<const llama_grammar_element *> stack_after(stack.begin(), stack.end() - 1);
-    if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
-        stack_after.push_back(stack_pos_after);
-    }
-    std::vector<std::vector<const llama_grammar_element *>> next_stacks;
-    llama_grammar_advance_stack(rules, stack_after, next_stacks);
-
-    auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
-    for (const auto & tok : next_rejects) {
-        rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
-    }
-
-    return rejects;
-}
-
-static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
-        const std::vector<std::vector<llama_grammar_element>>         & rules,
-        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
-        const std::vector<llama_grammar_candidate>                    & candidates) {
-    GGML_ASSERT(!stacks.empty()); // REVIEW
-
-    if (candidates.empty()) {
-        return std::vector<llama_grammar_candidate>();
-    }
-
-    auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);
-
-    for (size_t i = 1, size = stacks.size(); i < size; ++i) {
-        rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
-    }
-    return rejects;
-}
-
-static bool llama_grammar_detect_left_recursion(
-        const std::vector<std::vector<llama_grammar_element>> & rules,
-        size_t                                                  rule_index,
-        std::vector<bool>                                     * rules_visited,
-        std::vector<bool>                                     * rules_in_progress,
-        std::vector<bool>                                     * rules_may_be_empty) {
-    if ((*rules_in_progress)[rule_index]) {
-        return true;
-    }
-
-    (*rules_in_progress)[rule_index] = true;
-
-    const std::vector<llama_grammar_element> & rule = rules[rule_index];
-
-    // First check if the rule might produce the empty string. This could be done combined with the second
-    // step but it's more readable as two steps.
-    bool at_rule_start = true;
-    for (size_t i = 0; i < rule.size(); i++) {
-        if (llama_grammar_is_end_of_sequence(&rule[i])) {
-            if (at_rule_start) {
-                (*rules_may_be_empty)[rule_index] = true;
-                break;
-            }
-            at_rule_start = true;
-        } else {
-            at_rule_start = false;
-        }
-    }
-
-    // Second, recurse into leftmost nonterminals (or next-leftmost as long as the previous nonterminal may
-    // be empty)
-    bool recurse_into_nonterminal = true;
-    for (size_t i = 0; i < rule.size(); i++) {
-        if (rule[i].type == LLAMA_GRETYPE_RULE_REF && recurse_into_nonterminal) {
-            if (llama_grammar_detect_left_recursion(rules, (size_t)rule[i].value, rules_visited, rules_in_progress, rules_may_be_empty)) {
-                return true;
-            }
-            if (!((*rules_may_be_empty)[(size_t)rule[i].value])) {
-                recurse_into_nonterminal = false;
-            }
-        } else if (llama_grammar_is_end_of_sequence(&rule[i])) {
-            recurse_into_nonterminal = true;
-        } else {
-            recurse_into_nonterminal = false;
-        }
-    }
-
-    (*rules_in_progress)[rule_index] = false;
-    (*rules_visited)[rule_index] = true;
-    return false;
-}
-
-//
-// grammar - external
-//
-
-struct llama_grammar * llama_grammar_init(
-            const llama_grammar_element ** rules,
-                                 size_t    n_rules,
-                                 size_t    start_rule_index) {
-    const llama_grammar_element * pos;
-
-    // copy rule definitions into vectors
-    std::vector<std::vector<llama_grammar_element>> vec_rules(n_rules);
-    for (size_t i = 0; i < n_rules; i++) {
-        for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
-            vec_rules[i].push_back(*pos);
-        }
-        vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
-    }
-
-    // Check for left recursion
-    std::vector<bool> rules_visited(n_rules);
-    std::vector<bool> rules_in_progress(n_rules);
-    std::vector<bool> rules_may_be_empty(n_rules);
-    for (size_t i = 0; i < n_rules; i++) {
-        if (rules_visited[i]) {
-            continue;
-        }
-        if (llama_grammar_detect_left_recursion(vec_rules, i, &rules_visited, &rules_in_progress, &rules_may_be_empty)) {
-            LLAMA_LOG_ERROR("unsupported grammar, left recursion detected for nonterminal at index %zu", i);
-            return nullptr;
-        }
-    }
-
-    // loop over alternates of start rule to build initial stacks
-    std::vector<std::vector<const llama_grammar_element *>> stacks;
-    pos = vec_rules[start_rule_index].data();
-    do {
-        std::vector<const llama_grammar_element *> stack;
-        if (!llama_grammar_is_end_of_sequence(pos)) {
-            // if alternate is nonempty, add to stack
-            stack.push_back(pos);
-        }
-        llama_grammar_advance_stack(vec_rules, stack, stacks);
-        while (!llama_grammar_is_end_of_sequence(pos)) {
-            // scan to end of alternate def
-            pos++;
-        }
-        if (pos->type == LLAMA_GRETYPE_ALT) {
-            // there's another alternate def of this rule to process
-            pos++;
-        } else {
-            break;
-        }
-    } while (true);
-
-    // Important: vec_rules has to be moved here, not copied, because stacks contains
-    // pointers to elements of vec_rules. If vec_rules were copied into llama_grammar
-    // then the pointers would be invalidated when the local vec_rules goes out of scope.
-    return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
-}
-
-void llama_grammar_free(struct llama_grammar * grammar) {
-    delete grammar;
-}
-
-struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
-    llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };
-
-    // redirect elements in stacks to point to new rules
-    for (size_t is = 0; is < result->stacks.size(); is++) {
-        for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
-            for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
-                for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
-                    if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
-                         result->stacks[is][ie]  =  &result->rules[ir0][ir1];
-                    }
-                }
-            }
-        }
-    }
-
-    return result;
-}
-
-//
-// sampling
-//
-
-void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
-    if (seed == LLAMA_DEFAULT_SEED) {
-        seed = time(NULL);
-    }
-    ctx->rng.seed(seed);
-}
-
-void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
-    GGML_ASSERT(candidates->size > 0);
-
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    // Sort the logits in descending order
-    if (!candidates->sorted) {
-        std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
-            return a.logit > b.logit;
-        });
-        candidates->sorted = true;
-    }
-
-    float max_l = candidates->data[0].logit;
-    float cum_sum = 0.0f;
-    for (size_t i = 0; i < candidates->size; ++i) {
-        float p = expf(candidates->data[i].logit - max_l);
-        candidates->data[i].p = p;
-        cum_sum += p;
-    }
-    for (size_t i = 0; i < candidates->size; ++i) {
-        candidates->data[i].p /= cum_sum;
-    }
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
-
-void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
-    // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
-    // if (k >= (int32_t)candidates->size) {
-    //     return;
-    // }
-
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    if (k <= 0) {
-        k = candidates->size;
-    }
-
-    k = std::max(k, (int) min_keep);
-    k = std::min(k, (int) candidates->size);
-
-    // Sort scores in descending order
-    if (!candidates->sorted) {
-        auto comp = [](const llama_token_data & a, const llama_token_data & b) {
-            return a.logit > b.logit;
-        };
-        if (k <= 128) {
-            std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
-        } else {
-            constexpr int   nbuckets     = 128;
-            constexpr float bucket_low   = -10.0f;
-            constexpr float bucket_high  =  10.0f;
-            constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
-            constexpr float bucker_inter = -bucket_low * bucket_scale;
-
-            std::vector<int> bucket_idx(candidates->size);
-            std::vector<int> histo(nbuckets, 0);
-
-            for (int i = 0; i < (int)candidates->size; ++i) {
-                const float val = candidates->data[i].logit;
-                int ib = int(bucket_scale * val + bucker_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
-                ib = std::max(0, std::min(nbuckets-1, ib));
-                bucket_idx[i] = ib;
-                ++histo[ib];
-            }
-            int nhave = 0;
-            int ib = nbuckets - 1;
-            for ( ; ib >= 0; --ib) {
-                nhave += histo[ib];
-                if (nhave >= k) break;
-            }
-            std::vector<llama_token_data> tmp_tokens(nhave);
-            auto ptr = tmp_tokens.data();
-            std::vector<llama_token_data*> bucket_ptrs;
-            bucket_ptrs.reserve(nbuckets - ib);
-            for (int j = nbuckets - 1; j >= ib; --j) {
-                bucket_ptrs.push_back(ptr);
-                ptr += histo[j];
-            }
-            for (int i = 0; i < (int)candidates->size; ++i) {
-                int j = bucket_idx[i];
-                if (j >= ib) {
-                    *bucket_ptrs[nbuckets-1-j]++ = candidates->data[i];
-                }
-            }
-
-            ptr = tmp_tokens.data();
-            int ndone = 0;
-            for (int j = nbuckets-1; j > ib; --j) {
-                std::sort(ptr, ptr + histo[j], comp);
-                ptr += histo[j];
-                ndone += histo[j];
-            }
-            std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp);
-
-            std::memcpy(candidates->data, tmp_tokens.data(), k*sizeof(llama_token_data));
-
-        }
-        candidates->sorted = true;
-    }
-    candidates->size = k;
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
-
-void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
-    if (p >= 1.0f) {
-        return;
-    }
-
-    llama_sample_softmax(ctx, candidates);
-
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    // Compute the cumulative probabilities
-    float cum_sum = 0.0f;
-    size_t last_idx = candidates->size;
-
-    for (size_t i = 0; i < candidates->size; ++i) {
-        cum_sum += candidates->data[i].p;
-
-        // Check if the running sum is at least p or if we have kept at least min_keep tokens
-        // we set the last index to i+1 to indicate that the current iterate should be included in the set
-        if (cum_sum >= p && i + 1 >= min_keep) {
-            last_idx = i + 1;
-            break;
-        }
-    }
-
-    // Resize the output vector to keep only the top-p tokens
-    candidates->size = last_idx;
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
-
-void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
-    if (p <= 0.0f || !candidates->size) {
-        return;
-    }
-
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    bool min_p_applied = false;
-
-    // if the candidates aren't sorted, try the unsorted implementation first
-    if (!candidates->sorted) {
-        std::vector<llama_token_data> filtered_tokens;
-
-        float max_logit = -FLT_MAX;
-        for (size_t i = 0; i < candidates->size; ++i) {
-            max_logit = std::max(max_logit, candidates->data[i].logit);
-        }
-        const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max
-
-        for (size_t i = 0; i < candidates->size; ++i) {
-            if (candidates->data[i].logit >= min_logit) {
-                filtered_tokens.push_back(candidates->data[i]);
-            }
-        }
-
-        // if we have enough values the operation was a success
-        if (filtered_tokens.size() >= min_keep) {
-            memcpy(candidates->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
-            candidates->size = filtered_tokens.size();
-            min_p_applied = true;
-        }
-    }
-
-    // if the candidates are sorted or the unsorted implementation failed, use this implementation
-    if (!min_p_applied) {
-        // Sort the logits in descending order
-        if (!candidates->sorted) {
-            std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
-                return a.logit > b.logit;
-            });
-            candidates->sorted = true;
-        }
-
-        const float min_logit = candidates->data[0].logit + logf(p); // min logit for p_i >= p * p_max
-        size_t i = 1; // first token always matches
-
-        for (; i < candidates->size; ++i) {
-            if (candidates->data[i].logit < min_logit && i >= min_keep) {
-                break; // prob too small
-            }
-        }
-
-        // Resize the output vector to keep only the matching tokens
-        candidates->size = i;
-    }
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
-
-void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
-    if (z >= 1.0f || candidates->size <= 2) {
-        return;
-    }
-
-    llama_sample_softmax(nullptr, candidates);
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    // Compute the first and second derivatives
-    std::vector<float> first_derivatives(candidates->size - 1);
-    std::vector<float> second_derivatives(candidates->size - 2);
-
-    for (size_t i = 0; i < first_derivatives.size(); ++i) {
-        first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
-    }
-    for (size_t i = 0; i < second_derivatives.size(); ++i) {
-        second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
-    }
-
-    // Calculate absolute value of second derivatives
-    for (size_t i = 0; i < second_derivatives.size(); ++i) {
-        second_derivatives[i] = std::abs(second_derivatives[i]);
-    }
-
-    // Normalize the second derivatives
-    {
-        const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
-
-        if (second_derivatives_sum > 1e-6f) {
-            for (float & value : second_derivatives) {
-                value /= second_derivatives_sum;
-            }
-        } else {
-            for (float & value : second_derivatives) {
-                value = 1.0f / second_derivatives.size();
-            }
-        }
-    }
-
-    float cum_sum = 0.0f;
-    size_t last_idx = candidates->size;
-    for (size_t i = 0; i < second_derivatives.size(); ++i) {
-        cum_sum += second_derivatives[i];
-
-        // Check if the running sum is greater than z or if we have kept at least min_keep tokens
-        if (cum_sum > z && i >= min_keep) {
-            last_idx = i;
-            break;
-        }
-    }
-
-    // Resize the output vector to keep only the tokens above the tail location
-    candidates->size = last_idx;
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
-
-void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
-    // Reference implementation:
-    // https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
-    if (p >= 1.0f) {
-        return;
-    }
-
-    // Compute the softmax of logits and calculate entropy
-    llama_sample_softmax(nullptr, candidates);
-
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    float entropy = 0.0f;
-    for (size_t i = 0; i < candidates->size; ++i) {
-        entropy += -candidates->data[i].p * logf(candidates->data[i].p);
-    }
-
-    // Compute the absolute difference between negative log probability and entropy for each candidate
-    std::vector<float> shifted_scores;
-    for (size_t i = 0; i < candidates->size; ++i) {
-        float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
-        shifted_scores.push_back(shifted_score);
-    }
-
-    // Sort tokens based on the shifted_scores and their corresponding indices
-    std::vector<size_t> indices(candidates->size);
-    std::iota(indices.begin(), indices.end(), 0);
-
-    std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
-        return shifted_scores[a] < shifted_scores[b];
-    });
-
-    // Compute the cumulative probabilities
-    float cum_sum = 0.0f;
-    size_t last_idx = indices.size();
-
-    for (size_t i = 0; i < indices.size(); ++i) {
-        size_t idx = indices[i];
-        cum_sum += candidates->data[idx].p;
-
-        // Check if the running sum is greater than typical or if we have kept at least min_keep tokens
-        if (cum_sum > p && i >= min_keep - 1) {
-            last_idx = i + 1;
-            break;
-        }
-    }
-
-    // Resize the output vector to keep only the locally typical tokens
-    std::vector<llama_token_data> new_candidates;
-    for (size_t i = 0; i < last_idx; ++i) {
-        size_t idx = indices[i];
-        new_candidates.push_back(candidates->data[idx]);
-    }
-
-    // Replace the data in candidates with the new_candidates data
-    std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
-    candidates->size = new_candidates.size();
-    candidates->sorted = false;
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
-
-void llama_sample_entropy(struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val) {
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    // no need to do anything if there is only one (or zero) candidates
-    if(candidates_p->size <= 1) {
-        return;
-    }
-
-    // Calculate maximum possible entropy
-    float max_entropy = -logf(1.0f / candidates_p->size);
-
-    llama_sample_softmax(nullptr, candidates_p);
-
-    // Calculate entropy of the softmax probabilities
-    float entropy = 0.0f;
-    for (size_t i = 0; i < candidates_p->size; ++i) {
-        float prob = candidates_p->data[i].p;
-        if (prob > 0.0f) { // Ensure no log(0)
-            entropy -= prob * logf(prob);
-        }
-    }
-
-    // Normalize the entropy (max_entropy cannot be 0 here because we checked candidates_p->size != 1 above)
-    float normalized_entropy = entropy / max_entropy;
-
-    // Map the normalized entropy to the desired temperature range using the power function
-    float dyn_temp = min_temp + (max_temp - min_temp) * powf(normalized_entropy, exponent_val);
-
-#ifdef DEBUG
-    LLAMA_LOG_INFO("Your text maxtemp value is: %f\n", max_temp);
-    LLAMA_LOG_INFO("Entropy: %f\n", entropy);
-    LLAMA_LOG_INFO("Max Possible Entropy: %f\n", max_entropy);
-    LLAMA_LOG_INFO("Normalized Entropy: %f\n", normalized_entropy);
-    LLAMA_LOG_INFO("Exponent: %f\n", exponent_val);
-    LLAMA_LOG_INFO("Dynamic Temperature (dyn_temp): %f\n", dyn_temp);
-#endif
-
-    // Apply the dynamically calculated temperature scaling
-    for (size_t i = 0; i < candidates_p->size; ++i) {
-        candidates_p->data[i].logit /= dyn_temp;
-    }
-
-    // Re-compute softmax probabilities after scaling logits with dynamic temperature
-    double max_l_double = candidates_p->data[0].logit;
-    double cum_sum_double = 0.0;
-    for (size_t i = 0; i < candidates_p->size; ++i) {
-        double p = exp(candidates_p->data[i].logit - max_l_double);
-        candidates_p->data[i].p = p; // Store the scaled probability
-        cum_sum_double += p;
-    }
-    for (size_t i = 0; i < candidates_p->size; ++i) {
-        candidates_p->data[i].p /= cum_sum_double; // Re-normalize the probabilities
-    }
-
-#ifdef DEBUG
-    // Print the updated top 25 probabilities after temperature scaling
-    LLAMA_LOG_INFO("\nUpdated Top 25 Probabilities After Dynamic Temperature Scaling (in percentages):\n");
-    for (size_t i = 0; i < 25 && i < candidates_p->size; ++i) {
-        LLAMA_LOG_INFO("Token %zu: %f%%\n", i + 1, candidates_p->data[i].p * 100.0f);
-    }
-#endif
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
-
-void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    for (size_t i = 0; i < candidates_p->size; ++i) {
-        candidates_p->data[i].logit /= temp;
-    }
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
-
-void llama_sample_repetition_penalties(
-            struct llama_context * ctx,
-          llama_token_data_array * candidates,
-               const llama_token * last_tokens,
-                          size_t   penalty_last_n,
-                           float   penalty_repeat,
-                           float   penalty_freq,
-                           float   penalty_present) {
-    if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) {
-        return;
-    }
-
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    // Create a frequency map to count occurrences of each token in last_tokens
-    std::unordered_map<llama_token, int> token_count;
-    for (size_t i = 0; i < penalty_last_n; ++i) {
-        token_count[last_tokens[i]]++;
-    }
-
-    // Apply frequency and presence penalties to the candidates
-    for (size_t i = 0; i < candidates->size; ++i) {
-        const auto token_iter = token_count.find(candidates->data[i].id);
-        if (token_iter == token_count.end()) {
-            continue;
-        }
-
-        const int count = token_iter->second;
-
-        // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
-        // This is common fix for this problem, which is to multiply by the penalty instead of dividing.
-        if (candidates->data[i].logit <= 0) {
-            candidates->data[i].logit *= penalty_repeat;
-        } else {
-            candidates->data[i].logit /= penalty_repeat;
-        }
-
-        candidates->data[i].logit -= float(count) * penalty_freq + float(count > 0) * penalty_present;
-    }
-
-    candidates->sorted = false;
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-}
-
-void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
-    GGML_ASSERT(ctx);
-    int64_t t_start_sample_us = ggml_time_us();
-
-    bool allow_eog = false;
-    for (const auto & stack : grammar->stacks) {
-        if (stack.empty()) {
-            allow_eog = true;
-            break;
-        }
-    }
-
-    std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
-    candidates_decoded.reserve(candidates->size);
-
-    std::vector<llama_grammar_candidate> candidates_grammar;
-    candidates_grammar.reserve(candidates->size);
-
-    for (size_t i = 0; i < candidates->size; ++i) {
-        const llama_token id      = candidates->data[i].id;
-        const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(id);
-
-        if (llama_token_is_eog(&ctx->model, id)) {
-            if (!allow_eog) {
-                candidates->data[i].logit = -INFINITY;
-            }
-        } else if (piece.empty() || piece[0] == 0) {
-            candidates->data[i].logit = -INFINITY;
-        } else {
-            candidates_decoded.push_back(decode_utf8(piece, grammar->partial_utf8));
-            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
-        }
-    }
-
-    const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
-    for (const auto & reject : rejects) {
-        candidates->data[reject.index].logit = -INFINITY;
-    }
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-}
-
-static void llama_log_softmax(float * array, size_t size) {
-    float max_l = *std::max_element(array, array + size);
-    float sum = 0.f;
-    for (size_t i = 0; i < size; ++i) {
-        float p = expf(array[i] - max_l);
-        sum += p;
-        array[i] = p;
-    }
-
-    for (size_t i = 0; i < size; ++i) {
-        array[i] = logf(array[i] / sum);
-    }
-}
-
-void llama_sample_apply_guidance(
-          struct llama_context * ctx,
-                         float * logits,
-                         float * logits_guidance,
-                         float   scale) {
-    GGML_ASSERT(ctx);
-
-    const auto t_start_sample_us = ggml_time_us();
-    const auto n_vocab = llama_n_vocab(llama_get_model(ctx));
-
-    llama_log_softmax(logits, n_vocab);
-    llama_log_softmax(logits_guidance, n_vocab);
-
-    for (int i = 0; i < n_vocab; ++i) {
-              auto & l = logits[i];
-        const auto & g = logits_guidance[i];
-
-        l = scale * (l - g) + g;
-    }
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-}
-
-llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
-    GGML_ASSERT(ctx);
-
-    auto N = float(llama_n_vocab(llama_get_model(ctx)));
-    int64_t t_start_sample_us;
-    t_start_sample_us = ggml_time_us();
-
-    llama_sample_softmax(nullptr, candidates);
-
-    // Estimate s_hat using the most probable m tokens
-    float s_hat = 0.0;
-    float sum_ti_bi = 0.0;
-    float sum_ti_sq = 0.0;
-    for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
-        float t_i = logf(float(i + 2) / float(i + 1));
-        float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
-        sum_ti_bi += t_i * b_i;
-        sum_ti_sq += t_i * t_i;
-    }
-    s_hat = sum_ti_bi / sum_ti_sq;
-
-    // Compute k from the estimated s_hat and target surprise value
-    float epsilon_hat = s_hat - 1;
-    float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
-
-    // Sample the next word X using top-k sampling
-    llama_sample_top_k(nullptr, candidates, int(k), 1);
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    llama_token X = llama_sample_token(ctx, candidates);
-    t_start_sample_us = ggml_time_us();
-
-    // Compute error as the difference between observed surprise and target surprise value
-    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
-        return candidate.id == X;
-    }));
-    float observed_surprise = -log2f(candidates->data[X_idx].p);
-    float e = observed_surprise - tau;
-
-    // Update mu using the learning rate and error
-    *mu = *mu - eta * e;
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    return X;
-}
-
-llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
-    int64_t t_start_sample_us;
-    t_start_sample_us = ggml_time_us();
-
-    llama_sample_softmax(ctx, candidates);
-
-    // Truncate the words with surprise values greater than mu
-    candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
-        return -log2f(candidate.p) > *mu;
-    }));
-
-    if (candidates->size == 0) {
-        candidates->size = 1;
-    }
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-
-    // Normalize the probabilities of the remaining words
-    llama_sample_softmax(ctx, candidates);
-
-    // Sample the next word X from the remaining words
-    llama_token X = llama_sample_token(ctx, candidates);
-    t_start_sample_us = ggml_time_us();
-
-    // Compute error as the difference between observed surprise and target surprise value
-    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
-        return candidate.id == X;
-    }));
-    float observed_surprise = -log2f(candidates->data[X_idx].p);
-    float e = observed_surprise - tau;
-
-    // Update mu using the learning rate and error
-    *mu = *mu - eta * e;
-
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
-    return X;
-}
-
-llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    // Find max element
-    auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
-        return a.logit < b.logit;
-    });
-
-    llama_token result = max_iter->id;
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-        ctx->n_sample++;
-    }
-    return result;
-}
-
-llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng) {
-    GGML_ASSERT(ctx);
-
-    const int64_t t_start_sample_us = ggml_time_us();
-    llama_sample_softmax(nullptr, candidates);
-
-    std::vector<float> probs;
-    probs.reserve(candidates->size);
-    for (size_t i = 0; i < candidates->size; ++i) {
-        probs.push_back(candidates->data[i].p);
-    }
-
-    std::discrete_distribution<> dist(probs.begin(), probs.end());
-    int idx = dist(rng);
-
-    llama_token result = candidates->data[idx].id;
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    ctx->n_sample++;
-    return result;
-}
-
-llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
-    return llama_sample_token_with_rng(ctx, candidates, ctx->rng);
-}
-
-void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    if (llama_token_is_eog(&ctx->model, token)) {
-        for (const auto & stack : grammar->stacks) {
-            if (stack.empty()) {
-                return;
-            }
-        }
-        GGML_ASSERT(false);
-    }
-
-    const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(token);
-
-    // Note terminating 0 in decoded string
-    const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
-    const auto & code_points = decoded.first;
-    std::vector<std::vector<const llama_grammar_element *>> tmp_new_stacks;
-    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
-        llama_grammar_accept(grammar->rules, grammar->stacks, *it, tmp_new_stacks);
-        grammar->stacks = tmp_new_stacks;
-    }
-    grammar->partial_utf8 = decoded.second;
-    GGML_ASSERT(!grammar->stacks.empty());
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-}
-
-//
-// quantization
-//
-
-struct quantize_state_internal {
-    const llama_model                 & model;
-    const llama_model_quantize_params * params;
-
-    int n_attention_wv    = 0;
-    int n_ffn_down        = 0;
-    int n_ffn_gate        = 0;
-    int n_ffn_up          = 0;
-    int i_attention_wv    = 0;
-    int i_ffn_down        = 0;
-    int i_ffn_gate        = 0;
-    int i_ffn_up          = 0;
-
-    int n_k_quantized     = 0;
-    int n_fallback        = 0;
-
-    bool has_imatrix      = false;
-
-    // used to figure out if a model shares tok_embd with the output weight
-    bool has_output       = false;
-
-    quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
-        : model(model)
-        , params(params)
-        {}
-};
-
-static void llama_tensor_dequantize_internal(
-    struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
-    const size_t nelements, const int nthread
-) {
-    if (output.size() < nelements) {
-        output.resize(nelements);
-    }
-    float * f32_output = (float *) output.data();
-
-    ggml_type_traits_t qtype;
-    if (ggml_is_quantized(tensor->type)) {
-        qtype = ggml_internal_get_type_traits(tensor->type);
-        if (qtype.to_float == NULL) {
-            throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
-        }
-    } else if (tensor->type != GGML_TYPE_F16 &&
-               tensor->type != GGML_TYPE_BF16) {
-        throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
-    }
-
-    if (nthread < 2) {
-        if (tensor->type == GGML_TYPE_F16) {
-            ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
-        } else if (tensor->type == GGML_TYPE_BF16) {
-            ggml_bf16_to_fp32_row((ggml_bf16_t *)tensor->data, f32_output, nelements);
-        } else if (ggml_is_quantized(tensor->type)) {
-            qtype.to_float(tensor->data, f32_output, nelements);
-        } else {
-            GGML_ASSERT(false); // unreachable
-        }
-        return;
-    }
-
-    size_t block_size;
-    if (tensor->type == GGML_TYPE_F16 ||
-        tensor->type == GGML_TYPE_BF16) {
-        block_size = 1;
-    } else {
-        block_size = (size_t)ggml_blck_size(tensor->type);
-    }
-
-    size_t block_size_bytes = ggml_type_size(tensor->type);
-
-    GGML_ASSERT(nelements % block_size == 0);
-    size_t nblocks = nelements / block_size;
-    size_t blocks_per_thread = nblocks / nthread;
-    size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
-
-    size_t in_buff_offs = 0;
-    size_t out_buff_offs = 0;
-
-    for (int tnum = 0; tnum < nthread; tnum++) {
-        size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
-        size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
-        size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
-
-        auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
-            if (typ == GGML_TYPE_F16) {
-                ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
-            } else if (typ == GGML_TYPE_BF16) {
-                ggml_bf16_to_fp32_row((ggml_bf16_t *)inbuf, outbuf, nels);
-            } else {
-                qtype.to_float(inbuf, outbuf, nels);
-            }
-        };
-        workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
-        in_buff_offs += thr_block_bytes;
-        out_buff_offs += thr_elems;
-    }
-    for (auto & w : workers) { w.join(); }
-    workers.clear();
-}
-
-static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
-    const std::string name = ggml_get_name(tensor);
-
-    // TODO: avoid hardcoded tensor names - use the TN_* constants
-    const llm_arch arch = qs.model.arch;
-    const auto       tn = LLM_TN(arch);
-
-    auto use_more_bits = [](int i_layer, int n_layers) -> bool {
-        return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2;
-    };
-    const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
-    auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
-        if (n_expert > 1) {
-            // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly
-            // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
-            // for getting the current layer as I initially thought, and we need to resort to parsing the
-            // tensor name.
-            if (sscanf(name, "blk.%d.", &i_layer) != 1) {
-                throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
-            }
-            if (i_layer < 0 || i_layer >= n_layer) {
-                throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer));
-            }
-        }
-        return std::make_pair(i_layer, n_layer);
-    };
-
-    // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
-    // with the quantization of the output tensor
-    if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
-        if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
-            new_type = qs.params->output_tensor_type;
-        } else {
-            int nx = tensor->ne[0];
-            if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
-                new_type = GGML_TYPE_Q8_0;
-            }
-            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
-                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S  || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M   ||
-                     ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
-                new_type = GGML_TYPE_Q5_K;
-            }
-            else if (new_type != GGML_TYPE_Q8_0) {
-                new_type = GGML_TYPE_Q6_K;
-            }
-        }
-    } else if (name == "token_embd.weight") {
-        if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
-            new_type = qs.params->token_embedding_type;
-        } else {
-            if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
-                ftype == LLAMA_FTYPE_MOSTLY_IQ1_S   || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
-                new_type = GGML_TYPE_Q2_K;
-            }
-            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
-                new_type = GGML_TYPE_IQ3_S;
-            }
-            else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
-                new_type = GGML_TYPE_IQ3_S;
-            }
-            else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 ||
-                     new_type == GGML_TYPE_Q4_0_8_8) {
-                new_type = GGML_TYPE_Q4_0;
-            }
-        }
-    } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
-               ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M    || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
-        if (name.find("attn_v.weight") != std::string::npos) {
-            if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
-            else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
-            ++qs.i_attention_wv;
-        }
-        else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
-            new_type = GGML_TYPE_Q4_K;
-        }
-        else if (name.find("ffn_down") != std::string::npos) {
-            if (qs.i_ffn_down < qs.n_ffn_down/8) {
-                new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
-            }
-            ++qs.i_ffn_down;
+        else if (name.find("ffn_down") != std::string::npos) {
+            if (qs.i_ffn_down < qs.n_ffn_down/8) {
+                new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+            }
+            ++qs.i_ffn_down;
         }
         else if (name.find("attn_output.weight") != std::string::npos) {
             if (qs.model.hparams.n_expert == 8) {
@@ -19131,8 +16527,8 @@ struct llama_context * llama_new_context_with_model(
     ctx->abort_callback      = params.abort_callback;
     ctx->abort_callback_data = params.abort_callback_data;
 
-    ctx->rng                 = std::mt19937(params.seed);
-    ctx->logits_all          = params.logits_all;
+    ctx->sampling.rng = std::mt19937(params.seed);
+    ctx->logits_all   = params.logits_all;
 
     uint32_t kv_size = cparams.n_ctx;
     ggml_type type_k = params.type_k;
@@ -19408,10 +16804,18 @@ void llama_free(struct llama_context * ctx) {
     delete ctx;
 }
 
-const llama_model * llama_get_model(const struct llama_context * ctx) {
+const struct llama_model * llama_get_model(const struct llama_context * ctx) {
     return &ctx->model;
 }
 
+const struct llama_vocab * llama_get_vocab(const struct llama_context * ctx) {
+    return &ctx->model.vocab;
+}
+
+struct llama_grammar * llama_get_grammar(struct llama_context * ctx) {
+    return &ctx->grammar;
+}
+
 uint32_t llama_n_ctx(const struct llama_context * ctx) {
     return ctx->cparams.n_ctx;
 }
@@ -20000,7 +17404,7 @@ static void llama_state_get_data_internal(struct llama_context * ctx, llama_data
     // copy rng
     {
         std::ostringstream rng_ss;
-        rng_ss << ctx->rng;
+        rng_ss << ctx->sampling.rng;
 
         const std::string & rng_str  = rng_ss.str();
         const size_t        rng_size = rng_str.size();
@@ -20166,7 +17570,7 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) {
         std::string rng_str((const char *)inp, rng_size); inp += rng_size;
 
         std::istringstream rng_ss(rng_str);
-        rng_ss >> ctx->rng;
+        rng_ss >> ctx->sampling.rng;
 
         GGML_ASSERT(!rng_ss.fail());
     }
@@ -21112,79 +18516,81 @@ float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id
     return it->second.data();
 }
 
+//
+// vocab
+//
+
 const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
-    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return model->vocab.id_to_token[token].text.c_str();
+    return llama_token_get_text_impl(model->vocab, token);
 }
 
 float llama_token_get_score(const struct llama_model * model, llama_token token) {
-    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return model->vocab.id_to_token[token].score;
+    return llama_token_get_score_impl(model->vocab, token);
 }
 
-llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token) {
-    GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return model->vocab.id_to_token[token].attr;
+enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token) {
+    return llama_token_get_attr_impl(model->vocab, token);
 }
 
 bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
-    return token != -1 && (
-        token == llama_token_eos(model) ||
-        token == llama_token_eot(model)
-    );
+    return llama_token_is_eog_impl(model->vocab, token);
 }
 
 bool llama_token_is_control(const struct llama_model * model, llama_token token) {
-    return llama_is_control_token(model->vocab, token);
+    return llama_token_is_control_impl(model->vocab, token);
 }
 
 llama_token llama_token_bos(const struct llama_model * model) {
-    return model->vocab.special_bos_id;
+    return llama_token_bos_impl(model->vocab);
 }
 
 llama_token llama_token_eos(const struct llama_model * model) {
-    return model->vocab.special_eos_id;
+    return llama_token_eos_impl(model->vocab);
 }
 
 llama_token llama_token_cls(const struct llama_model * model) {
-    return model->vocab.special_cls_id;
+    return llama_token_cls_impl(model->vocab);
 }
 
 llama_token llama_token_sep(const struct llama_model * model) {
-    return model->vocab.special_sep_id;
+    return llama_token_sep_impl(model->vocab);
+}
+
+llama_token llama_token_nl (const struct llama_model * model) {
+    return llama_token_nl_impl(model->vocab);
 }
 
-llama_token llama_token_nl(const struct llama_model * model) {
-    return model->vocab.linefeed_id;
+llama_token llama_token_pad(const struct llama_model * model) {
+    return llama_token_pad_impl(model->vocab);
 }
 
 int32_t llama_add_bos_token(const struct llama_model * model) {
-    return model->vocab.tokenizer_add_bos;
+    return llama_add_bos_token_impl(model->vocab);
 }
 
 int32_t llama_add_eos_token(const struct llama_model * model) {
-    return model->vocab.tokenizer_add_eos;
+    return llama_add_eos_token_impl(model->vocab);
 }
 
 llama_token llama_token_prefix(const struct llama_model * model) {
-    return model->vocab.special_prefix_id;
+    return llama_token_prefix_impl(model->vocab);
 }
 
 llama_token llama_token_middle(const struct llama_model * model) {
-    return model->vocab.special_middle_id;
+    return llama_token_middle_impl(model->vocab);
 }
 
 llama_token llama_token_suffix(const struct llama_model * model) {
-    return model->vocab.special_suffix_id;
+    return llama_token_suffix_impl(model->vocab);
 }
 
 llama_token llama_token_eot(const struct llama_model * model) {
-    return model->vocab.special_eot_id;
+    return llama_token_eot_impl(model->vocab);
 }
 
-llama_token llama_token_pad(const struct llama_model * model) {
-    return model->vocab.special_pad_id;
-}
+//
+// tokenization
+//
 
 int32_t llama_tokenize(
     const struct llama_model * model,
@@ -21194,229 +18600,33 @@ int32_t llama_tokenize(
                      int32_t   n_tokens_max,
                         bool   add_special,
                         bool   parse_special) {
-    auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_special, parse_special);
-    if (n_tokens_max < (int) res.size()) {
-        // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
-        return -((int) res.size());
-    }
-
-    for (size_t i = 0; i < res.size(); i++) {
-        tokens[i] = res[i];
-    }
-
-    return res.size();
-}
-
-static std::string llama_decode_text(const std::string & text) {
-    std::string decoded_text;
-
-    const auto cpts = unicode_cpts_from_utf8(text);
-    for (const auto cpt : cpts) {
-        const auto utf8 = unicode_cpt_to_utf8(cpt);
-        try {
-            decoded_text += unicode_utf8_to_byte(utf8);
-        } catch (const std::out_of_range & /*e*/) {
-            decoded_text += "[UNK_BYTE_0x";
-            for (const auto c : utf8) {
-                decoded_text += format("%02x", (uint8_t) c);
-            }
-            decoded_text += text + "]";
-        }
-    }
-
-    return decoded_text;
+    return llama_tokenize_impl(model->vocab, text, text_len, tokens, n_tokens_max, add_special, parse_special);
 }
 
-// does not write null-terminator to buf
-int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, int32_t lstrip, bool special) {
-    // ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843
-    static const int attr_special = LLAMA_TOKEN_ATTR_UNKNOWN | LLAMA_TOKEN_ATTR_CONTROL;
-    const llama_token_attr attr = llama_token_get_attr(model, token);
-    if (!special && (attr & attr_special)) {
-        return 0;
-    }
-
-    // copy piece chars to output text buffer
-    // skip up to 'lstrip' leading spaces before copying
-    auto _try_copy = [=] (const char * token, size_t size) -> int32_t {
-        for (int32_t i = 0; i < lstrip && size && *token == ' '; ++i) {
-            token++;
-            size--;
-        }
-        if (length < (int32_t)size) {
-            return -(int32_t) size;
-        }
-        memcpy(buf, token, size);
-        return (int32_t) size;
-    };
-
-    // if we have a cache - use it
-    {
-        const auto & cache = model->vocab.cache_token_to_piece;
-
-        if (!cache.empty()) {
-            const auto & result = cache.at(token);
-            return _try_copy(result.data(), result.size());
-        }
-    }
-
-    if (0 <= token && token < llama_n_vocab(model)) {
-        const std::string & token_text = model->vocab.id_to_token[token].text;
-        switch (llama_vocab_get_type(model->vocab)) {
-            case LLAMA_VOCAB_TYPE_WPM:
-            case LLAMA_VOCAB_TYPE_SPM:
-            case LLAMA_VOCAB_TYPE_UGM: {
-                // NOTE: we accept all unsupported token types,
-                // suppressing them like CONTROL tokens.
-                if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
-                    return _try_copy(token_text.data(), token_text.size());
-                } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
-                    std::string result = token_text;
-                    llama_unescape_whitespace(result);
-                    return _try_copy(result.data(), result.size());
-                } else if (attr & LLAMA_TOKEN_ATTR_BYTE) {
-                    char byte = (char) llama_token_to_byte(model->vocab, token);
-                    return _try_copy((char*) &byte, 1);
-                }
-                break;
-            }
-            case LLAMA_VOCAB_TYPE_BPE: {
-                // NOTE: we accept all unsupported token types,
-                // suppressing them like CONTROL tokens.
-                if (attr & (attr_special | LLAMA_TOKEN_ATTR_USER_DEFINED)) {
-                    return _try_copy(token_text.data(), token_text.size());
-                } else if (attr & LLAMA_TOKEN_ATTR_NORMAL) {
-                    std::string result = llama_decode_text(token_text);
-                    return _try_copy(result.data(), result.size());
-                }
-                break;
-            }
-            default:
-                GGML_ASSERT(false);
-        }
-    }
-    return 0;
+int32_t llama_token_to_piece(
+    const struct llama_model * model,
+                 llama_token   token,
+                        char * buf,
+                     int32_t   length,
+                     int32_t   lstrip,
+                        bool   special) {
+    return llama_token_to_piece_impl(model->vocab, token, buf, length, lstrip, special);
 }
 
 int32_t llama_detokenize(
-        const struct llama_model * model,
-               const llama_token * tokens,
-                         int32_t   n_tokens,
-                            char * text,
-                         int32_t   text_len_max,
-                            bool   remove_special,
-                            bool   unparse_special) {
-    int32_t avail = text_len_max;
-    int32_t total = 0;
-
-    // remove the leading space
-    bool remove_space = model->vocab.tokenizer_add_space_prefix;
-
-    if (remove_special && model->vocab.tokenizer_add_bos) {
-        if (n_tokens > 0 && tokens[0] == model->vocab.special_bos_id) {
-            remove_space = false;
-            n_tokens--;
-            tokens++;
-        }
-    }
-
-    if (remove_special && model->vocab.tokenizer_add_eos) {
-        if (n_tokens > 0 && tokens[n_tokens-1] == model->vocab.special_eos_id) {
-            n_tokens--;
-        }
-    }
-
-    for (int32_t i = 0; i < n_tokens; ++i) {
-        GGML_ASSERT(avail >= 0);
-        int32_t n_chars = llama_token_to_piece(model, tokens[i], text, avail, remove_space, unparse_special);
-        remove_space = false;
-        if (n_chars < 0) {
-            avail = 0;
-            total -= n_chars;
-        } else if (n_chars > 0) {
-            avail -= n_chars;
-            text  += n_chars;
-            total += n_chars;
-        }
-    }
-
-    if (total > text_len_max) {
-        return -total;
-    }
-
-    if (model->vocab.tokenizer_clean_spaces) {
-        text -= total;  // restart text
-
-        // first pass: characters ?!.,  //TODO: where do these characters come from?
-        const int32_t total1 = total;
-        total = total ? 1 : 0;
-        for (int32_t i = 1; i < total1; ++i) {
-            const char x = text[i];
-            if (text[i - 1] == ' ') {
-                if (x == '?' || x == '!' || x == '.' || x == ',') {  // " ?", " !", " .", " ,"
-                    total--;  // remove space
-                }
-            }
-            text[total++] = x;
-        }
-
-        // second pass: strip single apostrophe between spaces
-        const int32_t total2 = total;
-        total = total ? 1 : 0;
-        for (int32_t i = 1; i < total2; ++i) {
-            const char x = text[i];
-            if (x == '\'' && i + 1 < total2 && text[i - 1] == ' ' && text[i + 1] == ' ') {  // " ' "
-                total--;           // remove prev space
-                text[++i] = '\0';  // remove next space
-            }
-            text[total++] = x;
-        }
-
-        // third pass: apostrophe contractions  //NOTE: this makes sense?
-        const int32_t total3 = total;
-        total = total ? 1 : 0;
-        for (int32_t i = 1; i < total3; ++i) {
-            const char x = text[i];
-            if (text[i - 1] == ' ') {
-                if (x == '\'' && i + 1 < total3) {
-                    const char x1 = text[i + 1];
-                    if (x1 == 't' || x1 == 'd') {  // " 't", " 'd"
-                        //total--;  // remove space
-                    } else if (x1 == 's' || x1 == 'm') {  // " 's", " 'm"
-                        total--;  // remove space
-                    } else if (i + 2 < total3) {
-                        const char x2 = text[i + 2];
-                        if ((x1 == 'l' && x2 == 'l')) {  // " 'll"
-                            //total--;  // remove space
-                        } else if ((x1 == 'r' && x2 == 'e') || (x1 == 'v' && x2 == 'e')) {  // " 're", " 've"
-                            total--;  // remove space
-                        } else {
-                            //total--;  // remove space
-                        }
-                    } else {
-                        //total--;  // remove space
-                    }
-                }
-            }
-            text[total++] = x;
-        }
-    }
-
-    return total <= text_len_max ? total : -total;
+    const struct llama_model * model,
+           const llama_token * tokens,
+                     int32_t   n_tokens,
+                        char * text,
+                     int32_t   text_len_max,
+                        bool   remove_special,
+                        bool   unparse_special) {
+    return llama_detokenize_impl(model->vocab, tokens, n_tokens, text, text_len_max, remove_special, unparse_special);
 }
 
-// trim whitespace from the beginning and end of a string
-static std::string trim(const std::string & str) {
-    size_t start = 0;
-    size_t end = str.size();
-    while (start < end && isspace(str[start])) {
-        start += 1;
-    }
-    while (end > start && isspace(str[end - 1])) {
-        end -= 1;
-    }
-    return str.substr(start, end - start);
-}
+//
+// chat templates
+//
 
 // Simple version of "llama_apply_chat_template" that only works with strings
 // This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
@@ -21667,7 +18877,7 @@ static int32_t llama_chat_apply_template_internal(
     return dest.size();
 }
 
-LLAMA_API int32_t llama_chat_apply_template(
+int32_t llama_chat_apply_template(
                 const struct llama_model * model,
                               const char * tmpl,
          const struct llama_chat_message * chat,
@@ -21708,7 +18918,126 @@ LLAMA_API int32_t llama_chat_apply_template(
     return res;
 }
 
-LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
+//
+// grammar
+//
+
+struct llama_grammar * llama_grammar_init(
+        const llama_grammar_element ** rules,
+        size_t    n_rules,
+        size_t    start_rule_index) {
+    return llama_grammar_init_impl(rules, n_rules, start_rule_index);
+}
+
+void llama_grammar_free(struct llama_grammar * grammar) {
+    llama_grammar_free_impl(grammar);
+}
+
+struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
+    return llama_grammar_copy_impl(grammar);
+}
+
+void llama_grammar_sample(
+      const struct llama_grammar * grammar,
+      const struct llama_context * ctx,
+          llama_token_data_array * candidates) {
+    llama_grammar_sample_impl(grammar, &ctx->model.vocab, &ctx->sampling, candidates);
+}
+
+void llama_sample_grammar(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+      const struct llama_grammar * grammar) {
+    llama_grammar_sample(grammar, ctx, candidates);
+}
+
+void llama_grammar_accept_token(
+            struct llama_grammar * grammar,
+            struct llama_context * ctx,
+                     llama_token   token) {
+    llama_grammar_accept_token_impl(grammar, &ctx->model.vocab, &ctx->sampling, token);
+}
+
+//
+// sampling
+//
+
+void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
+    llama_set_rng_seed_impl(&ctx->sampling, seed);
+}
+
+void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
+    llama_sample_softmax_impl(ctx ? &ctx->sampling : nullptr, candidates);
+}
+
+void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
+    llama_sample_top_k_impl(ctx ? &ctx->sampling : nullptr, candidates, k, min_keep);
+}
+
+void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+    llama_sample_top_p_impl(ctx ? &ctx->sampling : nullptr, candidates, p, min_keep);
+}
+
+void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+    llama_sample_min_p_impl(ctx ? &ctx->sampling : nullptr, candidates, p, min_keep);
+}
+
+void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
+    llama_sample_tail_free_impl(ctx ? &ctx->sampling : nullptr, candidates, z, min_keep);
+}
+
+void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+    llama_sample_typical_impl(ctx ? &ctx->sampling : nullptr, candidates, p, min_keep);
+}
+
+void llama_sample_entropy(struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val) {
+    llama_sample_entropy_impl(ctx ? &ctx->sampling : nullptr, candidates_p, min_temp, max_temp, exponent_val);
+}
+
+void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
+    llama_sample_temp_impl(ctx ? &ctx->sampling : nullptr, candidates_p, temp);
+}
+
+void llama_sample_repetition_penalties(
+            struct llama_context * ctx,
+          llama_token_data_array * candidates,
+               const llama_token * last_tokens,
+                          size_t   penalty_last_n,
+                           float   penalty_repeat,
+                           float   penalty_freq,
+                           float   penalty_present) {
+    llama_sample_repetition_penalties_impl(ctx ? &ctx->sampling : nullptr, candidates, last_tokens, penalty_last_n, penalty_repeat, penalty_freq, penalty_present);
+}
+
+void llama_sample_apply_guidance(
+          struct llama_context * ctx,
+                         float * logits,
+                         float * logits_guidance,
+                         float   scale) {
+    llama_sample_apply_guidance_impl(&ctx->sampling, logits, logits_guidance, scale);
+}
+
+llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
+    return llama_sample_token_mirostat_impl(&ctx->sampling, candidates, tau, eta, m, mu);
+}
+
+llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
+    return llama_sample_token_mirostat_v2_impl(ctx ? &ctx->sampling : nullptr, candidates, tau, eta, mu);
+}
+
+llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
+    return llama_sample_token_greedy_impl(ctx ? &ctx->sampling : nullptr, candidates);
+}
+
+llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng) {
+    return llama_sample_token_with_rng_impl(&ctx->sampling, candidates, rng);
+}
+
+llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
+    return llama_sample_token_with_rng_impl(&ctx->sampling, candidates, ctx->sampling.rng);
+}
+
+int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
     static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf";
     if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) {
         return strlen(split_path);
@@ -21737,11 +19066,11 @@ struct llama_timings llama_get_timings(struct llama_context * ctx) {
         /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
         /*.t_end_ms    =*/ 1.00 * ggml_time_ms(),
         /*.t_load_ms   =*/ 1e-3 * ctx->t_load_us,
-        /*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
+        /*.t_sample_ms =*/ 1e-3 * ctx->sampling.t_sample_us,
         /*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
         /*.t_eval_ms   =*/ 1e-3 * ctx->t_eval_us,
 
-        /*.n_sample =*/ std::max(1, ctx->n_sample),
+        /*.n_sample =*/ std::max(1, ctx->sampling.n_sample),
         /*.n_p_eval =*/ std::max(0, ctx->n_p_eval),
         /*.n_eval   =*/ std::max(1, ctx->n_eval),
     };
@@ -21764,10 +19093,11 @@ void llama_print_timings(struct llama_context * ctx) {
 }
 
 void llama_reset_timings(struct llama_context * ctx) {
-    ctx->t_start_us = ggml_time_us();
-    ctx->t_sample_us = ctx->n_sample = 0;
+    ctx->t_start_us  = ggml_time_us();
     ctx->t_eval_us   = ctx->n_eval   = 0;
     ctx->t_p_eval_us = ctx->n_p_eval = 0;
+
+    ctx->sampling.reset_timings();
 }
 
 const char * llama_print_system_info(void) {
@@ -21814,20 +19144,20 @@ void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
     fprintf(stream, "mst_p_eval: %.2f  # ms / token during prompt processing\n",
             1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
     fprintf(stream, "mst_sample: %.2f  # ms / token during sampling\n",
-            1.0e-3 * ctx->t_sample_us / ctx->n_sample);
+            1.0e-3 * ctx->sampling.t_sample_us / ctx->sampling.n_sample);
     fprintf(stream, "n_eval: %d  # number of tokens generated (excluding the first one)\n", ctx->n_eval);
     fprintf(stream, "n_p_eval: %d  # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
-    fprintf(stream, "n_sample: %d  # number of sampled tokens\n", ctx->n_sample);
+    fprintf(stream, "n_sample: %d  # number of sampled tokens\n", ctx->sampling.n_sample);
     fprintf(stream, "t_eval_us: %" PRId64 "  # total microseconds spent generating tokens\n", ctx->t_eval_us);
     fprintf(stream, "t_load_us: %" PRId64 "  # total microseconds spent loading the model\n", ctx->t_load_us);
     fprintf(stream, "t_p_eval_us: %" PRId64 "  # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
-    fprintf(stream, "t_sample_us: %" PRId64 "  # total microseconds spent sampling\n", ctx->t_sample_us);
+    fprintf(stream, "t_sample_us: %" PRId64 "  # total microseconds spent sampling\n", ctx->sampling.t_sample_us);
     fprintf(stream, "ts_eval: %.2f  # tokens / second during generation\n",
             1.0e6 * ctx->n_eval / ctx->t_eval_us);
     fprintf(stream, "ts_p_eval: %.2f  # tokens / second during prompt processing\n",
             1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
     fprintf(stream, "ts_sample: %.2f  # tokens / second during sampling\n",
-            1.0e6 * ctx->n_sample / ctx->t_sample_us);
+            1.0e6 * ctx->sampling.n_sample / ctx->sampling.t_sample_us);
 }
 
 // For internal test use
@@ -21866,14 +19196,14 @@ static void llama_log_internal_v(ggml_log_level level, const char * format, va_l
     va_end(args_copy);
 }
 
-static void llama_log_internal(ggml_log_level level, const char * format, ...) {
+void llama_log_internal(ggml_log_level level, const char * format, ...) {
     va_list args;
     va_start(args, format);
     llama_log_internal_v(level, format, args);
     va_end(args);
 }
 
-static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
+void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
     (void) level;
     (void) user_data;
     fputs(text, stderr);
diff --git a/src/unicode.cpp b/src/unicode.cpp
index e05fb9d1775ddd..46650bff06d15e 100644
--- a/src/unicode.cpp
+++ b/src/unicode.cpp
@@ -19,6 +19,12 @@
 #include <locale>
 #include <codecvt>
 
+size_t unicode_len_utf8(char src) {
+    const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
+    uint8_t highbits = static_cast<uint8_t>(src) >> 4;
+    return lookup[highbits];
+}
+
 static std::string unicode_cpts_to_utf8(const std::vector<uint32_t> & cps) {
     std::string result;
     for (size_t i = 0; i < cps.size(); ++i) {
diff --git a/src/unicode.h b/src/unicode.h
index 30b07ba7fa4935..008532a242ab8d 100644
--- a/src/unicode.h
+++ b/src/unicode.h
@@ -4,6 +4,8 @@
 #include <string>
 #include <vector>
 
+// TODO: prefix all symbols with "llama_"
+
 struct codepoint_flags {
     enum {
         UNDEFINED       = 0x0001,
@@ -46,6 +48,7 @@ struct codepoint_flags {
     }
 };
 
+size_t unicode_len_utf8(char src);
 
 std::string unicode_cpt_to_utf8(uint32_t cp);
 uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset);
diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp
index 975658f7953c94..68f971bfe2cf3d 100644
--- a/tests/test-grammar-integration.cpp
+++ b/tests/test-grammar-integration.cpp
@@ -44,21 +44,26 @@ static bool test_build_grammar_fails(const std::string & grammar_str) {
     return grammar_fails;
 }
 
-static bool match_string(const std::string & input, llama_grammar* grammar) {
+static bool match_string(const std::string & input, llama_grammar * grammar) {
     auto decoded = decode_utf8(input, {});
 
     const auto & code_points = decoded.first;
 
+    const llama_grammar_rules  & rules      = llama_grammar_get_rules (grammar);
+          llama_grammar_stacks & cur_stacks = llama_grammar_get_stacks(grammar);
+
     for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
-        auto prev_stacks = grammar->stacks;
-        llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks);
-        if (grammar->stacks.empty()) {
+        const llama_grammar_stacks prev_stacks = llama_grammar_get_stacks(grammar); // copy
+
+        llama_grammar_accept(rules, prev_stacks, *it, cur_stacks);
+
+        if (cur_stacks.empty()) {
             // no stacks means that the grammar failed to match at this point
             return false;
         }
     }
 
-    for (const auto & stack : grammar->stacks) {
+    for (const auto & stack : cur_stacks) {
         if (stack.empty()) {
             // An empty stack means that the grammar has been completed
             return true;
@@ -75,7 +80,9 @@ static void test(const std::string & test_desc, const std::string & grammar_str,
     auto grammar = build_grammar(grammar_str);
 
     // Save the original grammar stacks so that we can reset after every new string we want to test
-    auto original_stacks = grammar->stacks;
+    const llama_grammar_stacks original_stacks = llama_grammar_get_stacks(grammar);
+
+    llama_grammar_stacks & cur_stacks = llama_grammar_get_stacks(grammar);
 
     fprintf(stderr, "  🔵 Valid strings:\n");
 
@@ -112,7 +119,7 @@ static void test(const std::string & test_desc, const std::string & grammar_str,
         assert(matched);
 
         // Reset the grammar stacks
-        grammar->stacks = original_stacks;
+        cur_stacks = original_stacks;
     }
 
     fprintf(stderr, "  🟠 Invalid strings:\n");
@@ -132,7 +139,7 @@ static void test(const std::string & test_desc, const std::string & grammar_str,
         assert(!matched);
 
         // Reset the grammar stacks
-        grammar->stacks = original_stacks;
+        cur_stacks = original_stacks;
     }
 
     // Clean up allocated memory
diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp
index c8badb20630761..1f3a267b39f9ba 100644
--- a/tests/test-llama-grammar.cpp
+++ b/tests/test-llama-grammar.cpp
@@ -2,10 +2,12 @@
 #undef NDEBUG
 #endif
 
-#include "llama.cpp" // TODO: not great
+#define LLAMA_API_INTERNAL
+#include "llama.h"
 #include "grammar-parser.h"
 
 #include <cassert>
+#include <stdexcept>
 
 int main()
 {
@@ -112,10 +114,10 @@ int main()
         }
     }
 
-    llama_grammar *grammar = NULL;
+    llama_grammar * grammar = NULL;
     std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
-    grammar = llama_grammar_init(
-        grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
+
+    grammar = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
     if (grammar == nullptr)
     {
         throw std::runtime_error("Failed to initialize llama_grammar");
@@ -172,7 +174,7 @@ int main()
         }};
 
     auto index = 0;
-    for (auto stack : grammar->stacks)
+    for (auto stack : llama_grammar_get_stacks(grammar))
     {
         // compare stack to expected_stack
         for (uint32_t i = 0; i < stack.size(); i++)
@@ -374,13 +376,13 @@ int main()
         },
     };
 
-    std::vector<llama_grammar_candidate> rejects = llama_grammar_reject_candidates_for_stack(grammar->rules, grammar->stacks[0], next_candidates);
+    std::vector<llama_grammar_candidate> rejects = llama_grammar_reject_candidates_for_stack(llama_grammar_get_rules(grammar), llama_grammar_get_stacks(grammar)[0], next_candidates);
 
     std::vector<std::vector<llama_grammar_candidate>> all_rejects;
 
-    for (std::size_t count = 0; count < grammar->stacks.size(); ++count)
+    for (std::size_t count = 0; count < llama_grammar_get_stacks(grammar).size(); ++count)
     {
-        rejects = llama_grammar_reject_candidates_for_stack(grammar->rules, grammar->stacks[count], next_candidates);
+        rejects = llama_grammar_reject_candidates_for_stack(llama_grammar_get_rules(grammar), llama_grammar_get_stacks(grammar)[count], next_candidates);
         all_rejects.push_back(rejects);
     }
 
@@ -401,6 +403,6 @@ int main()
         delete[] candidate.code_points;
         candidate.code_points = nullptr;
     }
-    delete grammar;
+    llama_grammar_free(grammar);
     return 0;
 }

From 64cf50a0ed62d41e4f6c13e08a9b6b0816f46c6e Mon Sep 17 00:00:00 2001
From: Joe Todd <joe.todd@codeplay.com>
Date: Tue, 23 Jul 2024 14:58:37 +0100
Subject: [PATCH 106/125] sycl : Add support for non-release DPC++ & oneMKL
 (#8644)

* Update cmake to support nvidia hardware & open-source compiler
---------
Signed-off-by: Joe Todd <joe.todd@codeplay.com>
---
 ggml/src/CMakeLists.txt | 31 ++++++++++++++++---------------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 3f4c66bf70fc9a..12c440327e3bbb 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -467,15 +467,18 @@ if (GGML_SYCL)
         message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL or NVIDIA")
     endif()
 
-    if ( NOT DEFINED ENV{ONEAPI_ROOT})
-        message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh")
+    check_cxx_compiler_flag("-fsycl" SUPPORTS_SYCL)
+    if ( DEFINED ENV{ONEAPI_ROOT})
+        message(STATUS "Using oneAPI Release SYCL compiler (icpx).")
+    elseif(SUPPORTS_SYCL)
+        message(WARNING "Using open-source SYCL compiler (clang++). Didn't detect ENV {ONEAPI_ROOT}.
+         If you expected the oneAPI Release compiler, please install oneAPI & source it, like:
+         source /opt/intel/oneapi/setvars.sh")
+    else()
+        message(FATAL_ERROR, "C++ compiler lacks SYCL support.")
     endif()
-    #todo: AOT
-
-    find_package(IntelSYCL REQUIRED)
-    find_package(MKL REQUIRED)
-
     message(STATUS "SYCL found")
+    #todo: AOT
 
     list(APPEND GGML_CDEF_PUBLIC GGML_USE_SYCL)
 
@@ -487,11 +490,9 @@ if (GGML_SYCL)
         add_compile_definitions(GGML_SYCL_FORCE_MMQ)
     endif()
 
-    add_compile_options(-I./) #include DPCT
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl")
 
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
     if (GGML_SYCL_TARGET STREQUAL "NVIDIA")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
         add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
     else()
         add_compile_definitions(GGML_SYCL_WARP_SIZE=16)
@@ -504,15 +505,15 @@ if (GGML_SYCL)
     list(APPEND GGML_SOURCES_SYCL "ggml-sycl.cpp")
 
     if (WIN32)
+        find_package(IntelSYCL REQUIRED)
+        find_package(MKL REQUIRED)
         set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
     else()
-        add_compile_options(-I/${SYCL_INCLUDE_DIR})
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib")
-
         if (GGML_SYCL_TARGET STREQUAL "INTEL")
-            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
         elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA")
-            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl pthread m dl onemkl)
+            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
+            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} pthread m dl onemkl)
         endif()
     endif()
 endif()

From b841d0740855c5af1344a81f261139a45a2b39ee Mon Sep 17 00:00:00 2001
From: Vali Malinoiu <0x4139@gmail.com>
Date: Tue, 23 Jul 2024 17:37:42 +0300
Subject: [PATCH 107/125] server : fix URL.parse in the UI (#8646)

---
 examples/server/public/index-new.html | 2 +-
 examples/server/public/index.html     | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html
index bf2b0a3f0803c9..c87dd8f1e1d32b 100644
--- a/examples/server/public/index-new.html
+++ b/examples/server/public/index-new.html
@@ -225,7 +225,7 @@
         throw new Error("already running");
       }
       controller.value = new AbortController();
-      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: URL.parse('.', document.baseURI).href })) {
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: new URL('.', document.baseURI).href })) {
         const data = chunk.data;
         if (data.stop) {
           while (
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index a15424613499ff..48628a960de1c5 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -479,7 +479,7 @@
         throw new Error("already running");
       }
       controller.value = new AbortController();
-      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: URL.parse('.', document.baseURI).href })) {
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: new URL('.', document.baseURI).href })) {
         const data = chunk.data;
 
         if (data.stop) {

From de280085e7917dbb7f5753de5842ff4455f82a81 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Tue, 23 Jul 2024 23:48:37 +0200
Subject: [PATCH 108/125] examples : Fix `llama-export-lora` example (#8607)

* fix export-lora example

* add more logging

* reject merging subset

* better check

* typo
---
 Makefile                             |   2 +-
 common/common.cpp                    |  19 +-
 common/common.h                      |   3 +-
 examples/export-lora/README.md       |  15 +-
 examples/export-lora/export-lora.cpp | 721 ++++++++++++---------------
 5 files changed, 351 insertions(+), 409 deletions(-)

diff --git a/Makefile b/Makefile
index 52b55dd89da8bf..58a93db1aac301 100644
--- a/Makefile
+++ b/Makefile
@@ -1322,7 +1322,7 @@ llama-finetune: examples/finetune/finetune.cpp \
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 llama-export-lora: examples/export-lora/export-lora.cpp \
-	$(OBJ_GGML) common/log.h
+	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
diff --git a/common/common.cpp b/common/common.cpp
index dbb724fbbbcffb..4c19132f19832d 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -694,11 +694,6 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
         return true;
     }
-    if (arg == "--lora-base") {
-        CHECK_ARG
-        params.lora_base = argv[i];
-        return true;
-    }
     if (arg == "--control-vector") {
         CHECK_ARG
         params.control_vectors.push_back({ 1.0f, argv[i], });
@@ -1274,6 +1269,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         CHECK_ARG
         params.out_file = argv[i];
         params.cvector_outfile = argv[i];
+        params.lora_outfile = argv[i];
         return true;
     }
     if (arg == "-ofreq" || arg == "--output-frequency") {
@@ -1583,9 +1579,8 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
     options.push_back({ "*",           "       --override-kv KEY=TYPE:VALUE",
                                                                         "advanced option to override model metadata by key. may be specified multiple times.\n"
                                                                         "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false" });
-    options.push_back({ "*",           "       --lora FNAME",           "apply LoRA adapter (implies --no-mmap)" });
-    options.push_back({ "*",           "       --lora-scaled FNAME S",  "apply LoRA adapter with user defined scaling S (implies --no-mmap)" });
-    options.push_back({ "*",           "       --lora-base FNAME",      "optional model to use as a base for the layers modified by the LoRA adapter" });
+    options.push_back({ "*",           "       --lora FNAME",           "apply LoRA adapter (can be repeated to use multiple adapters)" });
+    options.push_back({ "*",           "       --lora-scaled FNAME S",  "apply LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
     options.push_back({ "*",           "       --control-vector FNAME", "add a control vector\n"
                                                                         "note: this argument can be repeated to add multiple control vectors" });
     options.push_back({ "*",           "       --control-vector-scaled FNAME SCALE",
@@ -1676,6 +1671,13 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
     options.push_back({ "cvector",     "       --pca-iter N",           "number of iterations used for PCA (default: %d)", params.n_pca_iterations });
     options.push_back({ "cvector",     "       --method {pca,mean}",    "dimensionality reduction method to be used (default: pca)" });
 
+    options.push_back({ "export-lora" });
+    options.push_back({ "export-lora", "-m,    --model",                "model path from which to load base model (default '%s')", params.model.c_str() });
+    options.push_back({ "export-lora", "       --lora FNAME",           "path to LoRA adapter  (can be repeated to use multiple adapters)" });
+    options.push_back({ "export-lora", "       --lora-scaled FNAME S",  "path to LoRA adapter with user defined scaling S  (can be repeated to use multiple adapters)" });
+    options.push_back({ "*",           "-t,    --threads N",            "number of threads to use during computation (default: %d)", params.n_threads });
+    options.push_back({ "export-lora", "-o,    --output FNAME",         "output file (default: '%s')", params.lora_outfile.c_str() });
+
     printf("usage: %s [options]\n", argv[0]);
 
     for (const auto & o : options) {
@@ -3166,7 +3168,6 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
         }
         fprintf(stream, "  - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la));
     }
-    fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
     fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
     fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
     fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
diff --git a/common/common.h b/common/common.h
index 184a53dc090646..8240ff99b8e2ae 100644
--- a/common/common.h
+++ b/common/common.h
@@ -128,7 +128,6 @@ struct gpt_params {
 
     // TODO: avoid tuple, use struct
     std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
-    std::string lora_base  = "";                              // base model path for the lora adapter
 
     std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
 
@@ -255,6 +254,8 @@ struct gpt_params {
     std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
 
     bool spm_infill = false; // suffix/prefix/middle pattern for infill
+
+    std::string lora_outfile = "ggml-lora-merged-f16.gguf";
 };
 
 void gpt_params_handle_hf_token(gpt_params & params);
diff --git a/examples/export-lora/README.md b/examples/export-lora/README.md
index 1fb17feeca6277..6d51f4b24dc6f4 100644
--- a/examples/export-lora/README.md
+++ b/examples/export-lora/README.md
@@ -6,12 +6,11 @@ Apply LORA adapters to base model and export the resulting model.
 usage: llama-export-lora [options]
 
 options:
-  -h, --help                         show this help message and exit
-  -m FNAME, --model-base FNAME       model path from which to load base model (default '')
-  -o FNAME, --model-out FNAME        path to save exported model (default '')
-  -l FNAME, --lora FNAME             apply LoRA adapter
-  -s FNAME S, --lora-scaled FNAME S  apply LoRA adapter with user defined scaling S
-  -t N, --threads N                  number of threads to use during computation (default: 4)
+  -m,    --model                  model path from which to load base model (default '')
+         --lora FNAME             path to LoRA adapter  (can be repeated to use multiple adapters)
+         --lora-scaled FNAME S    path to LoRA adapter with user defined scaling S  (can be repeated to use multiple adapters)
+  -t,    --threads N              number of threads to use during computation (default: 4)
+  -o,    --output FNAME           output file (default: 'ggml-lora-merged-f16.gguf')
 ```
 
 For example:
@@ -20,7 +19,7 @@ For example:
 ./bin/llama-export-lora \
     -m open-llama-3b-v2-q8_0.gguf \
     -o open-llama-3b-v2-q8_0-english2tokipona-chat.gguf \
-    -l lora-open-llama-3b-v2-q8_0-english2tokipona-chat-LATEST.bin
+    --lora lora-open-llama-3b-v2-q8_0-english2tokipona-chat-LATEST.bin
 ```
 
-Multiple LORA adapters can be applied by passing multiple `-l FN` or `-s FN S` command line parameters.
+Multiple LORA adapters can be applied by passing multiple `--lora FNAME` or `--lora-scaled FNAME S` command line parameters.
diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp
index 16f27aa77b500d..124ee167d2255f 100644
--- a/examples/export-lora/export-lora.cpp
+++ b/examples/export-lora/export-lora.cpp
@@ -1,465 +1,406 @@
-
 #include "common.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
 
+#include <map>
 #include <vector>
 #include <string>
 #include <thread>
+#include <fstream>
 
-struct lora_info {
-    std::string filename;
-    float scale;
-};
-
-struct export_lora_params {
-    std::string fn_model_base;
-    std::string fn_model_out;
-    std::vector<struct lora_info> lora;
-    int n_threads;
-};
+static bool g_verbose = false;
 
-struct lora_data {
-    struct lora_info     info;
-    std::vector<uint8_t> data;
-    struct ggml_context * ctx;
+static std::string get_kv_str(struct gguf_context * ctx_gguf, const std::string & key){
+    int id = gguf_find_key(ctx_gguf, key.c_str());
+    return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id));
+}
 
-    uint32_t lora_r;
-    uint32_t lora_alpha;
-};
+static float get_kv_f32(struct gguf_context * ctx_gguf, const std::string & key) {
+    int id = gguf_find_key(ctx_gguf, key.c_str());
+    return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf, id);
+}
 
-struct llama_file {
-    // use FILE * so we don't have to re-open the file to mmap
-    FILE * fp;
-    size_t size;
+static void zeros(std::ofstream & file, size_t n) {
+    char zero = 0;
+    for (size_t i = 0; i < n; ++i) {
+        file.write(&zero, 1);
+    }
+}
 
-    llama_file(const char * fname, const char * mode) {
-        fp = std::fopen(fname, mode);
-        if (fp == NULL) {
-            size = 0;
-        } else {
-            seek(0, SEEK_END);
-            size = tell();
-            seek(0, SEEK_SET);
+static std::string ggml_ne_string(const ggml_tensor * t) {
+    std::string str;
+    for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+        str += std::to_string(t->ne[i]);
+        if (i + 1 < GGML_MAX_DIMS) {
+            str += ", ";
         }
     }
+    return str;
+}
 
-    size_t tell() const {
-#ifdef _WIN32
-        __int64 ret = _ftelli64(fp);
-#else
-        long ret = std::ftell(fp);
-#endif
-        GGML_ASSERT(ret != -1); // this really shouldn't fail
-        return (size_t) ret;
+static struct gguf_context * load_gguf(std::string & fname, struct ggml_context ** ctx_ggml) {
+    struct gguf_init_params params = {
+        /*.no_alloc = */ true,
+        /*.ctx      = */ ctx_ggml,
+    };
+    struct gguf_context * ctx_gguf = gguf_init_from_file(fname.c_str(), params);
+    if (!ctx_gguf) {
+        throw std::runtime_error("failed to load input GGUF from " + fname);
     }
+    return ctx_gguf;
+}
 
-    void seek(size_t offset, int whence) {
-#ifdef _WIN32
-        int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
-        int ret = std::fseek(fp, (long) offset, whence);
-#endif
-        GGML_ASSERT(ret == 0); // same
+static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
+    std::string result;
+    for (size_t pos = 0; ; pos += search.length()) {
+        auto new_pos = s.find(search, pos);
+        if (new_pos == std::string::npos) {
+            result += s.substr(pos, s.size() - pos);
+            break;
+        }
+        result += s.substr(pos, new_pos - pos) + replace;
+        pos = new_pos;
     }
+    s = std::move(result);
+}
 
-    void read_raw(void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        std::size_t ret = std::fread(ptr, size, 1, fp);
-        if (ferror(fp)) {
-            die_fmt("read error: %s", strerror(errno));
-        }
-        if (ret != 1) {
-            die("unexpectedly reached end of file");
+struct file_input {
+    struct ggml_context * ctx_meta = nullptr;
+    struct gguf_context * ctx_gguf = nullptr;
+    std::ifstream f_in;
+    std::map<std::string, ggml_tensor *> tensors;
+    float alpha;
+    float scale;
+
+    file_input(std::string & fname, float scale): f_in(fname, std::ios::binary), scale(scale) {
+        if (!f_in.is_open()) {
+            throw std::runtime_error("failed to open input gguf from " + fname);
         }
-    }
 
-    std::uint32_t read_u32() {
-        std::uint32_t ret;
-        read_raw(&ret, sizeof(ret));
-        return ret;
+        ctx_gguf = load_gguf(fname, &ctx_meta);
+        alpha = get_kv_f32(ctx_gguf, "adapter.lora.alpha");
+        printf("%s: loaded gguf from %s\n", __func__, fname.c_str());
+
+        for (ggml_tensor * cur = ggml_get_first_tensor(ctx_meta); cur; cur = ggml_get_next_tensor(ctx_meta, cur)) {
+            std::string name(cur->name);
+            tensors[name] = cur;
+            if (g_verbose) {
+                printf("%s: %s\n", __func__, cur->name);
+            }
+        }
     }
 
-    std::string read_string(std::uint32_t len) {
-        std::vector<char> chars(len);
-        read_raw(chars.data(), len);
-        return std::string(chars.data(), len);
+    ggml_tensor * get_tensor(std::string name) {
+        if (tensors.find(name) == tensors.end()) {
+            return nullptr;
+        }
+        return tensors[name];
     }
 
-    void write_raw(const void * ptr, size_t size) {
-        if (size == 0) {
-            return;
+    void read_tensor_data(std::string name, std::vector<uint8_t> & buf) {
+        if (tensors.find(name) == tensors.end()) {
+            throw std::runtime_error("cannot find tensor with name: " + name);
         }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, size, 1, fp);
-        if (ret != 1) {
-            die_fmt("write error: %s", strerror(errno));
+        auto len = ggml_nbytes(tensors[name]);
+        if (buf.size() < len) {
+            buf.resize(len);
         }
+        auto i_tensor_in = gguf_find_tensor(ctx_gguf, name.c_str()); // idx of tensor in the input file
+        auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in);
+        f_in.seekg(offset);
+        f_in.read((char* )buf.data(), len);
     }
 
-    void write_u32(std::uint32_t val) {
-        write_raw(&val, sizeof(val));
+    ~file_input() {
+        gguf_free(ctx_gguf);
+        ggml_free(ctx_meta);
     }
+};
 
-    bool eof() {
-        return tell() >= size;
-    }
+struct lora_merge_ctx {
+    // input base model + adapters
+    file_input base_model;
+    std::vector<std::unique_ptr<file_input>> adapters;
 
-    ~llama_file() {
-        if (fp) {
-            std::fclose(fp);
+    // for computing merged tensor
+    int n_threads;
+    ggml_backend_t backend = nullptr;
+    ggml_gallocr_t allocr = nullptr;
+    std::vector<uint8_t> read_buf;
+
+    // output file
+    struct gguf_context * ctx_out;
+    struct ggml_context * ctx_out_ggml;
+    std::ofstream fout;
+
+    lora_merge_ctx(
+            std::string & base_fname,
+            std::vector<std::tuple<std::string, float>> & lora_files,
+            std::string & outfile,
+            int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) {
+        fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+
+        if (gguf_find_key(base_model.ctx_gguf, LLM_KV_SPLIT_COUNT) >= 0) {
+            throw std::runtime_error("split model is not yet supported");
         }
-    }
-};
 
-static struct export_lora_params get_default_export_lora_params() {
-    struct export_lora_params result;
-    result.fn_model_base = "";
-    result.fn_model_out  = "";
-    result.n_threads = GGML_DEFAULT_N_THREADS;
-    return result;
-}
+        for (auto lora_inp : lora_files) {
+            auto fname = std::get<0>(lora_inp);
+            auto scale = std::get<1>(lora_inp);
+            std::unique_ptr<file_input> adapter(new file_input(fname, scale));
+            check_metadata_lora(adapter.get());
+            adapters.push_back(std::move(adapter));
+        }
 
-static void export_lora_print_usage(int /*argc*/, char ** argv, const struct export_lora_params * params) {
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h, --help                         show this help message and exit\n");
-    fprintf(stderr, "  -m FNAME, --model-base FNAME       model path from which to load base model (default '%s')\n", params->fn_model_base.c_str());
-    fprintf(stderr, "  -o FNAME, --model-out FNAME        path to save exported model (default '%s')\n", params->fn_model_out.c_str());
-    fprintf(stderr, "  -l FNAME, --lora FNAME             apply LoRA adapter\n");
-    fprintf(stderr, "  -s FNAME S, --lora-scaled FNAME S  apply LoRA adapter with user defined scaling S\n");
-    fprintf(stderr, "  -t N, --threads N                  number of threads to use during computation (default: %d)\n", params->n_threads);
-}
+        ctx_out = gguf_init_empty();
+        struct ggml_init_params params = {
+            /*.mem_size   =*/ gguf_get_n_tensors(base_model.ctx_gguf)*ggml_tensor_overhead(),
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+        };
+        ctx_out_ggml = ggml_init(params);
+        backend = ggml_backend_cpu_init();
+        allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
+    }
 
-static bool export_lora_params_parse(int argc, char ** argv, struct export_lora_params * params) {
-    bool invalid_param = false;
-    std::string arg;
-    struct export_lora_params default_params = get_default_export_lora_params();
-    const std::string arg_prefix = "--";
+    void check_metadata_lora(file_input * adapter) {
+        auto general_type = get_kv_str(adapter->ctx_gguf, "general.type");
+        if (general_type != "adapter") {
+            throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
+        }
 
-    for (int i = 1; i < argc; i++) {
-        arg = argv[i];
-        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-            std::replace(arg.begin(), arg.end(), '_', '-');
+        auto adapter_type = get_kv_str(adapter->ctx_gguf, "adapter.type");
+        if (adapter_type != "lora") {
+            throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
         }
 
-        if (arg == "-m" || arg == "--model-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_model_base = argv[i];
-        } else if (arg == "-o" || arg == "--model-out") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_model_out = argv[i];
-        } else if (arg == "-l" || arg == "--lora") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            struct lora_info lora;
-            lora.filename = argv[i];
-            lora.scale = 1.0f;
-            params->lora.push_back(lora);
-        } else if (arg == "-s" || arg == "--lora-scaled") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            struct lora_info lora;
-            lora.filename = argv[i];
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            lora.scale = std::stof(argv[i]);
-            params->lora.push_back(lora);
-        } else if (arg == "-t" || arg == "--threads") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_threads = std::stoi(argv[i]);
-            if (params->n_threads <= 0) {
-                params->n_threads = std::thread::hardware_concurrency();
-            }
-        } else if (arg == "-h" || arg == "--help") {
-            export_lora_print_usage(argc, argv, &default_params);
-            exit(0);
-        } else {
-            fprintf(stderr, "error: unknown argument: '%s'\n", arg.c_str());
-            export_lora_print_usage(argc, argv, &default_params);
-            exit(1);
+        auto general_arch_base = get_kv_str(base_model.ctx_gguf, "general.architecture");
+        auto general_arch_lora = get_kv_str(adapter->ctx_gguf,   "general.architecture");
+        if (general_arch_base != general_arch_lora) {
+            throw std::runtime_error("model arch and LoRA arch mismatch");
         }
     }
 
-    if (params->fn_model_base == default_params.fn_model_base) {
-        fprintf(stderr, "error: please specify a filename for model-base.\n");
-        export_lora_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    if (params->fn_model_out == default_params.fn_model_out) {
-        fprintf(stderr, "error: please specify a filename for model-out.\n");
-        export_lora_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    if (invalid_param) {
-        fprintf(stderr, "error: invalid parameter for argument: '%s'\n", arg.c_str());
-        export_lora_print_usage(argc, argv, &default_params);
-        exit(1);
+    ggml_type get_out_tensor_type(struct ggml_tensor * t) {
+        if (t->type == GGML_TYPE_F32) {
+            return GGML_TYPE_F32;
+        } else {
+            return GGML_TYPE_F16;
+        }
     }
-    return true;
-}
 
-static void free_lora(struct lora_data * lora) {
-    if (lora->ctx != NULL) {
-        ggml_free(lora->ctx);
-    }
-    delete lora;
-}
+    void run_merge() {
+        // prepare metadata
+        gguf_set_kv(ctx_out, base_model.ctx_gguf);
+        // output is forced to f16 for now
+        gguf_set_val_u32(ctx_out, "general.file_type", LLAMA_FTYPE_MOSTLY_F16);
+
+        // check if all lora adapters have the same tensors
+        // TODO: remove this when we can support merging subset of adapters. Ref: https://github.com/ggerganov/llama.cpp/pull/8607#discussion_r1686027777
+        static const char * err_no_subset_adapter = "Input adapters do not have the same list of tensors. This is not yet supported. Please merge the adapter one-by-one instead of merging all at once.";
+        if (adapters.size() > 1) {
+            for (size_t i = 1; i < adapters.size(); ++i) {
+                if (adapters[0]->tensors.size() != adapters[i]->tensors.size()) {
+                    throw std::runtime_error(err_no_subset_adapter);
+                }
+                for (auto & it : adapters[i]->tensors) {
+                    if (adapters[0]->get_tensor(it.first) == nullptr) {
+                        throw std::runtime_error(err_no_subset_adapter);
+                    }
+                }
+            }
+        }
 
-static struct lora_data * load_lora(struct lora_info * info) {
-    struct lora_data * result = new struct lora_data;
-    result->info = *info;
-    result->ctx = NULL;
-    result->lora_r     = 1;
-    result->lora_alpha = 1;
-
-    struct llama_file file(info->filename.c_str(), "rb");
-    if (file.fp == NULL) {
-        fprintf(stderr, "warning: Could not open lora adapter '%s'. Ignoring this adapter.\n",
-            info->filename.c_str());
-        free_lora(result);
-        return NULL;
-    }
+        // if true, this tensor can be lora-merged. if false, we skip merging and just copy data to outfile
+        std::vector<std::pair<struct ggml_tensor *, bool>> base_tensors;
+        for (auto & it : base_model.tensors) {
+            bool t_a = true;
+            bool t_b = true;
+            for (auto & adapter : adapters) {
+                t_a &= nullptr != adapter->get_tensor(it.first + ".lora_a");
+                t_b &= nullptr != adapter->get_tensor(it.first + ".lora_b");
+            }
+            auto base_tensor = it.second;
+            struct ggml_tensor * out_tensor;
+            if (!t_a && !t_b) {
+                // only copy
+                out_tensor = ggml_dup_tensor(ctx_out_ggml, base_tensor);
+                ggml_set_name(out_tensor, base_tensor->name);
+                base_tensors.push_back(std::make_pair(out_tensor, false));
+            } else if (t_a && t_b) {
+                // need merging
+                out_tensor = ggml_dup_tensor(ctx_out_ggml, base_tensor);
+                out_tensor->type = get_out_tensor_type(base_tensor);
+                ggml_set_name(out_tensor, base_tensor->name);
+                base_tensors.push_back(std::make_pair(out_tensor, true));
+            } else {
+                throw std::runtime_error("tensor " + it.first + " missing either lora_a or lora_b");
+            }
+            gguf_add_tensor(ctx_out, out_tensor);
+        }
 
-    struct ggml_init_params params_ggml;
-    params_ggml.mem_size   = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE;
-    params_ggml.mem_buffer = NULL;
-    params_ggml.no_alloc   = true;
-    result->ctx = ggml_init(params_ggml);
+        // placeholder for the meta data
+        {
+            size_t meta_size = gguf_get_meta_size(ctx_out);
+            zeros(fout, meta_size);
+        }
 
-    uint32_t magic   = file.read_u32();
-    if (magic != LLAMA_FILE_MAGIC_GGLA) {
-        die_fmt("unexpected lora header file magic in '%s'", info->filename.c_str());
-    }
-    uint32_t version = file.read_u32();
-    if (version != 1) {
-        die_fmt("unexpected lora file version '%u' in '%s'", (unsigned) version, info->filename.c_str());
-    }
-    result->lora_r     = file.read_u32();
-    result->lora_alpha = file.read_u32();
-    // read tensor infos from file
-    std::vector<char> name_buf;
-    std::vector<struct ggml_tensor *> tensors;
-    std::vector<size_t> tensors_offset;
-    size_t total_nbytes_pad = 0;
-    while(!file.eof()) {
-        int64_t ne[4]   = {1,1,1,1};
-        uint32_t n_dims  = file.read_u32();
-        uint32_t namelen = file.read_u32();
-        uint32_t type    = file.read_u32();
-        for (uint32_t k = 0; k < n_dims; ++k) {
-            ne[k] = (int64_t)file.read_u32();
+        // process base model tensors
+        size_t n_merged = 0;
+        for (auto & it : base_tensors) {
+            if (it.second) {
+                merge_tensor(it.first);
+                n_merged++;
+            } else {
+                copy_tensor(it.first);
+            }
         }
-        name_buf.clear();
-        name_buf.resize(namelen + 1, '\0');
-        file.read_raw(name_buf.data(), namelen);
-        file.seek((0-file.tell()) & 31, SEEK_CUR);
-        size_t offset = file.tell();
-        struct ggml_tensor * tensor = ggml_new_tensor(result->ctx, (enum ggml_type) type, n_dims, ne);
-        ggml_set_name(tensor, name_buf.data());
-        size_t nbytes     = ggml_nbytes(tensor);
-        size_t nbytes_pad = ggml_nbytes_pad(tensor);
-        total_nbytes_pad += nbytes_pad;
-        tensors.push_back(tensor);
-        tensors_offset.push_back(offset);
-        file.seek(nbytes, SEEK_CUR);
-    }
-    // read tensor data
-    result->data.resize(total_nbytes_pad);
-    size_t data_offset = 0;
-    for (size_t i = 0; i < tensors.size(); ++i) {
-        struct ggml_tensor * tensor = tensors[i];
-        size_t offset     = tensors_offset[i];
-        size_t nbytes     = ggml_nbytes(tensor);
-        size_t nbytes_pad = ggml_nbytes_pad(tensor);
-        file.seek(offset, SEEK_SET);
-        tensor->data = result->data.data() + data_offset;
-        file.read_raw(tensor->data, nbytes);
-        data_offset += nbytes_pad;
-    }
-    return result;
-}
 
+        // write output metadata
+        {
+            std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
+            gguf_get_meta_data(ctx_out, data.data());
+            fout.seekp(0);
+            fout.write((const char *)data.data(), data.size());
+        }
 
-static struct ggml_cgraph * build_graph_lora(
-    struct ggml_context * ctx,
-    struct ggml_tensor * tensor,
-    struct ggml_tensor * lora_a,
-    struct ggml_tensor * lora_b,
-    float scaling
-) {
-    struct ggml_tensor * ab = ggml_mul_mat(ctx, lora_a, lora_b);
-    if (scaling != 1.0f) {
-        ab = ggml_scale(ctx, ab, scaling);
+        printf("%s : merged %ld tensors with lora adapters\n", __func__, n_merged);
+        printf("%s : wrote %ld tensors to output file\n", __func__, base_tensors.size());
     }
-    struct ggml_tensor * res = ggml_add_inplace(ctx, tensor, ab);
 
-    struct ggml_cgraph * gf = ggml_new_graph(ctx);
-    ggml_build_forward_expand (gf, res);
-    return gf;
-}
-
-static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int n_threads) {
-    if (lora->ctx == NULL) {
-        return false;
-    }
-    std::string name = ggml_get_name(tensor);
-    std::string name_a = name + std::string(".loraA");
-    std::string name_b = name + std::string(".loraB");
-    struct ggml_tensor * lora_a = ggml_get_tensor(lora->ctx, name_a.c_str());
-    struct ggml_tensor * lora_b = ggml_get_tensor(lora->ctx, name_b.c_str());
-    if (lora_a == NULL || lora_b == NULL) {
-        return false;
+    void copy_tensor(struct ggml_tensor * base) {
+        printf("%s :  %s [%s]\n", __func__, base->name, ggml_ne_string(base).c_str());
+        size_t len = ggml_nbytes(base);
+        base_model.read_tensor_data(base->name, read_buf);
+        fout.write((char* )read_buf.data(), len);
+        zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len);
     }
 
-    float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r;
-
-    struct ggml_init_params params;
-    params.mem_size   = GGML_OBJECT_SIZE + ggml_graph_overhead() + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5;
-    params.mem_buffer = NULL;
-    params.no_alloc   = true;
-    struct ggml_context * ctx = NULL;
-    struct ggml_gallocr * alloc = NULL;
-    struct ggml_cgraph  * gf = NULL;
-
-    ctx   = ggml_init(params);
-    alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-    gf    = build_graph_lora(ctx, tensor, lora_a, lora_b, scaling);
-
-    ggml_gallocr_alloc_graph(alloc, gf);
-
-    struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads);
-    static std::vector<uint8_t> data_work;
-    data_work.resize(cplan.work_size);
-    cplan.work_data = data_work.data();
-
-    ggml_graph_compute(gf, &cplan);
+    void merge_tensor(struct ggml_tensor * base) {
+        std::string name_base(base->name);
+        std::string name_lora_a = name_base + ".lora_a";
+        std::string name_lora_b = name_base + ".lora_b";
+
+        printf("%s : %s [%s]\n", __func__, base->name, ggml_ne_string(base).c_str());
+
+        // context for input tensor
+        std::vector<struct ggml_tensor *> inp_a(adapters.size());
+        std::vector<struct ggml_tensor *> inp_b(adapters.size());
+        struct ggml_init_params params {
+            /*.mem_size   =*/ ggml_tensor_overhead()*(1+adapters.size()*2),
+            /*.mem_buffer =*/ NULL,
+            /*.no_alloc   =*/ true,
+        };
+        struct ggml_context * ctx = ggml_init(params);
+
+        // alloc tensors
+        struct ggml_tensor * inp = ggml_dup_tensor(ctx, base);
+        for (size_t i = 0; i < adapters.size(); ++i) {
+            auto t_a = adapters[i]->get_tensor(name_lora_a);
+            auto t_b = adapters[i]->get_tensor(name_lora_b);
+            inp_a[i] = ggml_dup_tensor(ctx, t_a);
+            inp_b[i] = ggml_dup_tensor(ctx, t_b);
+        }
+        ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
+
+        // load data to backend buffer
+        base_model.read_tensor_data(name_base, read_buf);
+        ggml_backend_tensor_set(inp, read_buf.data(), 0, ggml_nbytes(inp));
+        for (size_t i = 0; i < adapters.size(); ++i) {
+            adapters[i]->read_tensor_data(name_lora_a, read_buf);
+            ggml_backend_tensor_set(inp_a[i], read_buf.data(), 0, ggml_nbytes(inp_a[i]));
+            adapters[i]->read_tensor_data(name_lora_b, read_buf);
+            ggml_backend_tensor_set(inp_b[i], read_buf.data(), 0, ggml_nbytes(inp_b[i]));
+        }
 
-    ggml_gallocr_free(alloc);
-    ggml_free(ctx);
-    return true;
-}
+        // build graph
+        struct ggml_cgraph * gf;
+        {
+            static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
+            static std::vector<uint8_t> buf(buf_size);
+            struct ggml_init_params params0 = {
+                /*.mem_size   =*/ buf_size,
+                /*.mem_buffer =*/ buf.data(),
+                /*.no_alloc   =*/ true,
+            };
+            struct ggml_context * ctx0 = ggml_init(params0);
+            gf = ggml_new_graph(ctx0);
+            struct ggml_tensor * cur = inp;
+            for (size_t i = 0; i < adapters.size(); ++i) {
+                struct ggml_tensor * a_T = ggml_cont(ctx0, ggml_transpose(ctx0, inp_a[i]));
+                struct ggml_tensor * delta = ggml_mul_mat(ctx0, a_T, inp_b[i]);
+                // scale
+                const float alpha = adapters[i]->alpha;
+                const float rank  = (float) inp_b[i]->ne[0];
+                const float scale = alpha ? adapters[i]->scale * alpha / rank : adapters[i]->scale;
+                delta = ggml_scale(ctx0, delta, scale);
+                cur = ggml_add(ctx0, cur, delta);
+                printf("%s :   + merging from adapter[%ld]\n", __func__, i);
+                printf("%s :     input_scale=%f calculated_scale=%f rank=%d\n", __func__, adapters[i]->scale, scale, (int) inp_b[i]->ne[0]);
+            }
+            cur = ggml_cast(ctx0, cur, get_out_tensor_type(base));
+            ggml_build_forward_expand(gf, cur);
+            ggml_free(ctx0);
+        }
 
-static void export_lora(struct export_lora_params * params) {
-    // load all loras
-    std::vector<struct lora_data *> loras;
-    for (size_t i = 0; i < params->lora.size(); ++i) {
-        struct lora_data * lora = load_lora(&params->lora[i]);
-        if (lora != NULL) {
-            loras.push_back(lora);
+        // compute
+        {
+            ggml_gallocr_alloc_graph(allocr, gf);
+            ggml_backend_cpu_set_n_threads(backend, n_threads);
+            ggml_backend_graph_compute(backend, gf);
         }
-    }
-    if (loras.size() == 0) {
-        fprintf(stderr, "warning: no lora adapters will be applied.\n");
-    }
 
-    // open input file
-    struct llama_file fin(params->fn_model_base.c_str(), "rb");
-    if (!fin.fp) {
-        die_fmt("Could not open file '%s'\n", params->fn_model_base.c_str());
-    }
+        // write data to output file
+        {
+            auto result = gf->nodes[gf->n_nodes - 1];
+            size_t len = ggml_nbytes(result);
+            if (read_buf.size() < len) {
+                read_buf.resize(len);
+            }
+            ggml_backend_tensor_get(result, read_buf.data(), 0, len);
+            fout.write((char* )read_buf.data(), len);
+            zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len);
+        }
 
-    // open base model gguf, read tensors without their data
-    struct ggml_context * ctx_in;
-    struct gguf_init_params params_gguf;
-    params_gguf.no_alloc = true;
-    params_gguf.ctx      = &ctx_in;
-    struct gguf_context * gguf_in = gguf_init_from_file(params->fn_model_base.c_str(), params_gguf);
-
-    // create new gguf
-    struct gguf_context * gguf_out = gguf_init_empty();
-
-    // copy meta data from base model: kv and tensors
-    gguf_set_kv(gguf_out, gguf_in);
-    int n_tensors = gguf_get_n_tensors(gguf_in);
-    for (int i=0; i < n_tensors; ++i) {
-        const char * name = gguf_get_tensor_name(gguf_in, i);
-        struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name);
-        gguf_add_tensor(gguf_out, tensor);
+        ggml_free(ctx);
+        ggml_backend_buffer_free(buffer);
     }
 
-    // create output file
-    struct llama_file fout(params->fn_model_out.c_str(), "wb");
-    if (!fout.fp) {
-        die_fmt("Could not create file '%s'\n", params->fn_model_out.c_str());
+    ~lora_merge_ctx() {
+        ggml_gallocr_free(allocr);
+        ggml_backend_free(backend);
+        gguf_free(ctx_out);
+        ggml_free(ctx_out_ggml);
     }
+};
 
-    // write gguf meta data
-    std::vector<uint8_t> meta;
-    meta.resize(gguf_get_meta_size(gguf_out));
-    gguf_get_meta_data(gguf_out, meta.data());
-    fout.write_raw(meta.data(), meta.size());
-
-    std::vector<uint8_t> data;
-    std::vector<uint8_t> padding;
-    for (int i=0; i < n_tensors; ++i) {
-        const char * name = gguf_get_tensor_name(gguf_in, i);
-        struct ggml_tensor * tensor = ggml_get_tensor(ctx_in, name);
-
-        // read tensor data
-        data.resize(ggml_nbytes(tensor));
-        tensor->data = data.data();
-        size_t offset = gguf_get_tensor_offset(gguf_in, i);
-        fin.seek(offset + meta.size(), SEEK_SET);
-        fin.read_raw(data.data(), data.size());
-
-        // apply all loras
-        for (size_t k = 0; k < loras.size(); ++k) {
-            apply_lora(tensor, loras[k], params->n_threads);
-        }
-
-        // write tensor data + padding
-        padding.clear();
-        padding.resize(GGML_PAD(data.size(), gguf_get_alignment(gguf_out)) - data.size(), 0);
-
-        GGML_ASSERT(fout.tell() == offset + meta.size());
-        // fout.seek(offset + meta.size(), SEEK_SET);
-        fout.write_raw(data.data(), data.size());
-        fout.write_raw(padding.data(), padding.size());
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
 
-        if (i % 2 == 0) {
-            printf(".");
-        }
-    }
+    printf("\nexample usage:\n");
+    printf("\n  %s -m base-model.gguf --lora lora-file.gguf -o merged-model-f16.gguf\n", argv[0]);
+    printf("\nNOTE: output model is F16\n");
     printf("\n");
-
-    // close gguf
-    gguf_free(gguf_out);
-    gguf_free(gguf_in);
-
-    // free loras
-    for (size_t i = 0; i < loras.size(); ++i) {
-        free_lora(loras[i]);
-    }
 }
 
 int main(int argc, char ** argv) {
-    struct export_lora_params params = get_default_export_lora_params();
+    gpt_params params;
 
-    if (!export_lora_params_parse(argc, argv, &params)) {
+    if (!gpt_params_parse(argc, argv, params)) {
+        print_usage(argc, argv, params);
         return 1;
     }
 
-    export_lora(&params);
+    g_verbose = (params.verbosity == 1);
+    try {
+        lora_merge_ctx ctx(params.model, params.lora_adapter, params.lora_outfile, params.n_threads);
+        ctx.run_merge();
+    } catch (const std::exception & err) {
+        fprintf(stderr, "%s\n", err.what());
+        exit(EXIT_FAILURE);
+    }
+
+    printf("done, output file is %s\n", params.lora_outfile.c_str());
 
     return 0;
 }

From b115105f05e3372bc75b2a486c1930c365fd2846 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Wed, 24 Jul 2024 11:25:19 +0200
Subject: [PATCH 109/125] add llama_lora_adapter_clear (#8653)

---
 include/llama.h | 6 +++++-
 src/llama.cpp   | 4 ++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/include/llama.h b/include/llama.h
index e68cd807e63bde..413070d95a5c4e 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -529,12 +529,16 @@ extern "C" {
             struct llama_lora_adapter * adapter,
             float scale);
 
-    // Remove a LoRA adapter from given context
+    // Remove a specific LoRA adapter from given context
     // Return -1 if the adapter is not present in the context
     LLAMA_API int32_t llama_lora_adapter_remove(
             struct llama_context * ctx,
             struct llama_lora_adapter * adapter);
 
+    // Remove all LoRA adapters from given context
+    LLAMA_API void llama_lora_adapter_clear(
+            struct llama_context * ctx);
+
     // Manually free a LoRA adapter
     // Note: loaded adapters will be free when the associated model is deleted
     LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter);
diff --git a/src/llama.cpp b/src/llama.cpp
index 40c5e8e8d4b3be..04eaf6730bc24c 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -16201,6 +16201,10 @@ int32_t llama_lora_adapter_remove(
     return -1;
 }
 
+void llama_lora_adapter_clear(struct llama_context * ctx) {
+    ctx->lora_adapters.clear();
+}
+
 void llama_lora_adapter_free(struct llama_lora_adapter * adapter) {
     delete adapter;
 }

From 79167d9e49aef9caa98e13ee7ca067ec9f88b4b5 Mon Sep 17 00:00:00 2001
From: Joe Todd <joe.todd@codeplay.com>
Date: Wed, 24 Jul 2024 11:55:26 +0100
Subject: [PATCH 110/125] Re-add erroneously removed -fsycl from
 GGML_EXTRA_LIBS (#8667)

---
 ggml/src/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 12c440327e3bbb..c6496c9211d70a 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -510,10 +510,10 @@ if (GGML_SYCL)
         set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} IntelSYCL::SYCL_CXX MKL::MKL MKL::MKL_SYCL)
     else()
         if (GGML_SYCL_TARGET STREQUAL "INTEL")
-            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
         elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA")
             set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
-            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} pthread m dl onemkl)
+            set(GGML_EXTRA_LIBS ${GGML_EXTRA_LIBS} -fsycl pthread m dl onemkl)
         endif()
     endif()
 endif()

From 96952e7181929c6001b2bc69a33f240de731cc3a Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Wed, 24 Jul 2024 13:48:46 +0200
Subject: [PATCH 111/125] llama : fix `llama_chat_format_single` for mistral
 (#8657)

* fix `llama_chat_format_single` for mistral

* fix typo

* use printf
---
 common/common.cpp            |  2 +-
 examples/main/main.cpp       |  1 +
 tests/test-chat-template.cpp | 30 ++++++++++++++++++++++++------
 3 files changed, 26 insertions(+), 7 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index 4c19132f19832d..ec44a05521c9d7 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -2723,7 +2723,7 @@ std::string llama_chat_format_single(const struct llama_model * model,
         const llama_chat_msg & new_msg,
         bool add_ass) {
     std::ostringstream ss;
-    auto fmt_past_msg = llama_chat_apply_template(model, tmpl, past_msg, false);
+    auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false);
     std::vector<llama_chat_msg> chat_new(past_msg);
     // if the past_msg ends with a newline, we must preserve it in the formatted version
     if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index a0d817b1a89d14..61e960ea2abe6a 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -124,6 +124,7 @@ static std::string chat_add_and_format(struct llama_model * model, std::vector<l
     auto formatted = llama_chat_format_single(
         model, g_params->chat_template, chat_msgs, new_msg, role == "user");
     chat_msgs.push_back({role, content});
+    LOG("formatted: %s\n", formatted.c_str());
     return formatted;
 }
 
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 6583dd0b2b39b6..46a7d3aea8f67a 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -1,4 +1,3 @@
-#include <iostream>
 #include <string>
 #include <vector>
 #include <sstream>
@@ -133,13 +132,31 @@ int main(void) {
         );
         formatted_chat.resize(res);
         std::string output(formatted_chat.data(), formatted_chat.size());
-        std::cout << output << "\n-------------------------\n";
+        printf("%s\n", output.c_str());
+        printf("-------------------------\n");
         assert(output == expected);
     }
 
-    // test llama_chat_format_single
-    std::cout << "\n\n=== llama_chat_format_single ===\n\n";
+
+    // test llama_chat_format_single for system message
+    printf("\n\n=== llama_chat_format_single (system message) ===\n\n");
     std::vector<llama_chat_msg> chat2;
+    llama_chat_msg sys_msg{"system", "You are a helpful assistant"};
+
+    auto fmt_sys = [&](std::string tmpl) {
+        auto output = llama_chat_format_single(nullptr, tmpl, chat2, sys_msg, false);
+        printf("fmt_sys(%s) : %s\n", tmpl.c_str(), output.c_str());
+        printf("-------------------------\n", output.c_str());
+        return output;
+    };
+    assert(fmt_sys("chatml") == "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n");
+    assert(fmt_sys("llama2") == "[INST] You are a helpful assistant\n");
+    assert(fmt_sys("gemma")  == ""); // for gemma, system message is merged with user message
+    assert(fmt_sys("llama3") == "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|>");
+
+
+    // test llama_chat_format_single for user message
+    printf("\n\n=== llama_chat_format_single (user message) ===\n\n");
     chat2.push_back({"system", "You are a helpful assistant"});
     chat2.push_back({"user", "Hello"});
     chat2.push_back({"assistant", "I am assistant"});
@@ -147,12 +164,13 @@ int main(void) {
 
     auto fmt_single = [&](std::string tmpl) {
         auto output = llama_chat_format_single(nullptr, tmpl, chat2, new_msg, true);
-        std::cout << "fmt_single(" << tmpl << ")\n" << output << "\n-------------------------\n";
+        printf("fmt_single(%s) : %s\n", tmpl.c_str(), output.c_str());
+        printf("-------------------------\n", output.c_str());
         return output;
     };
     assert(fmt_single("chatml") == "\n<|im_start|>user\nHow are you<|im_end|>\n<|im_start|>assistant\n");
     assert(fmt_single("llama2") == "[INST] How are you [/INST]");
-    assert(fmt_single("gemma") == "\n<start_of_turn>user\nHow are you<end_of_turn>\n<start_of_turn>model\n");
+    assert(fmt_single("gemma")  == "\n<start_of_turn>user\nHow are you<end_of_turn>\n<start_of_turn>model\n");
     assert(fmt_single("llama3") == "<|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n");
 
     return 0;

From 3a7ac5300a7e8ebbe4a3eb5aff9dba11ed76ea61 Mon Sep 17 00:00:00 2001
From: Thorsten Sommer <SommerEngineering@users.noreply.github.com>
Date: Wed, 24 Jul 2024 14:52:30 +0200
Subject: [PATCH 112/125] readme : update UI list [no ci] (#8505)

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 7c233b5e11165b..b7b9bf588b1cc9 100644
--- a/README.md
+++ b/README.md
@@ -138,6 +138,7 @@ Typically finetunes of the base models below are supported as well.
 
 Unless otherwise noted these projects are open-source with permissive licensing:
 
+- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT)
 - [iohub/collama](https://github.com/iohub/coLLaMA)
 - [janhq/jan](https://github.com/janhq/jan) (AGPL)
 - [nat/openplayground](https://github.com/nat/openplayground)

From f19bf99c015d3d745143e8bb4f056e0ea015ad40 Mon Sep 17 00:00:00 2001
From: Joe Todd <joe.todd@codeplay.com>
Date: Wed, 24 Jul 2024 14:36:00 +0100
Subject: [PATCH 113/125] Build Llama SYCL Intel with static libs (#8668)

Ensure SYCL CI builds both static & dynamic libs for testing purposes

Signed-off-by: Joe Todd <joe.todd@codeplay.com>
---
 .devops/llama-cli-intel.Dockerfile    | 4 +++-
 .devops/llama-server-intel.Dockerfile | 1 +
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/.devops/llama-cli-intel.Dockerfile b/.devops/llama-cli-intel.Dockerfile
index 2bf82bb586e6d6..79dba06a77d6eb 100644
--- a/.devops/llama-cli-intel.Dockerfile
+++ b/.devops/llama-cli-intel.Dockerfile
@@ -14,7 +14,9 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
         echo "GGML_SYCL_F16 is set" && \
         export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
     fi && \
-    cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
+    echo "Building with static libs" && \
+    cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \
+    ${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \
     cmake --build build --config Release --target llama-cli
 
 FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
diff --git a/.devops/llama-server-intel.Dockerfile b/.devops/llama-server-intel.Dockerfile
index eb9aba618d4d88..f525658dddfe5d 100644
--- a/.devops/llama-server-intel.Dockerfile
+++ b/.devops/llama-server-intel.Dockerfile
@@ -14,6 +14,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
         echo "GGML_SYCL_F16 is set" && \
         export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
     fi && \
+    echo "Building with dynamic libs" && \
     cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
     cmake --build build --config Release --target llama-server
 

From 68504f0970db5a3602d176953690f503059906b1 Mon Sep 17 00:00:00 2001
From: MorganRO8 <47795945+MorganRO8@users.noreply.github.com>
Date: Wed, 24 Jul 2024 12:48:00 -0400
Subject: [PATCH 114/125] readme : update games list (#8673)

Added link to game I made that depends on llama
---
 README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/README.md b/README.md
index b7b9bf588b1cc9..d0ae2efb9123e1 100644
--- a/README.md
+++ b/README.md
@@ -182,6 +182,9 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 
 - [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
 
+**Games:**
+- [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you.
+
 ## Demo
 
 <details>

From 8a4bad50a8ed24ed1e9df003521468dcc37320e8 Mon Sep 17 00:00:00 2001
From: Fan Shupei <dymarkfan@outlook.com>
Date: Thu, 25 Jul 2024 15:21:09 +0800
Subject: [PATCH 115/125] llama: use sliding window for phi3 (#8627)

* use sliding window for phi3

* fix typo, "data_swa" -> "data"

* [conver_hf_to_gguf.py] add phi3 sliding window
---
 convert_hf_to_gguf.py |  1 +
 src/llama.cpp         | 37 ++++++++++++++++++++++++++++---------
 2 files changed, 29 insertions(+), 9 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index dde4fa9c80ca3a..4087187c19834a 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -2084,6 +2084,7 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_rope_dimension_count(rope_dims)
         self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"]))
         self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_sliding_window(self.find_hparam(["sliding_window"]))
 
         # write rope scaling for long context (128k) model
         rope_scaling = self.find_hparam(['rope_scaling'], True)
diff --git a/src/llama.cpp b/src/llama.cpp
index 04eaf6730bc24c..9e502018dfb764 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -4889,6 +4889,7 @@ static void llm_load_hparams(
             } break;
         case LLM_ARCH_PHI3:
             {
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
@@ -10748,7 +10749,7 @@ struct llm_build_context {
         struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+        struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa();
 
         for (int il = 0; il < n_layer; ++il) {
             auto residual = inpL;
@@ -10806,7 +10807,7 @@ struct llm_build_context {
 
                 cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                         model.layers[il].wo, model.layers[il].bo,
-                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+                        Kcur, Vcur, Qcur, KQ_mask_swa, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
 
             if (il == n_layer - 1) {
@@ -14013,18 +14014,23 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
         "causal attention is not supported by this model"
     );
 
-    if (lctx.inp_KQ_mask) {
+    if (lctx.inp_KQ_mask || lctx.inp_KQ_mask_swa) {
         // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
         if (cparams.causal_attn && !lctx.is_encoding) {
             const int64_t n_kv     = kv_self.n;
             const int64_t n_tokens = batch.n_tokens;
 
-            GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
 
-            float * data     = (float *) lctx.inp_KQ_mask->data;
+            float * data     = nullptr;
             float * data_swa = nullptr;
 
+            if (lctx.inp_KQ_mask) {
+                GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+                data = (float *) lctx.inp_KQ_mask->data;
+            }
+
             if (lctx.inp_KQ_mask_swa) {
+                GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_swa->buffer));
                 data_swa = (float *) lctx.inp_KQ_mask_swa->data;
             }
 
@@ -14047,7 +14053,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
                                 f = 0.0f;
                             }
                         }
-                        data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
+
+                        if (data) {
+                            data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
+                        }
 
                         // may need to cut off old tokens for sliding window
                         if (data_swa) {
@@ -14059,9 +14068,19 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
                     }
                 }
 
-                for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
-                    for (int j = 0; j < n_kv; ++j) {
-                        data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+                if (data) {
+                    for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                        for (int j = 0; j < n_kv; ++j) {
+                            data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+                        }
+                    }
+                }
+
+                if (data_swa) {
+                    for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                        for (int j = 0; j < n_kv; ++j) {
+                            data_swa[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+                        }
                     }
                 }
             }

From 4b0eff3df58d8d86e47348fb73d54da3194d416d Mon Sep 17 00:00:00 2001
From: Ujjawal Panchal <31011628+Ujjawal-K-Panchal@users.noreply.github.com>
Date: Thu, 25 Jul 2024 13:43:27 +0530
Subject: [PATCH 116/125] docs : Quantum -> Quantized (#8666)

* docfix: imatrix readme, quantum models -> quantized models.

* docfix: server readme: quantum models -> quantized models.
---
 examples/imatrix/README.md | 2 +-
 examples/server/README.md  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/imatrix/README.md b/examples/imatrix/README.md
index 29602881a0d210..bb5faec94c20a6 100644
--- a/examples/imatrix/README.md
+++ b/examples/imatrix/README.md
@@ -1,6 +1,6 @@
 # llama.cpp/examples/imatrix
 
-Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantum models.
+Compute an importance matrix for a model and given text dataset. Can be used during quantization to enchance the quality of the quantized models.
 More information is available here: https://github.com/ggerganov/llama.cpp/pull/4861
 
 ## Usage
diff --git a/examples/server/README.md b/examples/server/README.md
index ff4074517f9f55..33a2b95cc0edb5 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -5,7 +5,7 @@ Fast, lightweight, pure C/C++ HTTP server based on [httplib](https://github.com/
 Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
 
 **Features:**
- * LLM inference of F16 and quantum models on GPU and CPU
+ * LLM inference of F16 and quantized models on GPU and CPU
  * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes
  * Parallel decoding with multi-user support
  * Continuous batching

From be6d7c079173d941b4f784500f9148f46cec2724 Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Thu, 25 Jul 2024 10:39:04 +0200
Subject: [PATCH 117/125] examples : remove `finetune` and
 `train-text-from-scratch` (#8669)

* examples : remove finetune and train-text-from-scratch

* fix build

* update help message

* fix small typo for export-lora
---
 .devops/nix/apps.nix                          |    1 -
 .devops/tools.sh                              |    4 -
 Makefile                                      |   30 +-
 examples/CMakeLists.txt                       |    2 -
 examples/deprecation-warning/README.md        |    2 -
 examples/export-lora/README.md                |   12 +-
 examples/finetune/CMakeLists.txt              |    5 -
 examples/finetune/README.md                   |   90 -
 .../convert_finetune_checkpoint_to_gguf.py    |  487 -----
 examples/finetune/finetune.cpp                | 1862 -----------------
 examples/finetune/finetune.sh                 |   34 -
 .../train-text-from-scratch/CMakeLists.txt    |    5 -
 examples/train-text-from-scratch/README.md    |   27 -
 .../convert_train_checkpoint_to_gguf.py       |  499 -----
 .../train-text-from-scratch.cpp               | 1253 -----------
 15 files changed, 14 insertions(+), 4299 deletions(-)
 delete mode 100644 examples/finetune/CMakeLists.txt
 delete mode 100644 examples/finetune/README.md
 delete mode 100644 examples/finetune/convert_finetune_checkpoint_to_gguf.py
 delete mode 100644 examples/finetune/finetune.cpp
 delete mode 100644 examples/finetune/finetune.sh
 delete mode 100644 examples/train-text-from-scratch/CMakeLists.txt
 delete mode 100644 examples/train-text-from-scratch/README.md
 delete mode 100644 examples/train-text-from-scratch/convert_train_checkpoint_to_gguf.py
 delete mode 100644 examples/train-text-from-scratch/train-text-from-scratch.cpp

diff --git a/.devops/nix/apps.nix b/.devops/nix/apps.nix
index 897fce4d324c13..0ecf19fc56d554 100644
--- a/.devops/nix/apps.nix
+++ b/.devops/nix/apps.nix
@@ -10,7 +10,6 @@
             "llama-embedding"
             "llama-server"
             "llama-quantize"
-            "llama-train-text-from-scratch"
           ];
           mkApp = name: {
             type = "app";
diff --git a/.devops/tools.sh b/.devops/tools.sh
index cf0e8f32d738c0..24dcfd35079cb7 100755
--- a/.devops/tools.sh
+++ b/.devops/tools.sh
@@ -13,8 +13,6 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
     ./llama-quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
     ./llama-cli "$@"
-elif [[ "$arg1" == '--finetune' || "$arg1" == '-f' ]]; then
-    ./llama-finetune "$@"
 elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
     echo "Converting PTH to GGML..."
     for i in `ls $1/$2/ggml-model-f16.bin*`; do
@@ -36,8 +34,6 @@ else
     echo "              ex: --outtype f16 \"/models/7B/\" "
     echo "  --quantize (-q): Optimize with quantization process ggml"
     echo "              ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
-    echo "  --finetune (-f): Run finetune command to create a lora finetune of the model"
-    echo "              See documentation for finetune for command-line parameters"
     echo "  --all-in-one (-a): Execute --convert & --quantize"
     echo "              ex: \"/models/\" 7B"
     echo "  --server (-s): Run a model on the server"
diff --git a/Makefile b/Makefile
index 58a93db1aac301..8d2ccddc469f97 100644
--- a/Makefile
+++ b/Makefile
@@ -11,7 +11,6 @@ BUILD_TARGETS = \
 	llama-embedding \
 	llama-eval-callback \
 	llama-export-lora \
-	llama-finetune \
 	llama-gbnf-validator \
 	llama-gguf \
 	llama-gguf-hash \
@@ -37,7 +36,6 @@ BUILD_TARGETS = \
 	llama-simple \
 	llama-speculative \
 	llama-tokenize \
-	llama-train-text-from-scratch \
 	llama-vdot \
 	llama-cvector-generator \
 	tests/test-c.o
@@ -64,13 +62,13 @@ TEST_TARGETS = \
 	tests/test-tokenizer-1-spm
 
 # Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned
-LEGACY_TARGETS_CLEAN = main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
+LEGACY_TARGETS_CLEAN = main quantize quantize-stats perplexity imatrix embedding vdot q8dot convert-llama2c-to-ggml \
 	simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \
-	retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm
+	retrieval speculative infill tokenize benchmark-matmult parallel export-lora lookahead lookup passkey gritlm
 
 # Legacy build targets that were renamed in #7809, but we want to build binaries that for them that output a deprecation warning if people try to use them.
 #  We don't want to clutter things too much, so we only build replacements for the most commonly used binaries.
-LEGACY_TARGETS_BUILD = main quantize perplexity embedding server finetune
+LEGACY_TARGETS_BUILD = main quantize perplexity embedding server
 
 # Deprecation aliases
 ifdef LLAMA_CUBLAS
@@ -1296,11 +1294,6 @@ llama-cvector-generator: examples/cvector-generator/cvector-generator.cpp \
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
 llama-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp \
 	$(OBJ_GGML) $(OBJ_LLAMA)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
@@ -1316,11 +1309,6 @@ llama-baby-llama: examples/baby-llama/baby-llama.cpp \
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-finetune: examples/finetune/finetune.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
 llama-export-lora: examples/export-lora/export-lora.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
@@ -1578,7 +1566,7 @@ llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \
 # Deprecated binaries that we want to keep around long enough for people to migrate to the new filenames, then these can be removed.
 #
 # Mark legacy binary targets as .PHONY so that they are always checked.
-.PHONY: main quantize perplexity embedding server finetune
+.PHONY: main quantize perplexity embedding server
 
 # NOTE: We currently will always build the deprecation-warning `main` and `server` binaries to help users migrate.
 #  Eventually we will want to remove these target from building all the time.
@@ -1621,13 +1609,3 @@ ifneq (,$(wildcard embedding))
 	@echo "  Remove the 'embedding' binary to remove this warning."
 	@echo "#########"
 endif
-
-finetune: examples/deprecation-warning/deprecation-warning.cpp
-ifneq (,$(wildcard finetune))
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-	@echo "#########"
-	@echo "WARNING: The 'finetune' binary is deprecated. Please use 'llama-finetune' instead."
-	@echo "  Remove the 'finetune' binary to remove this warning."
-	@echo "#########"
-endif
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 155743639adfda..67b3d277478508 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -21,7 +21,6 @@ else()
     add_subdirectory(embedding)
     add_subdirectory(eval-callback)
     add_subdirectory(export-lora)
-    add_subdirectory(finetune)
     add_subdirectory(gbnf-validator)
     add_subdirectory(gguf-hash)
     add_subdirectory(gguf-split)
@@ -53,5 +52,4 @@ else()
     add_subdirectory(simple)
     add_subdirectory(speculative)
     add_subdirectory(tokenize)
-    add_subdirectory(train-text-from-scratch)
 endif()
diff --git a/examples/deprecation-warning/README.md b/examples/deprecation-warning/README.md
index 1e20feb4aab872..59918ec2bbf72f 100644
--- a/examples/deprecation-warning/README.md
+++ b/examples/deprecation-warning/README.md
@@ -13,7 +13,6 @@ Please update all scripts and workflows to use the new binary names.
 | server | llama-server |
 | llama-bench | llama-bench |
 | embedding | llama-embedding |
-| finetune | llama-finetune |
 | quantize | llama-quantize |
 | tokenize | llama-tokenize |
 | export-lora | llama-export-lora |
@@ -45,7 +44,6 @@ Please update all scripts and workflows to use the new binary names.
 | save-load-state | llama-save-load-state |
 | simple | llama-simple |
 | speculative | llama-speculative |
-| train-text-from-scratch | llama-train-text-from-scratch |
 | vdot | llama-vdot |
 | tests/test-c.o | tests/test-c.o |
 
diff --git a/examples/export-lora/README.md b/examples/export-lora/README.md
index 6d51f4b24dc6f4..91c33c34acaa93 100644
--- a/examples/export-lora/README.md
+++ b/examples/export-lora/README.md
@@ -19,7 +19,15 @@ For example:
 ./bin/llama-export-lora \
     -m open-llama-3b-v2-q8_0.gguf \
     -o open-llama-3b-v2-q8_0-english2tokipona-chat.gguf \
-    --lora lora-open-llama-3b-v2-q8_0-english2tokipona-chat-LATEST.bin
+    --lora lora-open-llama-3b-v2-q8_0-english2tokipona-chat-LATEST.gguf
 ```
 
-Multiple LORA adapters can be applied by passing multiple `--lora FNAME` or `--lora-scaled FNAME S` command line parameters.
+Multiple LORA adapters can be applied by passing multiple `--lora FNAME` or `--lora-scaled FNAME S` command line parameters:
+
+```bash
+./bin/llama-export-lora \
+    -m your_base_model.gguf \
+    -o your_merged_model.gguf \
+    --lora-scaled lora_task_A.gguf 0.5 \
+    --lora-scaled lora_task_B.gguf 0.5
+```
diff --git a/examples/finetune/CMakeLists.txt b/examples/finetune/CMakeLists.txt
deleted file mode 100644
index 64afe6ddc647ae..00000000000000
--- a/examples/finetune/CMakeLists.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-set(TARGET llama-finetune)
-add_executable(${TARGET} finetune.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/finetune/README.md b/examples/finetune/README.md
deleted file mode 100644
index 1c27df053bc046..00000000000000
--- a/examples/finetune/README.md
+++ /dev/null
@@ -1,90 +0,0 @@
-# finetune
-
-Basic usage instructions:
-
-```bash
-# get training data
-wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt
-
-# finetune LORA adapter
-./bin/llama-finetune \
-        --model-base open-llama-3b-v2-q8_0.gguf \
-        --checkpoint-in  chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf \
-        --checkpoint-out chk-lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.gguf \
-        --lora-out lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.bin \
-        --train-data "shakespeare.txt" \
-        --save-every 10 \
-        --threads 6 --adam-iter 30 --batch 4 --ctx 64 \
-        --use-checkpointing
-
-# predict
-./bin/llama-cli -m open-llama-3b-v2-q8_0.gguf --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
-```
-
-**Only llama based models are supported!** The output files will be saved every N iterations (config with `--save-every N`).
-The pattern 'ITERATION' in the output filenames will be replaced with the iteration number and with 'LATEST' for the latest output.
-So in above example after 10 iterations these files will be written:
-- chk-lora-open-llama-3b-v2-q8_0-shakespeare-10.gguf
-- chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf
-- lora-open-llama-3b-v2-q8_0-shakespeare-10.bin
-- lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
-
-After 10 more iterations:
-- chk-lora-open-llama-3b-v2-q8_0-shakespeare-20.gguf
-- chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf
-- lora-open-llama-3b-v2-q8_0-shakespeare-20.bin
-- lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
-
-Checkpoint files (`--checkpoint-in FN`, `--checkpoint-out FN`) store the training process. When the input checkpoint file does not exist, it will begin finetuning a new randomly initialized adapter.
-
-llama.cpp compatible LORA adapters will be saved with filename specified by `--lora-out FN`.
-These LORA adapters can then be used by `llama-cli` together with the base model, like in the 'predict' example command above.
-
-In `llama-cli` you can also load multiple LORA adapters, which will then be mixed together.
-
-For example if you have two LORA adapters `lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin` and `lora-open-llama-3b-v2-q8_0-bible-LATEST.bin`, you can mix them together like this:
-
-```bash
-./bin/llama-cli -m open-llama-3b-v2-q8_0.gguf \
-  --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin \
-  --lora lora-open-llama-3b-v2-q8_0-bible-LATEST.bin
-```
-
-You can change how strong each LORA adapter is applied to the base model by using `--lora-scaled FN SCALE` instead of `--lora FN`.
-
-For example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' LORA adapter and 100% of yet another one:
-
-```bash
-./bin/llama-cli -m open-llama-3b-v2-q8_0.gguf \
-  --lora-scaled lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin 0.4 \
-  --lora-scaled lora-open-llama-3b-v2-q8_0-bible-LATEST.bin 0.8 \
-  --lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin
-```
-
-The scale numbers don't need to add up to one, and you can also use numbers greater than 1 to further increase the influence of an adapter. But making the values too big will sometimes result in worse output. Play around to find good values.
-
-Gradient checkpointing reduces the memory requirements by ~50% but increases the runtime.
-If you have enough RAM, you can make finetuning a bit faster by disabling checkpointing with `--no-checkpointing`.
-
-The default LORA rank can be specified with `--lora-r N`.
-The LORA rank can be configured for each model tensor type separately with these command line options:
-
-```bash
-  --lora-r N                 LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default 4)
-  --rank-att-norm N          LORA rank for attention norm tensor (default 1)
-  --rank-ffn-norm N          LORA rank for feed-forward norm tensor (default 1)
-  --rank-out-norm N          LORA rank for output norm tensor (default 1)
-  --rank-tok-embd N          LORA rank for token embeddings tensor (default 4)
-  --rank-out N               LORA rank for output tensor (default 4)
-  --rank-wq N                LORA rank for wq tensor (default 4)
-  --rank-wk N                LORA rank for wk tensor (default 4)
-  --rank-wv N                LORA rank for wv tensor (default 4)
-  --rank-wo N                LORA rank for wo tensor (default 4)
-  --rank-ffn_gate N          LORA rank for ffn_gate tensor (default 4)
-  --rank-ffn_down N          LORA rank for ffn_down tensor (default 4)
-  --rank-ffn_up N            LORA rank for ffn_up tensor (default 4)
-```
-
-The LORA rank of 'norm' tensors should always be 1.
-
-To see all available options use `llama-finetune --help`.
diff --git a/examples/finetune/convert_finetune_checkpoint_to_gguf.py b/examples/finetune/convert_finetune_checkpoint_to_gguf.py
deleted file mode 100644
index 1b79d6995d92a5..00000000000000
--- a/examples/finetune/convert_finetune_checkpoint_to_gguf.py
+++ /dev/null
@@ -1,487 +0,0 @@
-#!/usr/bin/env python3
-# finetune checkpoint --> gguf conversion
-
-import argparse
-import gguf
-import struct
-import numpy as np
-from pathlib import Path
-
-# gguf constants
-LLM_KV_OPTIMIZER_TYPE = "optimizer.type"
-LLM_KV_OPTIMIZER_TYPE_ADAM  = "adam"
-LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"
-LLM_KV_OPTIMIZER_FILE_VERSION               = "optimizer.file_version"
-LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT     = "optimizer.convergence_past_count"
-LLM_KV_OPTIMIZER_PARAMETER_COUNT            = "optimizer.parameter_count"
-LLM_KV_OPTIMIZER_ITERATION_COUNT            = "optimizer.iteration_count"
-LLM_KV_OPTIMIZER_JUST_INITIALIZED           = "optimizer.just_initialized"
-LLM_KV_OPTIMIZER_ADAM_BEST_LOSS             = "optimizer.adam.best_loss"
-LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS         = "optimizer.adam.previous_loss"
-LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT  = "optimizer.adam.no_improvement_count"
-LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"
-LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS            = "optimizer.lbfgs.best_loss"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP     = "optimizer.lbfgs.line_search_step"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J        = "optimizer.lbfgs.line_search_j"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K        = "optimizer.lbfgs.line_search_k"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END      = "optimizer.lbfgs.line_search_end"
-LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"
-
-LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS    = "optimizer.adam.first_moments"
-LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS   = "optimizer.adam.second_moments"
-LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"
-
-LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS  = "optimizer.lbfgs.current_parameters"
-LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"
-LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS   = "optimizer.lbfgs.current_gradients"
-LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS  = "optimizer.lbfgs.previous_gradients"
-LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION    = "optimizer.lbfgs.search_direction"
-LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES    = "optimizer.lbfgs.past_loss_values"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA        = "optimizer.lbfgs.memory_alpha"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS           = "optimizer.lbfgs.memory_ys"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S            = "optimizer.lbfgs.memory_s"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y            = "optimizer.lbfgs.memory_y"
-
-LLM_KV_TRAINING_TYPE_TRAIN_MODEL   = "train_model"
-LLM_KV_TRAINING_TYPE_FINETUNE_LORA = "finetune_lora"
-LLM_KV_TRAINING_TYPE               = "training.type"
-LLM_KV_TRAINING_FILE_VERSION       = "training.file_version"
-LLM_KV_TRAINING_ITERATION_COUNT    = "training.iteration_count"
-LLM_KV_TRAINING_SAMPLE_COUNT       = "training.sample_count"
-LLM_KV_TRAINING_TOKEN_COUNT        = "training.token_count"
-
-LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD  = "training.lora.rank.token_embd"
-LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm"
-LLM_KV_TRAINING_LORA_RANK_OUTPUT      = "training.lora.rank.output"
-LLM_KV_TRAINING_LORA_RANK_ATTN_NORM   = "training.lora.rank.attn_norm"
-LLM_KV_TRAINING_LORA_RANK_ATTN_Q      = "training.lora.rank.attn_q"
-LLM_KV_TRAINING_LORA_RANK_ATTN_K      = "training.lora.rank.attn_k"
-LLM_KV_TRAINING_LORA_RANK_ATTN_V      = "training.lora.rank.attn_v"
-LLM_KV_TRAINING_LORA_RANK_ATTN_OUT    = "training.lora.rank.attn_output"
-LLM_KV_TRAINING_LORA_RANK_FFN_NORM    = "training.lora.rank.ffn_norm"
-LLM_KV_TRAINING_LORA_RANK_FFN_GATE    = "training.lora.rank.ffn_gate"
-LLM_KV_TRAINING_LORA_RANK_FFN_DOWN    = "training.lora.rank.ffn_down"
-LLM_KV_TRAINING_LORA_RANK_FFN_UP      = "training.lora.rank.ffn_up"
-
-class Tensor:
-    def __init__(self, dtype='f', ne=None):
-        if ne is None:
-            ne = []
-        self.dtype = dtype
-        self.ne = ne
-        self.nbytes = 0
-        if self.dtype == 'f':
-            if len(self.ne) == 0:
-                self.nbytes = 0
-            else:
-                self.nbytes = int(np.prod(self.ne)) * 4
-        else:
-            raise ValueError(f"Unhandled data type '{self.dtype}'")
-
-    def load(self, data, offset):
-        nd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        namelen = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        dtype = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-
-        assert(nd == len(self.ne))
-        ne = []
-        for d in range(nd):
-            n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-            ne.append(n)
-
-        if tuple(ne) != tuple(self.ne):
-            raise ValueError(f"Tensor.load: Expected number of elements {str(self.ne)} does not match what is read from file {str(ne)}")
-
-        if self.dtype == 'f':
-            assert(dtype == 0)
-        else:
-            raise ValueError(f"Unhandled data type '{self.dtype}'")
-
-        self.name = bytes(data[offset:offset+namelen]); offset += namelen
-        # 32-byte alignment
-        offset += (0 - offset) & 31
-        self.data = data[offset:offset+self.nbytes]
-        offset += self.nbytes
-        return offset
-
-    def max_storage_size(self):
-        result = 0
-        result += 4 # nd
-        result += 4 # namelen
-        result += 4 # dtype
-        result += len(self.ne)*8 # ne
-        result += 48 # name (maximum as of commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9)
-        result += 31 # 32-byte alignment
-        result += self.nbytes
-        return result
-
-    def save_gguf(self, gguf_writer, name):
-        gguf_writer.add_tensor(
-            name=name,
-            tensor=self.data,
-            raw_shape=np.array(list(reversed(self.ne))),
-            raw_dtype=gguf.GGMLQuantizationType.F32)
-
-class OptimizationContext:
-    def __init__(self):
-        pass
-
-    def load(self, data, offset):
-        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
-        offset += 4
-
-        if self.version != 1:
-            raise ValueError('Invalid version of optimization context in checkpoint file')
-
-        self.past    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.nx      = struct.unpack('N',  bytes(data[offset:offset + 8]))[0];  offset += 8
-        self.iter    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]);  offset += 4
-
-        self.adam_m  = Tensor('f', [self.nx])
-        self.adam_v  = Tensor('f', [self.nx])
-        self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
-
-        self.lbfgs_x    = Tensor('f', [self.nx])
-        self.lbfgs_xp   = Tensor('f', [self.nx])
-        self.lbfgs_g    = Tensor('f', [self.nx])
-        self.lbfgs_gp   = Tensor('f', [self.nx])
-        self.lbfgs_d    = Tensor('f', [self.nx])
-        self.lbfgs_pf   = Tensor('f', [self.past] if self.past > 0 else [])
-        self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
-        self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
-        self.lbfgs_lms  = Tensor('f', [self.nx, self.lbfgs_m])
-        self.lbfgs_lmy  = Tensor('f', [self.nx, self.lbfgs_m])
-
-        # forgot to save type in version 1:
-        # guess self.type from number of remaining bytes
-        size_type_0 = 12 + sum([t.max_storage_size() for t in
-                                [self.adam_m, self.adam_v]
-                                +([self.adam_pf] if (self.past > 0) else [])])
-        size_type_1 = 24 + sum([t.max_storage_size() for t in
-                                [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,
-                                 self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,
-                                 self.lbfgs_lmal, self.lbfgs_lmys,
-                                 self.lbfgs_lms, self.lbfgs_lmy]
-                                 +([self.lbfgs_pf] if (self.past > 0) else [])])
-        # due to alignment padding the size might not by exact
-        # but the difference in size for both types is significant,
-        # so we can just use whichever is closest
-        remaining = len(data) - offset
-        if abs(remaining - size_type_0) < abs(remaining - size_type_1):
-            self.type = 0
-        else:
-            self.type = 1
-
-        if self.type == 0:
-            offset = self.adam_m.load(data, offset)
-            offset = self.adam_v.load(data, offset)
-            offset = self.adam_pf.load(data,offset)
-
-            self.adam_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.adam_fx_prev          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-        elif self.type == 1:
-            offset = self.lbfgs_x.load(data, offset)
-            offset = self.lbfgs_xp.load(data, offset)
-            offset = self.lbfgs_g.load(data, offset)
-            offset = self.lbfgs_gp.load(data, offset)
-            offset = self.lbfgs_d.load(data, offset)
-            offset = self.lbfgs_pf.load(data, offset)
-            offset = self.lbfgs_lmal.load(data, offset)
-            offset = self.lbfgs_lmys.load(data, offset)
-            offset = self.lbfgs_lms.load(data, offset)
-            offset = self.lbfgs_lmy.load(data, offset)
-
-            self.lbfgs_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_step             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_j                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_k                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_end              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-        else:
-            raise ValueError(f"Invalid optimizer type '{self.type}'")
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_FILE_VERSION, 0)
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past)
-        gguf_writer.add_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx)
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter)
-        gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized)
-
-        if self.type == 0:
-            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, self.adam_fx_best)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, self.adam_fx_prev)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, self.adam_n_no_improvement)
-
-            self.adam_m.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS)
-            self.adam_v.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS)
-            if self.past > 0:
-                self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES)
-
-        elif self.type == 1:
-            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement)
-
-            self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS)
-            self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS)
-            self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS)
-            self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS)
-            self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION)
-            if self.past > 0:
-                self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES)
-            self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA)
-            self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS)
-            self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S)
-            self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y)
-        else:
-            raise ValueError('Unknown optimizer type')
-
-class LoraParams:
-    def __init__(self):
-        pass
-
-    def load(self, data, offset):
-        self.n_rank_attention_norm  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_wq              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_wk              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_wv              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_wo              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_ffn_norm        = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_w1              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_w2              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_w3              = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_tok_embeddings  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_norm            = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rank_output          = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD,  self.n_rank_tok_embeddings)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM, self.n_rank_norm)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_OUTPUT,      self.n_rank_output)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_NORM,   self.n_rank_attention_norm)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_Q,      self.n_rank_wq)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_K,      self.n_rank_wk)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_V,      self.n_rank_wv)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_ATTN_OUT,    self.n_rank_wo)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_NORM,    self.n_rank_ffn_norm)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_GATE,    self.n_rank_w1)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_DOWN,    self.n_rank_w2)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_LORA_RANK_FFN_UP,      self.n_rank_w3)
-
-class ModelParams:
-    def __init__(self, n_ff = None):
-        self.n_ff = n_ff
-
-    def load(self, data, offset):
-        self.n_vocab = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_embd  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_mult  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_head  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_layer = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rot   = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        return offset
-
-    def get_n_ff(self):
-        if self.n_ff is None:
-            # struct my_llama_model::get_n_ff in train-text-from-scratch.cpp commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9
-            return ((2*(4*self.n_embd)//3 + self.n_mult - 1)//self.n_mult)*self.n_mult
-        else:
-            return self.n_ff
-
-    def save_gguf(self, gguf_writer):
-        # self.n_vocab not saved
-        gguf_writer.add_embedding_length(self.n_embd)
-        gguf_writer.add_head_count(self.n_head)
-        gguf_writer.add_block_count(self.n_layer)
-        gguf_writer.add_rope_dimension_count(self.n_rot)
-        gguf_writer.add_feed_forward_length(self.get_n_ff())
-
-def tensor_name(key, bid=None, suffix=".weight"):
-    return gguf.TENSOR_NAMES[key].format(bid=bid) + suffix
-
-class Layer:
-    def __init__(self, params, lora_params, bid):
-        self.bid = bid
-        self.att_norm_a = Tensor('f', [lora_params.n_rank_attention_norm, params.n_embd])
-        self.att_norm_b = Tensor('f', [lora_params.n_rank_attention_norm, 1])
-        self.wq_a       = Tensor('f', [lora_params.n_rank_wq, params.n_embd])
-        self.wq_b       = Tensor('f', [lora_params.n_rank_wq, params.n_embd])
-        self.wk_a       = Tensor('f', [lora_params.n_rank_wk, params.n_embd])
-        self.wk_b       = Tensor('f', [lora_params.n_rank_wk, params.n_embd])
-        self.wv_a       = Tensor('f', [lora_params.n_rank_wv, params.n_embd])
-        self.wv_b       = Tensor('f', [lora_params.n_rank_wv, params.n_embd])
-        self.wo_a       = Tensor('f', [lora_params.n_rank_wo, params.n_embd])
-        self.wo_b       = Tensor('f', [lora_params.n_rank_wo, params.n_embd])
-        self.ffn_norm_a = Tensor('f', [lora_params.n_rank_ffn_norm, params.n_embd])
-        self.ffn_norm_b = Tensor('f', [lora_params.n_rank_ffn_norm, 1])
-        self.w1_a       = Tensor('f', [lora_params.n_rank_w1, params.n_embd])
-        self.w1_b       = Tensor('f', [lora_params.n_rank_w1, params.get_n_ff()])
-        self.w2_a       = Tensor('f', [lora_params.n_rank_w2, params.get_n_ff()])
-        self.w2_b       = Tensor('f', [lora_params.n_rank_w2, params.n_embd])
-        self.w3_a       = Tensor('f', [lora_params.n_rank_w3, params.n_embd])
-        self.w3_b       = Tensor('f', [lora_params.n_rank_w3, params.get_n_ff()])
-
-    def load(self, data, offset):
-        offset = self.att_norm_a.load(data, offset)
-        offset = self.att_norm_b.load(data, offset)
-        offset = self.wq_a.load(data, offset)
-        offset = self.wq_b.load(data, offset)
-        offset = self.wk_a.load(data, offset)
-        offset = self.wk_b.load(data, offset)
-        offset = self.wv_a.load(data, offset)
-        offset = self.wv_b.load(data, offset)
-        offset = self.wo_a.load(data, offset)
-        offset = self.wo_b.load(data, offset)
-        offset = self.ffn_norm_a.load(data, offset)
-        offset = self.ffn_norm_b.load(data, offset)
-        offset = self.w1_a.load(data, offset)
-        offset = self.w1_b.load(data, offset)
-        offset = self.w2_a.load(data, offset)
-        offset = self.w2_b.load(data, offset)
-        offset = self.w3_a.load(data, offset)
-        offset = self.w3_b.load(data, offset)
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        self.att_norm_a.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid, ".weight.lora_a"))
-        self.att_norm_b.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid, ".weight.lora_b"))
-        self.wq_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q,    self.bid, ".weight.lora_a"))
-        self.wq_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q,    self.bid, ".weight.lora_b"))
-        self.wk_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K,    self.bid, ".weight.lora_a"))
-        self.wk_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K,    self.bid, ".weight.lora_b"))
-        self.wv_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V,    self.bid, ".weight.lora_a"))
-        self.wv_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V,    self.bid, ".weight.lora_b"))
-        self.wo_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT,  self.bid, ".weight.lora_a"))
-        self.wo_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT,  self.bid, ".weight.lora_b"))
-        self.ffn_norm_a.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM,  self.bid, ".weight.lora_a"))
-        self.ffn_norm_b.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM,  self.bid, ".weight.lora_b"))
-        self.w1_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE,  self.bid, ".weight.lora_a"))
-        self.w1_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE,  self.bid, ".weight.lora_b"))
-        self.w2_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN,  self.bid, ".weight.lora_a"))
-        self.w2_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN,  self.bid, ".weight.lora_b"))
-        self.w3_a.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP,    self.bid, ".weight.lora_a"))
-        self.w3_b.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP,    self.bid, ".weight.lora_b"))
-
-class LoraModel:
-    def __init__(self, n_ff = None):
-        self.params = ModelParams(n_ff = n_ff)
-        self.lora_params = LoraParams()
-        self.layers = []
-
-    def load(self, data, offset):
-        offset = self.params.load(data, offset)
-        offset = self.lora_params.load(data, offset)
-
-        self.tok_embd_a = Tensor('f', [self.lora_params.n_rank_tok_embeddings, self.params.n_embd])
-        self.tok_embd_b = Tensor('f', [self.lora_params.n_rank_tok_embeddings, self.params.n_vocab])
-        self.norm_a     = Tensor('f', [self.lora_params.n_rank_norm, self.params.n_embd])
-        self.norm_b     = Tensor('f', [self.lora_params.n_rank_norm, 1])
-        self.output_a   = Tensor('f', [self.lora_params.n_rank_output, self.params.n_embd])
-        self.output_b   = Tensor('f', [self.lora_params.n_rank_output, self.params.n_vocab])
-
-        offset = self.tok_embd_a.load(data, offset)
-        offset = self.tok_embd_b.load(data, offset)
-        offset = self.norm_a.load(data, offset)
-        offset = self.norm_b.load(data, offset)
-        offset = self.output_a.load(data, offset)
-        offset = self.output_b.load(data, offset)
-
-        self.layers.clear()
-        for bid in range(self.params.n_layer):
-            layer = Layer(self.params, self.lora_params, bid)
-            offset = layer.load(data, offset)
-            self.layers.append(layer)
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        self.params.save_gguf(gguf_writer)
-        self.lora_params.save_gguf(gguf_writer)
-
-        self.tok_embd_a.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD,  suffix=".weight.lora_a"))
-        self.tok_embd_b.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD,  suffix=".weight.lora_b"))
-        self.norm_a.save_gguf    (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM, suffix=".weight.lora_a"))
-        self.norm_b.save_gguf    (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM, suffix=".weight.lora_b"))
-        self.output_a.save_gguf  (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT,      suffix=".weight.lora_a"))
-        self.output_b.save_gguf  (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT,      suffix=".weight.lora_b"))
-
-        for layer in self.layers:
-            layer.save_gguf(gguf_writer)
-
-class LoraCheckpoint:
-    def __init__(self, n_ff = None):
-        self.model = LoraModel(n_ff = n_ff)
-        self.opt_ctx = OptimizationContext()
-
-    def load(self, data, offset):
-        magic   = bytes(reversed(data[offset:offset + 4])); offset += 4
-        if magic != b'ggcl':
-            raise ValueError(f"File header magic indicates, that this is no finetune-lora checkpoint file. Expected 'ggcl', Got '{str(magic)}'")
-
-        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        if self.version != 0:
-            raise ValueError('Invalid version of checkpoint file')
-
-        self.train_its     = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        self.train_samples = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        self.train_tokens  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-
-        offset = self.model.load(data, offset)
-        offset = self.opt_ctx.load(data, offset)
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32)
-        gguf_writer.add_layer_norm_rms_eps(1e-5)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION,    0)
-        gguf_writer.add_string(LLM_KV_TRAINING_TYPE,            LLM_KV_TRAINING_TYPE_FINETUNE_LORA)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_ITERATION_COUNT, self.train_its)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_SAMPLE_COUNT,    self.train_samples)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_TOKEN_COUNT,     self.train_tokens)
-        self.model.save_gguf(gguf_writer)
-        self.opt_ctx.save_gguf(gguf_writer)
-
-def handle_args():
-    parser = argparse.ArgumentParser(description = 'Convert finetune checkpoints to GGUF')
-    parser.add_argument('--input',  '-i', type = Path, help = 'Input finetune checkpoint filename', required=True)
-    parser.add_argument('--output', '-o', type = Path, help = 'Output GGUF filename', required=True)
-    parser.add_argument('--ff', type = int, help = "Feedforward size, if not provided compute from n_mult. Provide this if you get 'ValueError: Tensor.load: Expected number of elements does not match what is read from file'", required=False)
-    return parser.parse_args()
-
-def main():
-    cfg = handle_args()
-    print(cfg)
-    data = np.memmap(cfg.input, mode = 'r')
-    chk = LoraCheckpoint(n_ff = cfg.ff)
-    offset = 0
-    offset = chk.load(data, offset)
-    # we should have read all available data
-    assert(offset == len(data))
-
-    gguf_writer = gguf.GGUFWriter(cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
-    chk.save_gguf(gguf_writer)
-    print("    gguf: write header")
-    gguf_writer.write_header_to_file()
-    print("    gguf: write metadata")
-    gguf_writer.write_kv_data_to_file()
-    print("    gguf: write tensors")
-    gguf_writer.write_tensors_to_file()
-    gguf_writer.close()
-
-if __name__ == '__main__':
-    main()
diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp
deleted file mode 100644
index 71a4333ee79084..00000000000000
--- a/examples/finetune/finetune.cpp
+++ /dev/null
@@ -1,1862 +0,0 @@
-#include "ggml.h"
-#include "ggml-alloc.h"
-#include "ggml-backend.h"
-#include "llama.h"
-#include "common.h"
-#include "train.h"
-#include <vector>
-#include <cstring>
-#include <ctime>
-#include <algorithm>
-#include <string>
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
-struct my_llama_hparams {
-    uint32_t n_vocab    = 32000;
-    uint32_t n_ctx      = 512;
-    uint32_t n_embd     = 4096;
-    uint32_t n_ff       = 11008;
-    uint32_t n_head     = 32;
-    uint32_t n_head_kv  = 32;
-    uint32_t n_layer    = 32;
-
-    // float f_norm_eps     = 1e-5f; // falcon
-    float f_norm_rms_eps = 1e-5f; // llama
-
-    float rope_freq_base  = 10000.0f;
-    float rope_freq_scale = 1.0f;
-
-    uint32_t n_gqa() const {
-        return n_head/n_head_kv;
-    }
-
-    uint32_t n_embd_head() const {
-        return n_embd/n_head;
-    }
-
-    uint32_t n_embd_gqa() const {
-        return n_embd/n_gqa();
-    }
-
-    bool operator!=(const my_llama_hparams& other) const {
-        return memcmp(this, &other, sizeof(other));
-    }
-};
-
-struct my_llama_layer {
-    // normalization
-    struct ggml_tensor * attention_norm;
-
-    // attention
-    struct ggml_tensor * wq;
-    struct ggml_tensor * wk;
-    struct ggml_tensor * wv;
-    struct ggml_tensor * wo;
-
-    // normalization
-    struct ggml_tensor * ffn_norm;
-
-    // ff
-    struct ggml_tensor * ffn_gate; // w1
-    struct ggml_tensor * ffn_down; // w2
-    struct ggml_tensor * ffn_up;   // w3
-};
-
-struct my_llama_model {
-    struct my_llama_hparams hparams;
-
-    struct ggml_tensor * tok_embeddings;
-
-    struct ggml_tensor * norm;
-    struct ggml_tensor * output;
-
-    std::vector<my_llama_layer> layers;
-};
-
-struct my_llama_lora_hparams {
-    uint32_t lora_r = 1;
-    uint32_t lora_alpha = 1;
-    uint32_t n_rank_attention_norm = 1;
-    uint32_t n_rank_wq = 4;
-    uint32_t n_rank_wk = 4;
-    uint32_t n_rank_wv = 4;
-    uint32_t n_rank_wo = 4;
-    uint32_t n_rank_ffn_norm = 1;
-    uint32_t n_rank_ffn_gate = 4;
-    uint32_t n_rank_ffn_down = 4;
-    uint32_t n_rank_ffn_up = 4;
-    uint32_t n_rank_tok_embeddings = 4;
-    uint32_t n_rank_norm = 1;
-    uint32_t n_rank_output = 4;
-
-    bool operator!=(const my_llama_lora_hparams& other) const {
-        return memcmp(this, &other, sizeof(other));
-    }
-};
-
-struct my_llama_lora_layer {
-    // normalization
-    struct ggml_tensor * attention_norm_a;
-    struct ggml_tensor * attention_norm_b;
-
-    // attention
-    struct ggml_tensor * wq_a;
-    struct ggml_tensor * wq_b;
-    struct ggml_tensor * wk_a;
-    struct ggml_tensor * wk_b;
-    struct ggml_tensor * wv_a;
-    struct ggml_tensor * wv_b;
-    struct ggml_tensor * wo_a;
-    struct ggml_tensor * wo_b;
-
-    // normalization
-    struct ggml_tensor * ffn_norm_a;
-    struct ggml_tensor * ffn_norm_b;
-
-    // ff
-    struct ggml_tensor * ffn_gate_a;
-    struct ggml_tensor * ffn_gate_b;
-    struct ggml_tensor * ffn_down_a;
-    struct ggml_tensor * ffn_down_b;
-    struct ggml_tensor * ffn_up_a;
-    struct ggml_tensor * ffn_up_b;
-};
-
-struct my_llama_lora {
-    struct ggml_context * ctx = NULL;
-    ggml_backend_buffer_t data;
-
-    my_llama_lora_hparams hparams;
-
-    struct ggml_tensor * tok_embeddings_a;
-    struct ggml_tensor * tok_embeddings_b;
-
-    struct ggml_tensor * norm_a;
-    struct ggml_tensor * norm_b;
-    struct ggml_tensor * output_a;
-    struct ggml_tensor * output_b;
-
-    std::vector<my_llama_lora_layer> layers;
-};
-
-// gguf constants
-static const char * LLM_KV_TRAINING_TYPE_FINETUNE_LORA   = "finetune_lora";
-static const char * LLM_KV_TRAINING_TYPE                 = "training.type";
-
-static const char * LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD  = "training.lora.rank.token_embd";
-static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM = "training.lora.rank.output_norm";
-static const char * LLM_KV_TRAINING_LORA_RANK_OUTPUT      = "training.lora.rank.output";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_NORM   = "training.lora.rank.attn_norm";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_Q      = "training.lora.rank.attn_q";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_K      = "training.lora.rank.attn_k";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_V      = "training.lora.rank.attn_v";
-static const char * LLM_KV_TRAINING_LORA_RANK_ATTN_OUT    = "training.lora.rank.attn_output";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_NORM    = "training.lora.rank.ffn_norm";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_GATE    = "training.lora.rank.ffn_gate";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_DOWN    = "training.lora.rank.ffn_down";
-static const char * LLM_KV_TRAINING_LORA_RANK_FFN_UP      = "training.lora.rank.ffn_up";
-
-// gguf constants (sync with gguf.py)
-
-static const char * LLM_KV_GENERAL_ARCHITECTURE        = "general.architecture";
-static const char * LLM_KV_GENERAL_FILE_TYPE           = "general.file_type";
-
-static const char * LLM_KV_CONTEXT_LENGTH              = "%s.context_length";
-static const char * LLM_KV_EMBEDDING_LENGTH            = "%s.embedding_length";
-static const char * LLM_KV_BLOCK_COUNT                 = "%s.block_count";
-static const char * LLM_KV_FEED_FORWARD_LENGTH         = "%s.feed_forward_length";
-static const char * LLM_KV_ATTENTION_HEAD_COUNT        = "%s.attention.head_count";
-static const char * LLM_KV_ATTENTION_HEAD_COUNT_KV     = "%s.attention.head_count_kv";
-static const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = "%s.attention.layer_norm_rms_epsilon";
-static const char * LLM_KV_ROPE_DIMENSION_COUNT        = "%s.rope.dimension_count";
-static const char * LLM_KV_ROPE_FREQ_BASE              = "%s.rope.freq_base"; // TODO load in llama.cpp
-static const char * LLM_KV_ROPE_SCALE_LINEAR           = "%s.rope.scale_linear";
-
-static const char * LLM_TENSOR_TOKEN_EMBD    = "token_embd";
-static const char * LLM_TENSOR_OUTPUT_NORM   = "output_norm";
-static const char * LLM_TENSOR_OUTPUT        = "output";
-static const char * LLM_TENSOR_ATTN_NORM     = "blk.%d.attn_norm";
-static const char * LLM_TENSOR_ATTN_Q        = "blk.%d.attn_q";
-static const char * LLM_TENSOR_ATTN_K        = "blk.%d.attn_k";
-static const char * LLM_TENSOR_ATTN_V        = "blk.%d.attn_v";
-static const char * LLM_TENSOR_ATTN_OUT      = "blk.%d.attn_output";
-static const char * LLM_TENSOR_FFN_NORM      = "blk.%d.ffn_norm";
-static const char * LLM_TENSOR_FFN_GATE      = "blk.%d.ffn_gate";
-static const char * LLM_TENSOR_FFN_DOWN      = "blk.%d.ffn_down";
-static const char * LLM_TENSOR_FFN_UP        = "blk.%d.ffn_up";
-
-static void print_params(struct my_llama_hparams * params) {
-    printf("%s: n_vocab               : %u\n", __func__, params->n_vocab);
-    printf("%s: n_ctx                 : %u\n", __func__, params->n_ctx);
-    printf("%s: n_embd                : %u\n", __func__, params->n_embd);
-    printf("%s: n_ff                  : %u\n", __func__, params->n_ff);
-    printf("%s: n_head                : %u\n", __func__, params->n_head);
-    printf("%s: n_head_kv             : %u\n", __func__, params->n_head_kv);
-    printf("%s: n_layer               : %u\n", __func__, params->n_layer);
-    printf("%s: norm_rms_eps          : %f\n", __func__, params->f_norm_rms_eps);
-    printf("%s: rope_freq_base        : %f\n", __func__, params->rope_freq_base);
-    printf("%s: rope_freq_scale       : %f\n", __func__, params->rope_freq_scale);
-}
-
-static void print_lora_params(struct my_llama_lora_hparams * params) {
-    printf("%s: n_rank_attention_norm : %u\n", __func__, params->n_rank_attention_norm);
-    printf("%s: n_rank_wq             : %u\n", __func__, params->n_rank_wq);
-    printf("%s: n_rank_wk             : %u\n", __func__, params->n_rank_wk);
-    printf("%s: n_rank_wv             : %u\n", __func__, params->n_rank_wv);
-    printf("%s: n_rank_wo             : %u\n", __func__, params->n_rank_wo);
-    printf("%s: n_rank_ffn_norm       : %u\n", __func__, params->n_rank_ffn_norm);
-    printf("%s: n_rank_ffn_gate       : %u\n", __func__, params->n_rank_ffn_gate);
-    printf("%s: n_rank_ffn_down       : %u\n", __func__, params->n_rank_ffn_down);
-    printf("%s: n_rank_ffn_up         : %u\n", __func__, params->n_rank_ffn_up);
-    printf("%s: n_rank_tok_embeddings : %u\n", __func__, params->n_rank_tok_embeddings);
-    printf("%s: n_rank_norm           : %u\n", __func__, params->n_rank_norm);
-    printf("%s: n_rank_output         : %u\n", __func__, params->n_rank_output);
-}
-
-#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
-{ \
-    const std::string skey(key); \
-    const int kid = gguf_find_key(ctx, skey.c_str()); \
-    if (kid >= 0) { \
-        enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
-        if (ktype != (type)) { \
-            die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \
-        } \
-        (dst) = func(ctx, kid); \
-    } else if (req) { \
-        die_fmt("key not found in model: %s", skey.c_str()); \
-    } \
-}
-
-static void load_model_hparams_gguf(struct gguf_context * ctx, struct my_llama_hparams * hparams, const char * expected_arch) {
-    std::string arch;
-
-    GGUF_GET_KEY(ctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
-    if (expected_arch != NULL) {
-        if (arch != expected_arch) {
-            printf("%s: arch=%s expected_arch=%s\n", __func__, arch.c_str(), expected_arch);
-        }
-        GGML_ASSERT(arch == expected_arch);
-    }
-
-    std::vector<char> keybuf;
-    keybuf.resize(512);
-    auto kv = [&arch, &keybuf](const char * key) -> const char * {
-        snprintf(keybuf.data(), keybuf.size(), key, arch.c_str());
-        return keybuf.data();
-    };
-
-    GGUF_GET_KEY(ctx, hparams->n_embd,         gguf_get_val_u32, GGUF_TYPE_UINT32,  true, kv(LLM_KV_EMBEDDING_LENGTH));
-    GGUF_GET_KEY(ctx, hparams->n_ctx,          gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH));
-    GGUF_GET_KEY(ctx, hparams->n_ff,           gguf_get_val_u32, GGUF_TYPE_UINT32,  true, kv(LLM_KV_FEED_FORWARD_LENGTH));
-    GGUF_GET_KEY(ctx, hparams->n_head,         gguf_get_val_u32, GGUF_TYPE_UINT32,  true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
-    GGUF_GET_KEY(ctx, hparams->n_layer,        gguf_get_val_u32, GGUF_TYPE_UINT32,  true, kv(LLM_KV_BLOCK_COUNT));
-
-    // n_head_kv is optional, default to n_head
-    hparams->n_head_kv = hparams->n_head;
-    GGUF_GET_KEY(ctx, hparams->n_head_kv,      gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV));
-
-    float rope_freq_scale = 1.0f;
-    GGUF_GET_KEY(ctx, hparams->f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
-    GGUF_GET_KEY(ctx, hparams->rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
-    GGUF_GET_KEY(ctx, rope_freq_scale,         gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
-    if (rope_freq_scale != 1.0f) {
-        hparams->rope_freq_scale = 1.0f / rope_freq_scale;
-    }
-}
-
-static void init_model(struct llama_model * input, struct my_llama_model * model, const char * fn_model, uint32_t n_ctx) {
-    auto & hparams = model->hparams;
-
-    std::vector<char> tn_buf;
-    tn_buf.resize(GGML_MAX_NAME);
-    auto tn = [&tn_buf](const char * key) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
-        return tn_buf.data();
-    };
-    auto tni = [&tn_buf](const char * key, int bid) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
-        std::string s = tn_buf.data();
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
-        return tn_buf.data();
-    };
-
-
-    // get parameters directly from gguf file
-    {
-        struct gguf_init_params params = {
-            /*.no_alloc = */ false,
-            /*.ctx      = */ NULL,
-        };
-        struct gguf_context * mctx = gguf_init_from_file(fn_model, params);
-
-        load_model_hparams_gguf(mctx, &hparams, "llama");
-
-        gguf_free(mctx);
-    }
-    hparams.n_vocab = llama_n_vocab(input);
-    hparams.n_ctx = n_ctx;
-
-    // get tensors from llama_model (possibly mmapped)
-    model->tok_embeddings = llama_get_model_tensor(input, tn(LLM_TENSOR_TOKEN_EMBD));
-    model->norm           = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT_NORM));
-    model->output         = llama_get_model_tensor(input, tn(LLM_TENSOR_OUTPUT));
-
-    assert_shape_2d(model->tok_embeddings, hparams.n_embd, hparams.n_vocab);
-    assert_shape_1d(model->norm,           hparams.n_embd);
-    assert_shape_2d(model->output,         hparams.n_embd, hparams.n_vocab);
-
-    model->layers.resize(hparams.n_layer);
-    for (uint32_t i = 0; i < hparams.n_layer; ++i) {
-        auto & layer = model->layers[i];
-
-        layer.attention_norm = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_NORM, i));
-        layer.wq             = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_Q, i));
-        layer.wk             = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_K, i));
-        layer.wv             = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_V, i));
-        layer.wo             = llama_get_model_tensor(input, tni(LLM_TENSOR_ATTN_OUT, i));
-        layer.ffn_norm       = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_NORM, i));
-        layer.ffn_gate       = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_GATE, i));
-        layer.ffn_down       = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_DOWN, i));
-        layer.ffn_up         = llama_get_model_tensor(input, tni(LLM_TENSOR_FFN_UP, i));
-
-        assert_shape_1d(layer.attention_norm, hparams.n_embd);
-        assert_shape_2d(layer.wq,             hparams.n_embd, hparams.n_embd);
-        assert_shape_2d(layer.wk,             hparams.n_embd, hparams.n_embd_gqa());
-        assert_shape_2d(layer.wv,             hparams.n_embd, hparams.n_embd_gqa());
-        assert_shape_2d(layer.wo,             hparams.n_embd, hparams.n_embd);
-        assert_shape_1d(layer.ffn_norm,       hparams.n_embd);
-        assert_shape_2d(layer.ffn_gate,       hparams.n_embd, hparams.n_ff);
-        assert_shape_2d(layer.ffn_down,       hparams.n_ff,   hparams.n_embd);
-        assert_shape_2d(layer.ffn_up,         hparams.n_embd, hparams.n_ff);
-    }
-}
-
-static void set_param_lora(struct my_llama_lora * lora) {
-    const uint32_t n_layer = lora->layers.size();
-
-    struct ggml_context* ctx = lora->ctx;
-
-    ggml_set_param(ctx, lora->tok_embeddings_a);
-    ggml_set_param(ctx, lora->tok_embeddings_b);
-    ggml_set_param(ctx, lora->norm_a);
-    ggml_set_param(ctx, lora->norm_b);
-    ggml_set_param(ctx, lora->output_a);
-    ggml_set_param(ctx, lora->output_b);
-
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = lora->layers[i];
-
-        ggml_set_param(ctx, layer.attention_norm_a);
-        ggml_set_param(ctx, layer.attention_norm_b);
-        ggml_set_param(ctx, layer.wq_a);
-        ggml_set_param(ctx, layer.wq_b);
-        ggml_set_param(ctx, layer.wk_a);
-        ggml_set_param(ctx, layer.wk_b);
-        ggml_set_param(ctx, layer.wv_a);
-        ggml_set_param(ctx, layer.wv_b);
-        ggml_set_param(ctx, layer.wo_a);
-        ggml_set_param(ctx, layer.wo_b);
-        ggml_set_param(ctx, layer.ffn_norm_a);
-        ggml_set_param(ctx, layer.ffn_norm_b);
-        ggml_set_param(ctx, layer.ffn_gate_a);
-        ggml_set_param(ctx, layer.ffn_gate_b);
-        ggml_set_param(ctx, layer.ffn_down_a);
-        ggml_set_param(ctx, layer.ffn_down_b);
-        ggml_set_param(ctx, layer.ffn_up_a);
-        ggml_set_param(ctx, layer.ffn_up_b);
-    }
-}
-
-static void init_lora(const struct my_llama_model * model, struct my_llama_lora * lora) {
-    const auto & lparams = lora->hparams;
-
-    const uint32_t n_embd     = model->hparams.n_embd;
-    const uint32_t n_embd_gqa = model->hparams.n_embd_gqa();
-    const uint32_t n_layer    = model->hparams.n_layer;
-    const uint32_t n_vocab    = model->hparams.n_vocab;
-    const uint32_t n_ff       = model->hparams.n_ff;
-
-    std::vector<char> tn_buf;
-    tn_buf.resize(GGML_MAX_NAME);
-    auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), "%s%s", key, suffix);
-        return tn_buf.data();
-    };
-    auto tni = [&tn_buf](const char * key, const char * suffix, int bid) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
-        std::string s = tn_buf.data();
-        snprintf(tn_buf.data(), tn_buf.size(), "%s%s", s.c_str(), suffix);
-        return tn_buf.data();
-    };
-
-    // context for lora tensors without their data
-    struct ggml_init_params ctx_lora_params;
-    ctx_lora_params.mem_size   = ggml_tensor_overhead()*2*(6 + n_layer*18);
-    ctx_lora_params.mem_buffer = NULL;
-    ctx_lora_params.no_alloc   = true;
-
-    struct ggml_context * ctx = ggml_init(ctx_lora_params);
-    lora->ctx = ctx;
-
-    lora->tok_embeddings_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_embd);
-    lora->tok_embeddings_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_tok_embeddings, n_vocab);
-    lora->norm_a           = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, n_embd);
-    lora->norm_b           = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_norm, 1);
-    lora->output_a         = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_embd);
-    lora->output_b         = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_output, n_vocab);
-
-    ggml_set_name(lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD,  ".weight.lora_a"));
-    ggml_set_name(lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD,  ".weight.lora_b"));
-    ggml_set_name(lora->norm_a,           tn(LLM_TENSOR_OUTPUT_NORM, ".weight.lora_a"));
-    ggml_set_name(lora->norm_b,           tn(LLM_TENSOR_OUTPUT_NORM, ".weight.lora_b"));
-    ggml_set_name(lora->output_a,         tn(LLM_TENSOR_OUTPUT,      ".weight.lora_a"));
-    ggml_set_name(lora->output_b,         tn(LLM_TENSOR_OUTPUT,      ".weight.lora_b"));
-
-    lora->layers.resize(n_layer);
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = lora->layers[i];
-
-        layer.attention_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, n_embd);
-        layer.attention_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_attention_norm, 1);
-
-        layer.wq_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);
-        layer.wq_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wq, n_embd);
-        layer.wk_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd);
-        layer.wk_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wk, n_embd_gqa);
-        layer.wv_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd);
-        layer.wv_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wv, n_embd_gqa);
-        layer.wo_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);
-        layer.wo_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_wo, n_embd);
-
-        layer.ffn_norm_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, n_embd);
-        layer.ffn_norm_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_norm, 1);
-
-        layer.ffn_gate_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_embd);
-        layer.ffn_gate_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_gate, n_ff);
-        layer.ffn_down_a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_ff);
-        layer.ffn_down_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_down, n_embd);
-        layer.ffn_up_a   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up,   n_embd);
-        layer.ffn_up_b   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, lparams.n_rank_ffn_up,   n_ff);
-
-        ggml_set_name(layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_a", i));
-        ggml_set_name(layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, ".weight.lora_b", i));
-        ggml_set_name(layer.wq_a,             tni(LLM_TENSOR_ATTN_Q,    ".weight.lora_a", i));
-        ggml_set_name(layer.wq_b,             tni(LLM_TENSOR_ATTN_Q,    ".weight.lora_b", i));
-        ggml_set_name(layer.wk_a,             tni(LLM_TENSOR_ATTN_K,    ".weight.lora_a", i));
-        ggml_set_name(layer.wk_b,             tni(LLM_TENSOR_ATTN_K,    ".weight.lora_b", i));
-        ggml_set_name(layer.wv_a,             tni(LLM_TENSOR_ATTN_V,    ".weight.lora_a", i));
-        ggml_set_name(layer.wv_b,             tni(LLM_TENSOR_ATTN_V,    ".weight.lora_b", i));
-        ggml_set_name(layer.wo_a,             tni(LLM_TENSOR_ATTN_OUT,  ".weight.lora_a", i));
-        ggml_set_name(layer.wo_b,             tni(LLM_TENSOR_ATTN_OUT,  ".weight.lora_b", i));
-        ggml_set_name(layer.ffn_norm_a,       tni(LLM_TENSOR_FFN_NORM,  ".weight.lora_a", i));
-        ggml_set_name(layer.ffn_norm_b,       tni(LLM_TENSOR_FFN_NORM,  ".weight.lora_b", i));
-        ggml_set_name(layer.ffn_gate_a,       tni(LLM_TENSOR_FFN_GATE,  ".weight.lora_a", i));
-        ggml_set_name(layer.ffn_gate_b,       tni(LLM_TENSOR_FFN_GATE,  ".weight.lora_b", i));
-        ggml_set_name(layer.ffn_down_a,       tni(LLM_TENSOR_FFN_DOWN,  ".weight.lora_a", i));
-        ggml_set_name(layer.ffn_down_b,       tni(LLM_TENSOR_FFN_DOWN,  ".weight.lora_b", i));
-        ggml_set_name(layer.ffn_up_a,         tni(LLM_TENSOR_FFN_UP,    ".weight.lora_a", i));
-        ggml_set_name(layer.ffn_up_b,         tni(LLM_TENSOR_FFN_UP,    ".weight.lora_b", i));
-    }
-
-    set_param_lora(lora);
-
-    // allocate data for lora tensors
-    lora->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type());
-}
-
-static void randomize_lora(struct my_llama_lora * lora, int seed, float mean, float std, float min, float max) {
-    const uint32_t n_layer = lora->layers.size();
-
-    struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);
-
-    randomize_tensor_normal(lora->tok_embeddings_a, rnd);
-    ggml_set_zero(lora->tok_embeddings_b);
-    randomize_tensor_normal(lora->norm_a,           rnd);
-    ggml_set_zero(lora->norm_b);
-    randomize_tensor_normal(lora->output_a,         rnd);
-    ggml_set_zero(lora->output_b);
-
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = lora->layers[i];
-        randomize_tensor_normal(layer.attention_norm_a, rnd);
-        ggml_set_zero(layer.attention_norm_b);
-
-        randomize_tensor_normal(layer.wq_a, rnd);
-        ggml_set_zero(layer.wq_b);
-        randomize_tensor_normal(layer.wk_a, rnd);
-        ggml_set_zero(layer.wk_b);
-        randomize_tensor_normal(layer.wv_a, rnd);
-        ggml_set_zero(layer.wv_b);
-        randomize_tensor_normal(layer.wo_a, rnd);
-        ggml_set_zero(layer.wo_b);
-
-        randomize_tensor_normal(layer.ffn_norm_a, rnd);
-        ggml_set_zero(layer.ffn_norm_b);
-
-        randomize_tensor_normal(layer.ffn_gate_a, rnd);
-        ggml_set_zero(layer.ffn_gate_b);
-        randomize_tensor_normal(layer.ffn_down_a, rnd);
-        ggml_set_zero(layer.ffn_down_b);
-        randomize_tensor_normal(layer.ffn_up_a, rnd);
-        ggml_set_zero(layer.ffn_up_b);
-    }
-
-    free_random_normal_distribution(rnd);
-}
-
-static struct ggml_tensor * llama_build_lora_finetune_graphs(
-        struct my_llama_model * model,
-        struct my_llama_lora  * lora,
-        ggml_gallocr_t          alloc,
-        struct ggml_context   * ctx,
-        struct ggml_cgraph    * gf,
-        struct ggml_cgraph    * gb,
-        struct ggml_cgraph    * gb_tmp,
-        struct ggml_tensor  * * logits,
-        struct ggml_tensor    * tokens_input,
-        struct ggml_tensor    * targets,
-        const  int              n_tokens,
-        const  int              n_batch,
-        const  bool             enable_flash_attn,
-        const  bool             enable_checkpointing,
-        const  bool             measure_only) {
-
-    ggml_set_scratch(ctx, { 0, 0, nullptr, });
-    const int n_past = 0;
-    const int N = n_tokens;
-    const auto & hparams  = model->hparams;
-    const int n_ctx       = hparams.n_ctx;
-    const int n_vocab     = hparams.n_vocab;
-    const int n_embd      = hparams.n_embd;
-    const int n_layer     = hparams.n_layer;
-    const int n_head      = hparams.n_head;
-    const int n_head_kv   = hparams.n_head_kv;
-    const int n_ff        = hparams.n_ff;
-    const int n_rot       = hparams.n_embd_head();
-    const int n_embd_head = hparams.n_embd_head();
-    const int n_embd_gqa  = hparams.n_embd_gqa();
-
-    const float rms_norm_eps    = hparams.f_norm_rms_eps;
-    const float rope_freq_base  = hparams.rope_freq_base;
-    const float rope_freq_scale = hparams.rope_freq_scale;
-
-    GGML_ASSERT((size_t) n_layer == lora->layers.size());
-
-    auto set_name = [](struct ggml_tensor * t, const char * n) {
-        ggml_set_name(t, n);
-        if (t->grad) {
-            ggml_format_name(t->grad, "%s->grad", n);
-        }
-    };
-
-    // KQ_pos - contains the positions
-    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N);
-    ggml_set_input(KQ_pos);
-
-    // rope has so much parameters that we make a custom function for it
-    auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
-                (struct ggml_tensor * t) -> struct ggml_tensor * {
-        // not capturing these, to silcence warnings
-        const int rope_mode = 0;
-
-        return ggml_rope_ext(ctx,
-            t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx,
-            rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f
-        );
-    };
-
-    set_name(tokens_input, "tokens_input");
-    set_name(targets,      "targets");
-
-    GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);
-
-    auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
-        if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16 || a->type == GGML_TYPE_BF16) {
-            return ggml_add_cast(ctx, a, b, GGML_TYPE_F32);
-        } else if (a->type == GGML_TYPE_F32) {
-            return ggml_add(ctx, a, b);
-        } else {
-            die_fmt("%s: Finetuning on tensors with type '%s' is not yet supported.\n",
-                __func__, ggml_type_name(a->type));
-        }
-    };
-
-    struct ggml_tensor * tok_embeddings = add_to_f32(ctx, model->tok_embeddings, ggml_mul_mat(ctx, lora->tok_embeddings_a, lora->tok_embeddings_b));
-    struct ggml_tensor * norm           = add_to_f32(ctx, model->norm, ggml_mul_mat(ctx, lora->norm_a, lora->norm_b));
-    struct ggml_tensor * output         = add_to_f32(ctx, model->output, ggml_mul_mat(ctx, lora->output_a, lora->output_b));
-
-    struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch);  set_name(t00, "t00"); assert_shape_1d(t00, N*n_batch);
-    struct ggml_tensor * t01 = ggml_get_rows(ctx, tok_embeddings, t00);        set_name(t01, "t01"); assert_shape_2d(t01, n_embd, N*n_batch);
-
-    struct ggml_tensor * cur = t01;
-
-    std::vector<struct ggml_tensor *> checkpoints;
-    if (enable_checkpointing) {
-        checkpoints.push_back(tokens_input);
-        checkpoints.push_back(targets);
-        checkpoints.push_back(t00);
-        checkpoints.push_back(t01);
-    }
-
-    const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head);
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct my_llama_layer & layer = model->layers[il];
-        struct my_llama_lora_layer & llayer = lora->layers[il];
-
-        struct ggml_tensor * attention_norm = add_to_f32(ctx, layer.attention_norm, ggml_mul_mat(ctx, llayer.attention_norm_a, llayer.attention_norm_b));
-        struct ggml_tensor * ffn_norm = add_to_f32(ctx, layer.ffn_norm, ggml_mul_mat(ctx, llayer.ffn_norm_a, llayer.ffn_norm_b));
-        struct ggml_tensor * wq       = add_to_f32(ctx, layer.wq, ggml_mul_mat(ctx, llayer.wq_a, llayer.wq_b));
-        struct ggml_tensor * wk       = add_to_f32(ctx, layer.wk, ggml_mul_mat(ctx, llayer.wk_a, llayer.wk_b));
-        struct ggml_tensor * wv       = add_to_f32(ctx, layer.wv, ggml_mul_mat(ctx, llayer.wv_a, llayer.wv_b));
-        struct ggml_tensor * wo       = add_to_f32(ctx, layer.wo, ggml_mul_mat(ctx, llayer.wo_a, llayer.wo_b));
-        struct ggml_tensor * ffn_gate = add_to_f32(ctx, layer.ffn_gate, ggml_mul_mat(ctx, llayer.ffn_gate_a, llayer.ffn_gate_b));
-        struct ggml_tensor * ffn_down = add_to_f32(ctx, layer.ffn_down, ggml_mul_mat(ctx, llayer.ffn_down_a, llayer.ffn_down_b));
-        struct ggml_tensor * ffn_up   = add_to_f32(ctx, layer.ffn_up, ggml_mul_mat(ctx, llayer.ffn_up_a, llayer.ffn_up_b));
-
-        struct ggml_tensor * t02 = ggml_rms_norm     (ctx, cur, rms_norm_eps);                       set_name(t02, "t02");     assert_shape_2d(t02, n_embd, N*n_batch);
-        struct ggml_tensor * t03 = ggml_repeat       (ctx, attention_norm, t02);                     set_name(t03, "t03");     assert_shape_2d(t03, n_embd, N*n_batch);
-        struct ggml_tensor * t04 = ggml_mul          (ctx, t03, t02);                                set_name(t04, "t04");     assert_shape_2d(t04, n_embd, N*n_batch);
-        struct ggml_tensor * t05 = ggml_mul_mat      (ctx, wq, t04);                                 set_name(t05, "t05");     assert_shape_2d(t05, n_embd, N*n_batch);
-        struct ggml_tensor * t06 = ggml_reshape_4d   (ctx, t05, n_embd_head, n_head, N, n_batch);    set_name(t06, "t06");     assert_shape_4d(t06, n_embd_head, n_head, N, n_batch);
-        struct ggml_tensor * t07 = rope              (t06);                                          set_name(t07, "t07");     assert_shape_4d(t07, n_embd_head, n_head, N, n_batch);
-        struct ggml_tensor * t08 = ggml_mul_mat      (ctx, wk, t04);                                 set_name(t08, "t08");     assert_shape_2d(t08, n_embd_gqa, N*n_batch);
-        struct ggml_tensor * t09 = ggml_reshape_4d   (ctx, t08, n_embd_head, n_head_kv, N, n_batch); set_name(t09, "t09");     assert_shape_4d(t09, n_embd_head, n_head_kv, N, n_batch);
-        struct ggml_tensor * t10 = rope              (t09);                                          set_name(t10, "t10");     assert_shape_4d(t10, n_embd_head, n_head_kv, N, n_batch);
-
-        struct ggml_tensor * t11;
-        if (ggml_is_quantized(wv->type)) {
-            struct ggml_tensor * t11_1 = ggml_mul_mat  (ctx, wv, t04);                               set_name(t11_1, "t11_1"); assert_shape_2d(t11_1, n_embd_gqa, N*n_batch);
-            struct ggml_tensor * t11_2 = ggml_transpose(ctx, t11_1);                                 set_name(t11_2, "t11_2"); assert_shape_2d(t11_2, N*n_batch, n_embd_gqa);
-                                 t11   = ggml_cont     (ctx, t11_2);                                 set_name(t11, "t11");     assert_shape_2d(t11, N*n_batch, n_embd_gqa);
-        } else {
-                                 t11   = ggml_mul_mat  (ctx, t04, wv);                               set_name(t11, "t11");     assert_shape_2d(t11, N*n_batch, n_embd_gqa);
-        }
-
-        struct ggml_tensor * t12 = ggml_reshape_4d   (ctx, t11, N, n_batch, n_embd_head, n_head_kv); set_name(t12, "t12");     assert_shape_4d(t12, N, n_batch, n_embd_head, n_head_kv);
-        struct ggml_tensor * t13 = ggml_permute      (ctx, t07, 0, 2, 1, 3);                         set_name(t13, "t13");     assert_shape_4d(t13, n_embd_head, N, n_head, n_batch);
-        struct ggml_tensor * t14 = ggml_permute      (ctx, t10, 0, 2, 1, 3);                         set_name(t14, "t14");     assert_shape_4d(t14, n_embd_head, N, n_head_kv, n_batch);
-        struct ggml_tensor * t15 = ggml_permute      (ctx, t12, 0, 3, 1, 2);                         set_name(t15, "t15");     assert_shape_4d(t15, N, n_embd_head, n_head_kv, n_batch);
-        struct ggml_tensor * t16;
-        if (enable_flash_attn) {
-            GGML_ASSERT(false && "TODO: ggml_flash_attn_ext() not yet supported");
-            //t16 = ggml_flash_attn(ctx, t13, t14, t15, true);                                         set_name(t16, "t16");     assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);
-        } else {
-            struct ggml_tensor * t16_0 = ggml_mul_mat              (ctx, t14, t13);                  set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_1 = ggml_scale_inplace        (ctx, t16_0, kv_scale);           set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past);             set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_3 = ggml_soft_max_inplace     (ctx, t16_2);                     set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch);
-            t16 = ggml_mul_mat(ctx, t15, t16_3);                                                     set_name(t16, "t16");     assert_shape_4d(t16, n_embd_head, N, n_head, n_batch);
-        }
-        struct ggml_tensor * t17 = ggml_permute      (ctx, t16, 0, 2, 1, 3);                         set_name(t17, "t17");     assert_shape_4d(t17, n_embd_head, n_head, N, n_batch);
-        struct ggml_tensor * t18 = ggml_cont         (ctx, t17);                                     set_name(t18, "t18");     assert_shape_4d(t18, n_embd_head, n_head, N, n_batch);
-        struct ggml_tensor * t19 = ggml_reshape_2d   (ctx, t18, n_embd, N*n_batch);                  set_name(t19, "t19");     assert_shape_2d(t19, n_embd, N*n_batch);
-        struct ggml_tensor * t20 = ggml_mul_mat      (ctx, wo, t19);                                 set_name(t20, "t20");     assert_shape_2d(t20, n_embd, N*n_batch);
-        struct ggml_tensor * t21 = ggml_add          (ctx, t20, cur);                                set_name(t21, "t21");     assert_shape_2d(t21, n_embd, N*n_batch);
-        struct ggml_tensor * t22 = ggml_rms_norm     (ctx, t21, rms_norm_eps);                       set_name(t22, "t22");     assert_shape_2d(t22, n_embd, N*n_batch);
-        struct ggml_tensor * t23 = ggml_repeat       (ctx, ffn_norm, t22);                           set_name(t23, "t23");     assert_shape_2d(t23, n_embd, N*n_batch);
-        struct ggml_tensor * t24 = ggml_mul          (ctx, t23, t22);                                set_name(t24, "t24");     assert_shape_2d(t24, n_embd, N*n_batch);
-        struct ggml_tensor * t25 = ggml_mul_mat      (ctx, ffn_up, t24);                             set_name(t25, "t25");     assert_shape_2d(t25, n_ff, N*n_batch);
-        struct ggml_tensor * t26 = ggml_mul_mat      (ctx, ffn_gate, t24);                           set_name(t26, "t26");     assert_shape_2d(t26, n_ff, N*n_batch);
-        struct ggml_tensor * t27 = ggml_silu         (ctx, t26);                                     set_name(t27, "t27");     assert_shape_2d(t27, n_ff, N*n_batch);
-        struct ggml_tensor * t28 = ggml_mul          (ctx, t27, t25);                                set_name(t28, "t28");     assert_shape_2d(t28, n_ff, N*n_batch);
-        struct ggml_tensor * t29 = ggml_mul_mat      (ctx, ffn_down, t28);                           set_name(t29, "t29");     assert_shape_2d(t29, n_embd, N*n_batch);
-        struct ggml_tensor * t30 = ggml_add          (ctx, t29, t21);                                set_name(t30, "t30");     assert_shape_2d(t30, n_embd, N*n_batch);
-        cur = t30;
-        if (enable_checkpointing) {
-            checkpoints.push_back(cur);
-        }
-    }
-    struct ggml_tensor * t31   = ggml_rms_norm          (ctx, cur, rms_norm_eps);                    set_name(t31, "t31");     assert_shape_2d(t31, n_embd, N*n_batch);
-    struct ggml_tensor * t32   = ggml_repeat            (ctx, norm, t31);                            set_name(t32, "t32");     assert_shape_2d(t32, n_embd, N*n_batch);
-    struct ggml_tensor * t33   = ggml_mul               (ctx, t32, t31);                             set_name(t33, "t33");     assert_shape_2d(t33, n_embd, N*n_batch);
-    struct ggml_tensor * t34   = ggml_mul_mat           (ctx, output, t33);                          set_name(t34, "t34");     assert_shape_2d(t34, n_vocab, N*n_batch);
-    struct ggml_tensor * t35   = ggml_reshape_3d        (ctx, t34, n_vocab, N, n_batch);             set_name(t35, "t35");     assert_shape_3d(t35, n_vocab, N, n_batch);
-    struct ggml_tensor * t36   = ggml_cross_entropy_loss(ctx, t35, targets);                         set_name(t36, "t36");     assert_shape_1d(t36, 1);
-
-    if (enable_checkpointing) {
-        checkpoints.push_back(t31);
-        checkpoints.push_back(t32);
-        checkpoints.push_back(t33);
-        checkpoints.push_back(t34);
-        checkpoints.push_back(t35);
-        checkpoints.push_back(t36);
-    }
-
-    ggml_build_forward_expand(gf, t36);
-
-    if (enable_checkpointing) {
-        ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size());
-    } else {
-        ggml_graph_cpy(gf, gb);
-        ggml_build_backward_expand(ctx, gf, gb, true);
-    }
-
-    GGML_ASSERT(alloc != NULL);
-
-    // make sure some tensors are not reallocated by inserting new temporary nodes depending on them
-    int n_leafs_before = gb->n_leafs;
-    int n_nodes_before = gb->n_nodes;
-
-    // output tensors
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f));
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f));
-    // input gradient
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f));
-    GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
-    ggml_set_input(t36->grad);
-    // KQ_pos
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
-
-    // make sure base model tensors data cannot be used in viewable operations
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->tok_embeddings, 1.0f));
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->norm, 1.0f));
-    ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, model->output, 1.0f));
-    for (int il = 0; il < n_layer; ++il) {
-        struct my_llama_layer & layer = model->layers[il];
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.attention_norm, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_norm, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wq, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wk, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wv, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.wo, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_gate, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_down, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, layer.ffn_up, 1.0f));
-    }
-
-    // allocating checkpoints in one block to reduce memory fragmentation
-    // note: they will be freed in reverse order
-    for (unsigned int i = 0; i < checkpoints.size(); ++i) {
-        if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {
-            ggml_set_input(checkpoints[i]);
-        }
-    }
-
-    if (measure_only) {
-        ggml_gallocr_reserve(alloc, gb);
-    } else {
-        ggml_gallocr_alloc_graph(alloc, gb);
-
-        // set KQ_pos
-        {
-            int * data = (int *) KQ_pos->data;
-            for (int i = 0; i < N; ++i) {
-                data[i] = n_past + i;
-            }
-        }
-    }
-
-    // remove the additional nodes and leafs
-    for (int i = n_leafs_before; i < gb->n_leafs; ++i) {
-        gb->leafs[i] = NULL;
-    }
-    for (int i = n_nodes_before; i < gb->n_nodes; ++i) {
-        gb->nodes[i] = NULL;
-    }
-    gb->n_leafs = n_leafs_before;
-    gb->n_nodes = n_nodes_before;
-
-    *logits = t35;
-    return t36;
-}
-
-static void load_llama_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora) {
-    // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read
-
-    std::string arch;
-
-    std::vector<char> keybuf;
-    keybuf.resize(512);
-
-    GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
-    GGML_ASSERT(arch == "llama");
-
-    uint32_t ftype_u;
-    GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE);
-    GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32);
-
-    struct my_llama_hparams hparams;
-    load_model_hparams_gguf(fctx, &hparams, arch.c_str());
-
-    // parameters that define tensor shapes must match
-    GGML_ASSERT(hparams.n_embd    == model->hparams.n_embd);
-    GGML_ASSERT(hparams.n_ff      == model->hparams.n_ff);
-    GGML_ASSERT(hparams.n_head    == model->hparams.n_head);
-    GGML_ASSERT(hparams.n_head_kv == model->hparams.n_head_kv);
-    GGML_ASSERT(hparams.n_layer   == model->hparams.n_layer);
-
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_tok_embeddings, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_norm,           gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_output,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_OUTPUT);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_attention_norm, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_wq,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_Q);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_wk,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_K);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_wv,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_V);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_wo,             gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_norm,       gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_NORM);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_gate,       gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_GATE);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_down,       gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN);
-    GGUF_GET_KEY(fctx, lora->hparams.n_rank_ffn_up,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_LORA_RANK_FFN_UP);
-
-    init_lora(model, lora);
-
-    copy_tensor_by_name(lora->tok_embeddings_a, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_a));
-    copy_tensor_by_name(lora->tok_embeddings_b, f_ggml_ctx, ggml_get_name(lora->tok_embeddings_b));
-    copy_tensor_by_name(lora->norm_a,           f_ggml_ctx, ggml_get_name(lora->norm_a));
-    copy_tensor_by_name(lora->norm_b,           f_ggml_ctx, ggml_get_name(lora->norm_b));
-    copy_tensor_by_name(lora->output_a,         f_ggml_ctx, ggml_get_name(lora->output_a));
-    copy_tensor_by_name(lora->output_b,         f_ggml_ctx, ggml_get_name(lora->output_b));
-
-    for (uint32_t i = 0; i < lora->layers.size(); ++i) {
-        auto & layer = lora->layers[i];
-        copy_tensor_by_name(layer.attention_norm_a, f_ggml_ctx, ggml_get_name(layer.attention_norm_a));
-        copy_tensor_by_name(layer.attention_norm_b, f_ggml_ctx, ggml_get_name(layer.attention_norm_b));
-        copy_tensor_by_name(layer.wq_a,             f_ggml_ctx, ggml_get_name(layer.wq_a));
-        copy_tensor_by_name(layer.wq_b,             f_ggml_ctx, ggml_get_name(layer.wq_b));
-        copy_tensor_by_name(layer.wk_a,             f_ggml_ctx, ggml_get_name(layer.wk_a));
-        copy_tensor_by_name(layer.wk_b,             f_ggml_ctx, ggml_get_name(layer.wk_b));
-        copy_tensor_by_name(layer.wv_a,             f_ggml_ctx, ggml_get_name(layer.wv_a));
-        copy_tensor_by_name(layer.wv_b,             f_ggml_ctx, ggml_get_name(layer.wv_b));
-        copy_tensor_by_name(layer.wo_a,             f_ggml_ctx, ggml_get_name(layer.wo_a));
-        copy_tensor_by_name(layer.wo_b,             f_ggml_ctx, ggml_get_name(layer.wo_b));
-        copy_tensor_by_name(layer.ffn_norm_a,       f_ggml_ctx, ggml_get_name(layer.ffn_norm_a));
-        copy_tensor_by_name(layer.ffn_norm_b,       f_ggml_ctx, ggml_get_name(layer.ffn_norm_b));
-        copy_tensor_by_name(layer.ffn_gate_a,       f_ggml_ctx, ggml_get_name(layer.ffn_gate_a));
-        copy_tensor_by_name(layer.ffn_gate_b,       f_ggml_ctx, ggml_get_name(layer.ffn_gate_b));
-        copy_tensor_by_name(layer.ffn_down_a,       f_ggml_ctx, ggml_get_name(layer.ffn_down_a));
-        copy_tensor_by_name(layer.ffn_down_b,       f_ggml_ctx, ggml_get_name(layer.ffn_down_b));
-        copy_tensor_by_name(layer.ffn_up_a,         f_ggml_ctx, ggml_get_name(layer.ffn_up_a));
-        copy_tensor_by_name(layer.ffn_up_b,         f_ggml_ctx, ggml_get_name(layer.ffn_up_b));
-    }
-}
-
-static void save_llama_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora) {
-    const char * arch = "llama";
-    enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
-
-    std::vector<char> keybuf;
-    keybuf.resize(512);
-    auto kv = [arch, &keybuf](const char * key) -> const char * {
-        snprintf(keybuf.data(), keybuf.size(), key, arch);
-        return keybuf.data();
-    };
-
-    gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
-    gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);
-
-    gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH),              model->hparams.n_ctx);
-    gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH),            model->hparams.n_embd);
-    gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH),         model->hparams.n_ff);
-    gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT),        model->hparams.n_head);
-    gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV),     model->hparams.n_head_kv);
-    gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT),                 model->hparams.n_layer);
-    gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT),        model->hparams.n_embd_head());
-    gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps);
-    gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE),              model->hparams.rope_freq_base);
-    gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR),           model->hparams.rope_freq_scale);
-
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_TOKEN_EMBD,   lora->hparams.n_rank_tok_embeddings);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT_NORM,  lora->hparams.n_rank_norm);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_OUTPUT,       lora->hparams.n_rank_output);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_NORM,    lora->hparams.n_rank_attention_norm);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_Q,       lora->hparams.n_rank_wq);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_K,       lora->hparams.n_rank_wk);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_V,       lora->hparams.n_rank_wv);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_ATTN_OUT,     lora->hparams.n_rank_wo);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_NORM,     lora->hparams.n_rank_ffn_norm);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_GATE,     lora->hparams.n_rank_ffn_gate);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_DOWN,     lora->hparams.n_rank_ffn_down);
-    gguf_set_val_u32(fctx, LLM_KV_TRAINING_LORA_RANK_FFN_UP,       lora->hparams.n_rank_ffn_up);
-
-    gguf_add_tensor(fctx, lora->tok_embeddings_a);
-    gguf_add_tensor(fctx, lora->tok_embeddings_b);
-    gguf_add_tensor(fctx, lora->norm_a);
-    gguf_add_tensor(fctx, lora->norm_b);
-    gguf_add_tensor(fctx, lora->output_a);
-    gguf_add_tensor(fctx, lora->output_b);
-
-    for (uint32_t i = 0; i < lora->layers.size(); ++i) {
-        auto & layer = lora->layers[i];
-
-        gguf_add_tensor(fctx, layer.attention_norm_a);
-        gguf_add_tensor(fctx, layer.attention_norm_b);
-        gguf_add_tensor(fctx, layer.wq_a);
-        gguf_add_tensor(fctx, layer.wq_b);
-        gguf_add_tensor(fctx, layer.wk_a);
-        gguf_add_tensor(fctx, layer.wk_b);
-        gguf_add_tensor(fctx, layer.wv_a);
-        gguf_add_tensor(fctx, layer.wv_b);
-        gguf_add_tensor(fctx, layer.wo_a);
-        gguf_add_tensor(fctx, layer.wo_b);
-        gguf_add_tensor(fctx, layer.ffn_norm_a);
-        gguf_add_tensor(fctx, layer.ffn_norm_b);
-        gguf_add_tensor(fctx, layer.ffn_gate_a);
-        gguf_add_tensor(fctx, layer.ffn_gate_b);
-        gguf_add_tensor(fctx, layer.ffn_down_a);
-        gguf_add_tensor(fctx, layer.ffn_down_b);
-        gguf_add_tensor(fctx, layer.ffn_up_a);
-        gguf_add_tensor(fctx, layer.ffn_up_b);
-    }
-}
-
-static void load_checkpoint_lora_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
-    std::string train_type = LLM_KV_TRAINING_TYPE_FINETUNE_LORA;
-    GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE);
-    GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
-
-    load_train_state_gguf(fctx, f_ggml_ctx, train);
-    load_llama_lora_gguf(fctx, f_ggml_ctx, model, lora);
-}
-
-static void save_checkpoint_lora_gguf(struct gguf_context * fctx, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
-    gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_FINETUNE_LORA);
-    save_llama_lora_gguf(fctx, model, lora);
-    save_train_state_gguf(fctx, train);
-}
-
-static bool load_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
-    struct ggml_context * f_ggml_ctx;
-    struct gguf_init_params params;
-    params.no_alloc = false;
-    params.ctx = &f_ggml_ctx;
-    struct gguf_context * fctx = gguf_init_from_file(filename, params);
-    if (fctx == NULL) {
-        return false;
-    }
-
-    load_checkpoint_lora_gguf(fctx, f_ggml_ctx, model, lora, train);
-
-    gguf_free(fctx);
-    return true;
-}
-
-static void save_checkpoint_lora_file(const char * filename, struct my_llama_model * model, struct my_llama_lora * lora, struct train_state * train) {
-    printf("%s: saving to %s\n", __func__, filename);
-    struct gguf_context * fctx = gguf_init_empty();
-
-    save_checkpoint_lora_gguf(fctx, model, lora, train);
-
-    // write file
-    const bool only_meta = false;
-    gguf_write_to_file(fctx, filename, only_meta);
-    gguf_free(fctx);
-}
-
-struct llama_file {
-    // use FILE * so we don't have to re-open the file to mmap
-    FILE * fp;
-    size_t size;
-
-    llama_file(const char * fname, const char * mode) {
-        fp = std::fopen(fname, mode);
-        if (fp == NULL) {
-            size = 0;
-        } else {
-            seek(0, SEEK_END);
-            size = tell();
-            seek(0, SEEK_SET);
-        }
-    }
-
-    size_t tell() const {
-#ifdef _WIN32
-        __int64 ret = _ftelli64(fp);
-#else
-        long ret = std::ftell(fp);
-#endif
-        GGML_ASSERT(ret != -1); // this really shouldn't fail
-        return (size_t) ret;
-    }
-
-    void seek(size_t offset, int whence) {
-#ifdef _WIN32
-        int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
-        int ret = std::fseek(fp, (long) offset, whence);
-#endif
-        GGML_ASSERT(ret == 0); // same
-    }
-
-    void read_raw(void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        std::size_t ret = std::fread(ptr, size, 1, fp);
-        if (ferror(fp)) {
-            die_fmt("read error: %s", strerror(errno));
-        }
-        if (ret != 1) {
-            die("unexpectedly reached end of file");
-        }
-    }
-
-    std::uint32_t read_u32() {
-        std::uint32_t ret;
-        read_raw(&ret, sizeof(ret));
-        return ret;
-    }
-
-    std::string read_string(std::uint32_t len) {
-        std::vector<char> chars(len);
-        read_raw(chars.data(), len);
-        return std::string(chars.data(), len);
-    }
-
-    void write_raw(const void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, size, 1, fp);
-        if (ret != 1) {
-            die_fmt("write error: %s", strerror(errno));
-        }
-    }
-
-    void write_u32(std::uint32_t val) {
-        write_raw(&val, sizeof(val));
-    }
-
-    ~llama_file() {
-        if (fp) {
-            std::fclose(fp);
-        }
-    }
-};
-
-static void write_tensor(struct llama_file * file, struct ggml_tensor * tensor, const char * name) {
-    if (tensor == NULL) {
-        file->write_u32(0);
-        file->write_u32(0);
-        file->write_u32(GGML_TYPE_F32);
-        file->seek((0-file->tell()) & 31, SEEK_CUR);
-        return;
-    }
-    if (name == NULL) {
-        name = ggml_get_name(tensor);
-    }
-    uint32_t name_len = strlen(name);
-    uint32_t nd = ggml_n_dims(tensor);
-    uint32_t ne[4] = { (uint32_t)tensor->ne[0],
-                       (uint32_t)tensor->ne[1],
-                       (uint32_t)tensor->ne[2],
-                       (uint32_t)tensor->ne[3] };
-    file->write_u32(nd);
-    file->write_u32(name_len);
-    file->write_u32(tensor->type);
-    file->write_raw(ne, sizeof(ne[0]) * nd);
-    file->write_raw(name, name_len);
-    file->seek((0-file->tell()) & 31, SEEK_CUR);
-    file->write_raw(tensor->data, ggml_nbytes(tensor));
-}
-
-static void save_as_llama_lora(const char * filename, struct my_llama_lora * lora) {
-    printf("%s: saving to %s\n", __func__, filename);
-    struct llama_file file(filename, "wb");
-    if (file.fp == NULL) {
-        return;
-    }
-
-    std::vector<char> tn_buf;
-    tn_buf.resize(GGML_MAX_NAME);
-
-    auto tn = [&tn_buf](const char * key, const char * suffix) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), "%s%s", key, suffix);
-        return tn_buf.data();
-    };
-
-    auto tni = [&tn_buf](const char * key, int bid, const char * suffix) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
-        std::string s = tn_buf.data();
-        snprintf(tn_buf.data(), tn_buf.size(), "%s%s", s.c_str(), suffix);
-        return tn_buf.data();
-    };
-
-    // write_magic
-    file.write_u32(LLAMA_FILE_MAGIC_GGLA);   // magic
-    file.write_u32(1); // version
-    // write_hparams
-    file.write_u32(lora->hparams.lora_r);
-    file.write_u32(lora->hparams.lora_alpha);
-    // write tensors
-    write_tensor(&file, lora->tok_embeddings_a, tn(LLM_TENSOR_TOKEN_EMBD,  ".weight.loraA"));
-    write_tensor(&file, lora->tok_embeddings_b, tn(LLM_TENSOR_TOKEN_EMBD,  ".weight.loraB"));
-    write_tensor(&file, lora->norm_a,           tn(LLM_TENSOR_OUTPUT_NORM, ".weight.loraA"));
-    write_tensor(&file, lora->norm_b,           tn(LLM_TENSOR_OUTPUT_NORM, ".weight.loraB"));
-    write_tensor(&file, lora->output_a,         tn(LLM_TENSOR_OUTPUT,      ".weight.loraA"));
-    write_tensor(&file, lora->output_b,         tn(LLM_TENSOR_OUTPUT,      ".weight.loraB"));
-    for (uint32_t i = 0; i < lora->layers.size(); ++i) {
-        auto & layer = lora->layers[i];
-        write_tensor(&file, layer.attention_norm_a, tni(LLM_TENSOR_ATTN_NORM, i, ".weight.loraA"));
-        write_tensor(&file, layer.attention_norm_b, tni(LLM_TENSOR_ATTN_NORM, i, ".weight.loraB"));
-        write_tensor(&file, layer.wq_a,             tni(LLM_TENSOR_ATTN_Q,    i, ".weight.loraA"));
-        write_tensor(&file, layer.wq_b,             tni(LLM_TENSOR_ATTN_Q,    i, ".weight.loraB"));
-        write_tensor(&file, layer.wk_a,             tni(LLM_TENSOR_ATTN_K,    i, ".weight.loraA"));
-        write_tensor(&file, layer.wk_b,             tni(LLM_TENSOR_ATTN_K,    i, ".weight.loraB"));
-        write_tensor(&file, layer.wv_a,             tni(LLM_TENSOR_ATTN_V,    i, ".weight.loraA"));
-        write_tensor(&file, layer.wv_b,             tni(LLM_TENSOR_ATTN_V,    i, ".weight.loraB"));
-        write_tensor(&file, layer.wo_a,             tni(LLM_TENSOR_ATTN_OUT,  i, ".weight.loraA"));
-        write_tensor(&file, layer.wo_b,             tni(LLM_TENSOR_ATTN_OUT,  i, ".weight.loraB"));
-        write_tensor(&file, layer.ffn_norm_a,       tni(LLM_TENSOR_FFN_NORM,  i, ".weight.loraA"));
-        write_tensor(&file, layer.ffn_norm_b,       tni(LLM_TENSOR_FFN_NORM,  i, ".weight.loraB"));
-        write_tensor(&file, layer.ffn_gate_a,       tni(LLM_TENSOR_FFN_GATE,  i, ".weight.loraA"));
-        write_tensor(&file, layer.ffn_gate_b,       tni(LLM_TENSOR_FFN_GATE,  i, ".weight.loraB"));
-        write_tensor(&file, layer.ffn_down_a,       tni(LLM_TENSOR_FFN_DOWN,  i, ".weight.loraA"));
-        write_tensor(&file, layer.ffn_down_b,       tni(LLM_TENSOR_FFN_DOWN,  i, ".weight.loraB"));
-        write_tensor(&file, layer.ffn_up_a,         tni(LLM_TENSOR_FFN_UP,    i, ".weight.loraA"));
-        write_tensor(&file, layer.ffn_up_b,         tni(LLM_TENSOR_FFN_UP,    i, ".weight.loraB"));
-    }
-}
-
-struct train_params {
-    struct train_params_common common;
-
-    const char * fn_model_base;
-    const char * fn_lora_out;
-
-    bool only_write_lora;
-
-    float f_norm_rms_eps;
-    float rope_freq_base;
-    float rope_freq_scale;
-
-    bool custom_f_norm_rms_eps;
-    bool custom_rope_freq_base;
-    bool custom_rope_freq_scale;
-
-    int32_t lora_r;
-    int32_t lora_alpha;
-    bool custom_lora_alpha;
-
-    uint32_t n_rank_attention_norm;
-    uint32_t n_rank_wq;
-    uint32_t n_rank_wk;
-    uint32_t n_rank_wv;
-    uint32_t n_rank_wo;
-    uint32_t n_rank_ffn_norm;
-    uint32_t n_rank_ffn_gate;
-    uint32_t n_rank_ffn_down;
-    uint32_t n_rank_ffn_up;
-    uint32_t n_rank_tok_embeddings;
-    uint32_t n_rank_norm;
-    uint32_t n_rank_output;
-
-    bool custom_n_rank_attention_norm;
-    bool custom_n_rank_wq;
-    bool custom_n_rank_wk;
-    bool custom_n_rank_wv;
-    bool custom_n_rank_wo;
-    bool custom_n_rank_ffn_norm;
-    bool custom_n_rank_ffn_gate;
-    bool custom_n_rank_ffn_down;
-    bool custom_n_rank_ffn_up;
-    bool custom_n_rank_tok_embeddings;
-    bool custom_n_rank_norm;
-    bool custom_n_rank_output;
-};
-
-static struct train_params get_default_train_params() {
-    struct train_params params;
-    params.common = get_default_train_params_common();
-    params.fn_model_base     = "";
-    params.fn_lora_out       = "ggml-lora-ITERATION-f32.gguf";
-
-    params.only_write_lora = false;
-
-    params.f_norm_rms_eps  = 1e-5f;
-    params.rope_freq_base  = 10000.0f;
-    params.rope_freq_scale = 1.0f;
-
-    params.custom_f_norm_rms_eps  = false;
-    params.custom_rope_freq_base  = false;
-    params.custom_rope_freq_scale = false;
-
-    params.lora_r      = 4;
-    params.lora_alpha  = 4;
-    params.custom_lora_alpha = false;
-
-    params.n_rank_attention_norm = 1;
-    params.n_rank_wq             = 4;
-    params.n_rank_wk             = 4;
-    params.n_rank_wv             = 4;
-    params.n_rank_wo             = 4;
-    params.n_rank_ffn_norm       = 1;
-    params.n_rank_ffn_gate       = 4;
-    params.n_rank_ffn_down       = 4;
-    params.n_rank_ffn_up         = 4;
-    params.n_rank_tok_embeddings = 4;
-    params.n_rank_norm           = 1;
-    params.n_rank_output         = 4;
-
-    params.custom_n_rank_attention_norm = false;
-    params.custom_n_rank_wq             = false;
-    params.custom_n_rank_wk             = false;
-    params.custom_n_rank_wv             = false;
-    params.custom_n_rank_wo             = false;
-    params.custom_n_rank_ffn_norm       = false;
-    params.custom_n_rank_ffn_gate       = false;
-    params.custom_n_rank_ffn_down       = false;
-    params.custom_n_rank_ffn_up         = false;
-    params.custom_n_rank_tok_embeddings = false;
-    params.custom_n_rank_norm           = false;
-    params.custom_n_rank_output         = false;
-
-    return params;
-}
-
-static void train_print_usage(int argc, char ** argv, const struct train_params * params) {
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h, --help                 show this help message and exit\n");
-
-    fprintf(stderr, "  --model-base FNAME         model path from which to load base model (default '%s')\n", params->fn_model_base);
-    fprintf(stderr, "  --lora-out FNAME           path to save llama lora (default '%s')\n", params->fn_lora_out);
-    fprintf(stderr, "  --only-write-lora          only save llama lora, don't do any training.  use this if you only want to convert a checkpoint to a lora adapter.\n");
-    fprintf(stderr, "  --norm-rms-eps F           RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps);
-    fprintf(stderr, "  --rope-freq-base F         Frequency base for ROPE (default %f)\n", params->rope_freq_base);
-    fprintf(stderr, "  --rope-freq-scale F        Frequency scale for ROPE (default %f)\n", params->rope_freq_scale);
-    fprintf(stderr, "  --lora-alpha N             LORA alpha : resulting LORA scaling is alpha/r. (default %d)\n", params->lora_alpha);
-    fprintf(stderr, "  --lora-r N                 LORA r: default rank. Also specifies resulting scaling together with lora-alpha. (default %d)\n", params->lora_r);
-    fprintf(stderr, "  --rank-att-norm N          LORA rank for attention norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
-    fprintf(stderr, "  --rank-ffn-norm N          LORA rank for feed-forward norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
-    fprintf(stderr, "  --rank-out-norm N          LORA rank for output norm tensor, overrides default rank. Norm tensors should generally have rank 1.\n");
-    fprintf(stderr, "  --rank-tok-embd N          LORA rank for token embeddings tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-out N               LORA rank for output tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-wq N                LORA rank for wq tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-wk N                LORA rank for wk tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-wv N                LORA rank for wv tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-wo N                LORA rank for wo tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-ffn_gate N          LORA rank for ffn_gate tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-ffn_down N          LORA rank for ffn_down tensor, overrides default rank.\n");
-    fprintf(stderr, "  --rank-ffn_up N            LORA rank for ffn_up tensor, overrides default rank.\n");
-
-    print_common_train_usage(argc, argv, &params->common);
-}
-
-static bool train_params_parse(int argc, char ** argv, struct train_params * params) {
-    bool invalid_param = false;
-    std::string arg;
-    struct train_params default_params = get_default_train_params();
-    const std::string arg_prefix = "--";
-
-    for (int i = 1; i < argc; i++) {
-        arg = argv[i];
-        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-            std::replace(arg.begin(), arg.end(), '_', '-');
-        }
-
-        if (consume_common_train_arg(argc, argv, &i, &params->common, &invalid_param)) {
-            if (invalid_param) {
-                break;
-            } else if (params->common.print_usage) {
-                train_print_usage(argc, argv, &default_params);
-                exit(0);
-            }
-        } else if (arg == "--model-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_model_base = argv[i];
-        } else if (arg == "--lora-out") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_lora_out = argv[i];
-        } else if (arg == "--only-write-lora") {
-            params->only_write_lora = true;
-        } else if (arg == "--norm-rms-eps") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->f_norm_rms_eps = std::stof(argv[i]);
-            params->custom_f_norm_rms_eps = true;
-        } else if (arg == "--rope-freq-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->rope_freq_base = std::stof(argv[i]);
-            params->custom_rope_freq_base = true;
-        } else if (arg == "--rope-freq-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->rope_freq_scale = std::stof(argv[i]);
-            params->custom_rope_freq_scale = true;
-        } else if (arg == "--lora-alpha") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->lora_alpha = std::stoi(argv[i]);
-            params->custom_lora_alpha = true;
-        } else if (arg == "--lora-r") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->lora_r = std::stoi(argv[i]);
-        } else if (arg == "--rank-att-norm") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_attention_norm = std::stoi(argv[i]);
-            params->custom_n_rank_attention_norm = true;
-        } else if (arg == "--rank-ffn-norm") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_ffn_norm = std::stoi(argv[i]);
-            params->custom_n_rank_ffn_norm = true;
-        } else if (arg == "--rank-out-norm") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_norm = std::stoi(argv[i]);
-            params->custom_n_rank_norm = true;
-        } else if (arg == "--rank-tok-embd") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_tok_embeddings = std::stoi(argv[i]);
-            params->custom_n_rank_tok_embeddings = true;
-        } else if (arg == "--rank-out") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_output = std::stoi(argv[i]);
-            params->custom_n_rank_output = true;
-        } else if (arg == "--rank-wq") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_wq = std::stoi(argv[i]);
-            params->custom_n_rank_wq = true;
-        } else if (arg == "--rank-wk") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_wk = std::stoi(argv[i]);
-            params->custom_n_rank_wk = true;
-        } else if (arg == "--rank-wv") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_wv = std::stoi(argv[i]);
-            params->custom_n_rank_wv = true;
-        } else if (arg == "--rank-wo") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_wo = std::stoi(argv[i]);
-            params->custom_n_rank_wo = true;
-        } else if (arg == "--rank-ffn_gate") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_ffn_gate = std::stoi(argv[i]);
-            params->custom_n_rank_ffn_gate = true;
-        } else if (arg == "--rank-ffn_down") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_ffn_down = std::stoi(argv[i]);
-            params->custom_n_rank_ffn_down = true;
-        } else if (arg == "--rank-ffn_up") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_rank_ffn_up = std::stoi(argv[i]);
-            params->custom_n_rank_ffn_up = true;
-        } else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            train_print_usage(argc, argv, &default_params);
-            exit(1);
-        }
-    }
-    if (invalid_param) {
-        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
-        train_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    finish_processing_train_args(&params->common);
-    return true;
-}
-
-struct save_train_files_data {
-    const char            * fn_checkpoint_out;
-    const char            * fn_lora_out;
-    const char            * pattern_fn_it;
-    const char            * fn_latest;
-    struct my_llama_model * model;
-    struct my_llama_lora  * lora;
-};
-
-static void save_train_files(void * vdata, struct train_state * train) {
-    struct save_train_files_data * data   = (struct save_train_files_data *) vdata;
-
-    int64_t iter = train->opt->iter;
-
-    if (strlen(data->fn_checkpoint_out) > 0) {
-        save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->model, data->lora, train);
-        save_checkpoint_lora_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, -1  ).c_str(), data->model, data->lora, train);
-    }
-    if (strlen(data->fn_lora_out) > 0) {
-        save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->lora);
-        save_as_llama_lora(get_train_filename(data->fn_lora_out, data->pattern_fn_it, data->fn_latest, -1  ).c_str(), data->lora);
-    }
-}
-
-static int64_t get_parameter_count(struct my_llama_lora* lora) {
-    int64_t nx = 0;
-    nx += ggml_nelements(lora->tok_embeddings_a);
-    nx += ggml_nelements(lora->tok_embeddings_b);
-    nx += ggml_nelements(lora->norm_a);
-    nx += ggml_nelements(lora->norm_b);
-    nx += ggml_nelements(lora->output_a);
-    nx += ggml_nelements(lora->output_b);
-
-    for (uint32_t i = 0; i < lora->layers.size(); ++i) {
-        auto & layer = lora->layers[i];
-        nx += ggml_nelements(layer.attention_norm_a);
-        nx += ggml_nelements(layer.attention_norm_b);
-        nx += ggml_nelements(layer.wq_a);
-        nx += ggml_nelements(layer.wq_b);
-        nx += ggml_nelements(layer.wk_a);
-        nx += ggml_nelements(layer.wk_b);
-        nx += ggml_nelements(layer.wv_a);
-        nx += ggml_nelements(layer.wv_b);
-        nx += ggml_nelements(layer.wo_a);
-        nx += ggml_nelements(layer.wo_b);
-        nx += ggml_nelements(layer.ffn_norm_a);
-        nx += ggml_nelements(layer.ffn_norm_b);
-        nx += ggml_nelements(layer.ffn_gate_a);
-        nx += ggml_nelements(layer.ffn_gate_b);
-        nx += ggml_nelements(layer.ffn_down_a);
-        nx += ggml_nelements(layer.ffn_down_b);
-        nx += ggml_nelements(layer.ffn_up_a);
-        nx += ggml_nelements(layer.ffn_up_b);
-    }
-    return nx;
-}
-
-int main(int argc, char ** argv) {
-    struct train_params params = get_default_train_params();
-
-    if (!train_params_parse(argc, argv, &params)) {
-        return 1;
-    }
-
-    if (params.common.seed == LLAMA_DEFAULT_SEED) {
-        params.common.seed = time(NULL);
-    }
-    printf("%s: seed: %u\n", __func__, params.common.seed);
-    srand(params.common.seed);
-
-    struct llama_model_params llama_mparams = llama_model_default_params();
-    llama_mparams.n_gpu_layers = params.common.n_gpu_layers;
-    llama_mparams.vocab_only = false;
-
-    printf("%s: model base = '%s'\n", __func__, params.fn_model_base);
-    struct llama_model * lmodel = llama_load_model_from_file(params.fn_model_base, llama_mparams);
-
-    struct llama_context_params llama_cparams = llama_context_default_params();
-    struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_cparams);
-
-    struct my_llama_model model;
-    init_model(lmodel, &model, params.fn_model_base, params.common.n_ctx);
-
-    struct my_llama_lora lora;
-
-    struct train_state      * train = init_train_state();
-    struct ggml_opt_context * opt   = train->opt;
-
-    // set params from command line
-    if (params.custom_f_norm_rms_eps) {
-        model.hparams.f_norm_rms_eps  = params.f_norm_rms_eps;
-    }
-    if (params.custom_rope_freq_base) {
-        model.hparams.rope_freq_base  = params.rope_freq_base;
-    }
-    if (params.custom_rope_freq_scale) {
-        model.hparams.rope_freq_scale = params.rope_freq_scale;
-    }
-    lora.hparams.lora_r                = params.lora_r;
-    lora.hparams.lora_alpha            = params.custom_lora_alpha            ? params.lora_alpha            : params.lora_r;
-    uint32_t n_rank_attention_norm     = params.custom_n_rank_attention_norm ? params.n_rank_attention_norm : 1;
-    uint32_t n_rank_wq                 = params.custom_n_rank_wq             ? params.n_rank_wq             : params.lora_r;
-    uint32_t n_rank_wk                 = params.custom_n_rank_wk             ? params.n_rank_wk             : params.lora_r;
-    uint32_t n_rank_wv                 = params.custom_n_rank_wv             ? params.n_rank_wv             : params.lora_r;
-    uint32_t n_rank_wo                 = params.custom_n_rank_wo             ? params.n_rank_wo             : params.lora_r;
-    uint32_t n_rank_ffn_norm           = params.custom_n_rank_ffn_norm       ? params.n_rank_ffn_norm       : 1;
-    uint32_t n_rank_ffn_gate           = params.custom_n_rank_ffn_gate       ? params.n_rank_ffn_gate       : params.lora_r;
-    uint32_t n_rank_ffn_down           = params.custom_n_rank_ffn_down       ? params.n_rank_ffn_down       : params.lora_r;
-    uint32_t n_rank_ffn_up             = params.custom_n_rank_ffn_up         ? params.n_rank_ffn_up         : params.lora_r;
-    uint32_t n_rank_tok_embeddings     = params.custom_n_rank_tok_embeddings ? params.n_rank_tok_embeddings : params.lora_r;
-    uint32_t n_rank_norm               = params.custom_n_rank_norm           ? params.n_rank_norm           : 1;
-    uint32_t n_rank_output             = params.custom_n_rank_output         ? params.n_rank_output         : params.lora_r;
-    lora.hparams.n_rank_attention_norm = n_rank_attention_norm;
-    lora.hparams.n_rank_wq             = n_rank_wq;
-    lora.hparams.n_rank_wk             = n_rank_wk;
-    lora.hparams.n_rank_wv             = n_rank_wv;
-    lora.hparams.n_rank_wo             = n_rank_wo;
-    lora.hparams.n_rank_ffn_norm       = n_rank_ffn_norm;
-    lora.hparams.n_rank_ffn_gate       = n_rank_ffn_gate;
-    lora.hparams.n_rank_ffn_down       = n_rank_ffn_down;
-    lora.hparams.n_rank_ffn_up         = n_rank_ffn_up;
-    lora.hparams.n_rank_tok_embeddings = n_rank_tok_embeddings;
-    lora.hparams.n_rank_norm           = n_rank_norm;
-    lora.hparams.n_rank_output         = n_rank_output;
-
-    // set opt params from command line
-    opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
-    opt->params.print_forward_graph     = false;
-    opt->params.print_backward_graph    = false;
-    opt->params.graph_size              = LLAMA_TRAIN_MAX_NODES;
-    opt->params.n_threads               = params.common.n_threads;
-    opt->params.past                    = params.common.opt_past;
-    opt->params.delta                   = params.common.opt_delta;
-    opt->params.max_no_improvement      = params.common.opt_max_no_improvement;
-    opt->params.n_gradient_accumulation = params.common.n_gradient_accumulation;
-    opt->params.adam.n_iter             = params.common.adam_n_iter;
-    opt->params.adam.sched              = 1.0f;
-    opt->params.adam.alpha              = params.common.adam_alpha;
-    opt->params.adam.decay              = params.common.adam_decay;
-    opt->params.adam.decay_min_ndim     = params.common.adam_decay_min_ndim;
-    opt->params.adam.beta1              = params.common.adam_beta1;
-    opt->params.adam.beta2              = params.common.adam_beta2;
-    opt->params.adam.gclip              = params.common.adam_gclip;
-    opt->params.adam.eps_f              = params.common.adam_eps_f;
-
-    printf("%s: init model\n", __func__);
-    bool existed = load_checkpoint_lora_file(params.common.fn_checkpoint_in, &model, &lora, train);
-
-    if (existed) {
-        // overwrite last n_ctx with user provided n_ctx
-        if (params.common.custom_n_ctx) {
-            model.hparams.n_ctx = params.common.n_ctx;
-        }
-
-        const bool opt_param_count_changed = (
-           (lora.hparams.n_rank_attention_norm != n_rank_attention_norm)
-        || (lora.hparams.n_rank_wq             != n_rank_wq)
-        || (lora.hparams.n_rank_wk             != n_rank_wk)
-        || (lora.hparams.n_rank_wv             != n_rank_wv)
-        || (lora.hparams.n_rank_wo             != n_rank_wo)
-        || (lora.hparams.n_rank_ffn_norm       != n_rank_ffn_norm)
-        || (lora.hparams.n_rank_ffn_gate       != n_rank_ffn_gate)
-        || (lora.hparams.n_rank_ffn_down       != n_rank_ffn_down)
-        || (lora.hparams.n_rank_ffn_up         != n_rank_ffn_up)
-        || (lora.hparams.n_rank_tok_embeddings != n_rank_tok_embeddings)
-        || (lora.hparams.n_rank_norm           != n_rank_norm)
-        || (lora.hparams.n_rank_output         != n_rank_output)
-        );
-
-        const bool opt_past_changed = opt->params.past != params.common.opt_past;
-
-        if (opt_param_count_changed) {
-            print_lora_params(&lora.hparams);
-            die("Provided rank differs from checkpoint file. To use different rank start finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting.");
-            // need to discard previous optimizer gradient statistics and opt_init with new shapes
-            // TODO
-        }
-        if (opt_past_changed) {
-            die("Optimizer parameter '--opt-past N' differs from checkpoint file. To use different value finetune from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting");
-            // need to discard previous optimizer past function value statistics and opt_init with new shapes
-            // TODO
-        }
-    } else { // existed == false
-        init_lora(&model, &lora);
-        randomize_lora(&lora, params.common.seed, 0.0f, 1.0f, -1.0f, +1.0f);
-        if (!params.only_write_lora) {
-            ggml_opt_init(opt->ctx, opt, opt->params, get_parameter_count(&lora));
-        }
-    }
-    opt->iter = train->train_its;
-
-    print_params(&model.hparams);
-    print_lora_params(&lora.hparams);
-    printf("%s: total train_iterations %llu\n", __func__, (long long unsigned) train->train_its);
-    printf("%s: seen train_samples     %llu\n", __func__, (long long unsigned) train->train_samples);
-    printf("%s: seen train_tokens      %llu\n", __func__, (long long unsigned) train->train_tokens);
-    printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs);
-    printf("%s: lora_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)), (float) (ggml_used_mem(lora.ctx) + ggml_backend_buffer_get_size(lora.data)) / (1024.0f*1024.0f));
-
-    if (params.only_write_lora) {
-        save_train_files_data save_data;
-        save_data.fn_checkpoint_out = "";
-        save_data.fn_lora_out       = params.fn_lora_out;
-        save_data.pattern_fn_it     = params.common.pattern_fn_it;
-        save_data.fn_latest         = params.common.fn_latest;
-        save_data.model             = &model;
-        save_data.lora              = &lora;
-
-        save_train_files(&save_data, train);
-
-        free_train_state(train);
-        ggml_free(lora.ctx);
-        llama_free(lctx);
-        llama_free_model(lmodel);
-        return 0;
-    }
-
-    printf("%s: opt_size  = %zu bytes (%.1f MB)\n", __func__, ggml_get_mem_size(opt->ctx), (float) ggml_get_mem_size(opt->ctx) / (1024.0f*1024.0f));
-    printf("%s: opt iter %d\n", __func__, opt->iter);
-
-    int n_tokens = model.hparams.n_ctx;
-    int n_vocab  = model.hparams.n_vocab;
-    int n_batch  = params.common.n_batch;
-
-    // context for input tensors without their data
-    struct ggml_init_params ctx_input_params = {
-        ggml_tensor_overhead() * 2, // mem_size
-        NULL,                       // mem_buffer
-        true,                       // no_alloc
-    };
-    struct ggml_context * ctx_input = ggml_init(ctx_input_params);
-
-    // the input tensors
-    struct ggml_tensor * tokens_input  = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch);
-    struct ggml_tensor * target_probs  = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
-
-    // allocate input tensors
-    // measure required memory for input tensors
-    ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type());
-    size_t max_input_size = ggml_backend_buffer_get_size(input_data);
-    printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
-
-    // context for compute tensors without their data
-    const size_t estimated_compute_size_wo_data = (
-            2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() +
-            (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true))
-    );
-    struct ggml_init_params ctx_compute_params = {
-        estimated_compute_size_wo_data, // mem_size
-        NULL,                           // mem_buffer
-        true,                           // no_alloc
-    };
-    struct ggml_context * ctx_compute = NULL;
-
-    struct ggml_tensor * loss   = NULL;
-    struct ggml_tensor * logits = NULL;
-
-    struct ggml_cgraph * gf     = NULL;
-    struct ggml_cgraph * gb     = NULL;
-    struct ggml_cgraph * gb_tmp = NULL;
-
-    // measure required memory for compute tensors
-    size_t best_compute_size = SIZE_MAX;
-    enum ggml_cgraph_eval_order best_order = GGML_CGRAPH_EVAL_ORDER_COUNT;
-    // find best evaluation order
-    for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
-        ctx_compute = ggml_init(ctx_compute_params);
-        ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-        gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-        gf->order = (enum ggml_cgraph_eval_order) order;
-        gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-        gb_tmp = params.common.use_checkpointing
-            ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
-            : NULL;
-        loss = llama_build_lora_finetune_graphs(
-            &model, &lora, alloc, ctx_compute,
-            gf, gb, gb_tmp,
-            &logits, tokens_input, target_probs,
-            n_tokens, n_batch,
-            params.common.use_flash,
-            params.common.use_checkpointing,
-            true
-        );
-        size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer
-        if (max_compute_size < best_compute_size) {
-            best_compute_size = max_compute_size;
-            best_order = gf->order;
-        }
-        ggml_gallocr_free(alloc);
-        ggml_free(ctx_compute);
-    }
-    size_t max_compute_size = best_compute_size;
-    printf("%s: compute_size = %zu bytes (%.1f MB)\n", __func__, max_compute_size, (float) max_compute_size / (1024.0f*1024.0f));
-    printf("%s: evaluation order = %s\n", __func__,
-        (best_order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? "LEFT_TO_RIGHT" :
-        (best_order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? "RIGHT_TO_LEFT" :
-        "invalid");
-
-    // allocate compute tensors
-    ctx_compute = ggml_init(ctx_compute_params);
-    ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-    gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-    gf->order = best_order;
-    gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-    gb_tmp = params.common.use_checkpointing
-        ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
-        : NULL;
-    loss = llama_build_lora_finetune_graphs(
-        &model, &lora, alloc, ctx_compute,
-        gf, gb, gb_tmp,
-        &logits, tokens_input, target_probs,
-        n_tokens, n_batch,
-        params.common.use_flash,
-        params.common.use_checkpointing,
-        false
-    );
-
-    // tokenize data
-    std::vector<llama_token> train_tokens;
-    std::vector<size_t> train_samples_begin;
-    std::vector<size_t> train_samples_size;
-    printf("%s: tokenize training data from %s\n", __func__, params.common.fn_train_data);
-    printf("%s: sample-start: %s\n", __func__, params.common.sample_start.c_str());
-    printf("%s: include-sample-start: %s\n", __func__, params.common.include_sample_start ? "true" : "false");
-    tokenize_file(lctx,
-            params.common.fn_train_data,
-            params.common.sample_start,
-            params.common.include_sample_start,
-            params.common.overlapping_samples,
-            n_tokens,
-            train_tokens,
-            train_samples_begin,
-            train_samples_size);
-    GGML_ASSERT(train_samples_begin.size() == train_samples_size.size());
-
-    printf("%s: number of training tokens: %zu\n", __func__, train_tokens.size());
-
-    std::vector<size_t> token_noccurs;
-    token_noccurs.resize(model.hparams.n_vocab, 0);
-    for (unsigned int i = 0; i < train_tokens.size(); ++i) {
-        ++token_noccurs[train_tokens[i]];
-    }
-    int n_unique_tokens = 0;
-    for (unsigned int i = 0; i < token_noccurs.size(); ++i) {
-        if (token_noccurs[i] == 0) continue;
-        ++n_unique_tokens;
-    }
-    printf("%s: number of unique tokens: %d\n", __func__, n_unique_tokens);
-
-    size_t shuffle_samples_hash = compute_samples_hash(params.common.fn_train_data, train_samples_begin.data(), train_samples_size.data(), train_samples_size.size());
-    const bool changed_train_data = (shuffle_samples_hash != train->shuffle_samples_hash) || (train->shuffle_sample_count != train_samples_size.size());
-    if (changed_train_data) {
-        printf("%s: train data seems to have changed. restarting shuffled epoch.\n", __func__);
-    }
-    if (params.common.force_reshuffle) {
-        printf("%s: forced reshuffling of data. restarting with newly shuffled epoch.\n", __func__);
-    }
-    if ((train->shuffle_rng_state_current == "") || changed_train_data || params.common.force_reshuffle) {
-        train->shuffle_rng_state_current = mt19937_seed_to_state(params.common.seed);
-        train->shuffle_sample_count = train_samples_size.size();
-        train->shuffle_next_sample = 0;
-        train->shuffle_samples_hash = shuffle_samples_hash;
-    }
-    std::vector<size_t> train_shuffled_samples_offs;
-    std::vector<size_t> train_shuffled_samples_begin;
-    std::vector<size_t> train_shuffled_samples_size;
-    train_shuffled_samples_offs.resize(train_samples_begin.size());
-    train_shuffled_samples_begin.resize(train_samples_begin.size());
-    train_shuffled_samples_size.resize(train_samples_size.size());
-    train->shuffle_rng_state_next = shuffle_samples(
-        train->shuffle_rng_state_current,
-        train_shuffled_samples_offs.data(),
-        train_shuffled_samples_begin.data(),
-        train_shuffled_samples_size.data(),
-        train_samples_begin.data(),
-        train_samples_size.data(),
-        train_samples_size.size());
-
-    printf("%s: begin training\n", __func__);
-
-    save_train_files_data save_data;
-    save_data.fn_checkpoint_out = params.common.fn_checkpoint_out;
-    save_data.fn_lora_out       = params.fn_lora_out;
-    save_data.pattern_fn_it     = params.common.pattern_fn_it;
-    save_data.fn_latest         = params.common.fn_latest;
-    save_data.model             = &model;
-    save_data.lora              = &lora;
-
-    struct train_opt_callback_data opt_cb_data;
-    opt_cb_data.params                 = &params.common;
-    opt_cb_data.train                  = train;
-    opt_cb_data.save_cb                = &save_train_files;
-    opt_cb_data.save_data              = &save_data;
-    opt_cb_data.lctx                   = lctx;
-    opt_cb_data.last_save_iter         = opt->iter;
-    opt_cb_data.tokens_data            = train_tokens.data();
-    opt_cb_data.tokens_size            = train_tokens.size();
-    opt_cb_data.samples_begin          = train_samples_begin.data();
-    opt_cb_data.samples_size           = train_samples_size.data();
-    opt_cb_data.shuffled_samples_offs  = train_shuffled_samples_offs.data();
-    opt_cb_data.shuffled_samples_begin = train_shuffled_samples_begin.data();
-    opt_cb_data.shuffled_samples_size  = train_shuffled_samples_size.data();
-    opt_cb_data.samples_count          = train_samples_size.size();
-    opt_cb_data.tokens_input           = tokens_input;
-    opt_cb_data.target_probs           = target_probs;
-    opt_cb_data.first_iter             = opt->iter;
-    opt_cb_data.first_epoch            = train->train_epochs;
-    opt_cb_data.iter_at_last_epoch     = -1;
-    opt_cb_data.last_time              = ggml_time_ms();
-    opt_cb_data.millis_per_iter        = 0.0;
-
-    // measure required memory for work buffer
-    size_t max_work_size = ggml_graph_plan(gb, params.common.n_threads).work_size + GGML_OBJECT_SIZE;
-    printf("%s: work_size = %zu bytes (%.1f MB)\n", __func__, max_work_size, (float) max_work_size / (1024.0f*1024.0f));
-
-    // context for work buffer
-    struct ggml_init_params ctx_work_params = {
-        max_work_size, // mem_size
-        NULL,          // mem_buffer
-        false,         // no_alloc
-    };
-    struct ggml_context * ctx_work = ggml_init(ctx_work_params);
-
-    int64_t t0 = ggml_time_ms();
-
-    ggml_opt_resume_g(ctx_work, opt, loss, gf, gb, &train_opt_callback, (void *) &opt_cb_data);
-
-    ggml_free(ctx_work);
-    ggml_free(ctx_compute);
-    ggml_free(ctx_input);
-    ggml_gallocr_free(alloc);
-
-
-    int64_t t1 = ggml_time_ms();
-    printf("%s: total training time: ", __func__);
-    print_duration((double) (t1 - t0));
-    printf("\n");
-
-    int new_iters = opt->iter - opt_cb_data.last_save_iter;
-    if (new_iters > 0) {
-        train->train_its     += new_iters;
-        train->train_tokens  += new_iters * opt->params.n_gradient_accumulation * n_batch * n_tokens;
-
-        save_train_files(&save_data, train);
-        opt_cb_data.last_save_iter = opt->iter;
-    }
-
-    ggml_free(opt->ctx);
-    free_train_state(train);
-    ggml_free(lora.ctx);
-    llama_free(lctx);
-    llama_free_model(lmodel);
-    return 0;
-}
diff --git a/examples/finetune/finetune.sh b/examples/finetune/finetune.sh
deleted file mode 100644
index e3cc7f2712f17e..00000000000000
--- a/examples/finetune/finetune.sh
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/bin/bash
-cd `dirname $0`
-cd ../..
-
-EXE="./llama-finetune"
-
-if [[ ! $LLAMA_MODEL_DIR ]]; then LLAMA_MODEL_DIR="./models"; fi
-if [[ ! $LLAMA_TRAINING_DIR ]]; then LLAMA_TRAINING_DIR="."; fi
-
-# MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2-q8_0.gguf" # This is the model the readme uses.
-MODEL="$LLAMA_MODEL_DIR/openllama-3b-v2.gguf" # An f16 model. Note in this case with "-g", you get an f32-format .BIN file that isn't yet supported if you use it with "llama-cli --lora" with GPU inferencing.
-
-while getopts "dg" opt; do
-  case $opt in
-    d)
-      DEBUGGER="gdb --args"
-      ;;
-    g)
-      EXE="./build/bin/Release/finetune"
-      GPUARG="--gpu-layers 25"
-      ;;
-  esac
-done
-
-$DEBUGGER $EXE \
-        --model-base $MODEL \
-        $GPUARG \
-        --checkpoint-in  chk-ol3b-shakespeare-LATEST.gguf \
-        --checkpoint-out chk-ol3b-shakespeare-ITERATION.gguf \
-        --lora-out lora-ol3b-shakespeare-ITERATION.bin \
-        --train-data "$LLAMA_TRAINING_DIR\shakespeare.txt" \
-        --save-every 10 \
-        --threads 10 --adam-iter 30 --batch 4 --ctx 64 \
-        --use-checkpointing
diff --git a/examples/train-text-from-scratch/CMakeLists.txt b/examples/train-text-from-scratch/CMakeLists.txt
deleted file mode 100644
index 9a1d2a35e77315..00000000000000
--- a/examples/train-text-from-scratch/CMakeLists.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-set(TARGET llama-train-text-from-scratch)
-add_executable(${TARGET} train-text-from-scratch.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/examples/train-text-from-scratch/README.md b/examples/train-text-from-scratch/README.md
deleted file mode 100644
index 3abae238036dc1..00000000000000
--- a/examples/train-text-from-scratch/README.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# train-text-from-scratch
-
-Basic usage instructions:
-
-```bash
-# get training data
-wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt
-
-# train
-./bin/llama-train-text-from-scratch \
-        --vocab-model ../models/ggml-vocab-llama.gguf \
-        --ctx 64 --embd 256 --head 8 --layer 16 \
-        --checkpoint-in  chk-shakespeare-256x16-LATEST.gguf \
-        --checkpoint-out chk-shakespeare-256x16-ITERATION.gguf \
-        --model-out ggml-shakespeare-256x16-f32-ITERATION.gguf \
-        --train-data "shakespeare.txt" \
-        -t 6 -b 16 --seed 1 --adam-iter 256 \
-        --no-checkpointing
-
-# predict
-./bin/llama-cli -m ggml-shakespeare-256x16-f32.gguf
-```
-
-Output files will be saved every N iterations (config with `--save-every N`).
-The pattern "ITERATION" in the output filenames will be replaced with the iteration number and "LATEST" for the latest output.
-
-To train GGUF models just pass them to `--checkpoint-in FN`.
diff --git a/examples/train-text-from-scratch/convert_train_checkpoint_to_gguf.py b/examples/train-text-from-scratch/convert_train_checkpoint_to_gguf.py
deleted file mode 100644
index e045beb7281fd3..00000000000000
--- a/examples/train-text-from-scratch/convert_train_checkpoint_to_gguf.py
+++ /dev/null
@@ -1,499 +0,0 @@
-#!/usr/bin/env python3
-# train-text-from-scratch checkpoint --> gguf conversion
-
-import argparse
-import os
-import struct
-import sys
-import numpy as np
-from pathlib import Path
-
-if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py'))
-import gguf
-
-# gguf constants
-LLM_KV_OPTIMIZER_TYPE = "optimizer.type"
-LLM_KV_OPTIMIZER_TYPE_ADAM  = "adam"
-LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"
-LLM_KV_OPTIMIZER_FILE_VERSION               = "optimizer.file_version"
-LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT     = "optimizer.convergence_past_count"
-LLM_KV_OPTIMIZER_PARAMETER_COUNT            = "optimizer.parameter_count"
-LLM_KV_OPTIMIZER_ITERATION_COUNT            = "optimizer.iteration_count"
-LLM_KV_OPTIMIZER_JUST_INITIALIZED           = "optimizer.just_initialized"
-LLM_KV_OPTIMIZER_ADAM_BEST_LOSS             = "optimizer.adam.best_loss"
-LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS         = "optimizer.adam.previous_loss"
-LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT  = "optimizer.adam.no_improvement_count"
-LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"
-LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS            = "optimizer.lbfgs.best_loss"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP     = "optimizer.lbfgs.line_search_step"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J        = "optimizer.lbfgs.line_search_j"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K        = "optimizer.lbfgs.line_search_k"
-LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END      = "optimizer.lbfgs.line_search_end"
-LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"
-
-LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS    = "optimizer.adam.first_moments"
-LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS   = "optimizer.adam.second_moments"
-LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"
-
-LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS  = "optimizer.lbfgs.current_parameters"
-LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"
-LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS   = "optimizer.lbfgs.current_gradients"
-LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS  = "optimizer.lbfgs.previous_gradients"
-LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION    = "optimizer.lbfgs.search_direction"
-LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES    = "optimizer.lbfgs.past_loss_values"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA        = "optimizer.lbfgs.memory_alpha"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS           = "optimizer.lbfgs.memory_ys"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S            = "optimizer.lbfgs.memory_s"
-LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y            = "optimizer.lbfgs.memory_y"
-
-LLM_KV_TRAINING_TYPE_TRAIN_MODEL   = "train_model"
-LLM_KV_TRAINING_TYPE_FINETUNE_LORA = "finetune_lora"
-LLM_KV_TRAINING_TYPE               = "training.type"
-LLM_KV_TRAINING_FILE_VERSION       = "training.file_version"
-LLM_KV_TRAINING_ITERATION_COUNT    = "training.iteration_count"
-LLM_KV_TRAINING_SAMPLE_COUNT       = "training.sample_count"
-LLM_KV_TRAINING_TOKEN_COUNT        = "training.token_count"
-
-class Tensor:
-    def __init__(self, dtype='f', ne=None):
-        if ne is None:
-            ne = []
-        self.dtype = dtype
-        self.ne = ne
-        self.nbytes = 0
-        if self.dtype == 'f':
-            if len(self.ne) == 0:
-                self.nbytes = 0
-            else:
-                self.nbytes = int(np.prod(self.ne)) * 4
-        else:
-            raise ValueError(f"Unhandled data type '{self.dtype}'")
-
-    def load(self, data, offset):
-        nd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        namelen = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        dtype = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-
-        assert(nd == len(self.ne))
-        ne = []
-        for d in range(nd):
-            n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-            ne.append(n)
-
-        assert(tuple(ne) == tuple(self.ne))
-
-        if self.dtype == 'f':
-            assert(dtype == 0)
-        else:
-            raise ValueError(f"Unhandled data type '{self.dtype}'")
-
-        self.name = bytes(data[offset:offset+namelen]); offset += namelen
-        # 32-byte alignment
-        offset += (0 - offset) & 31
-        self.data = data[offset:offset+self.nbytes]
-        offset += self.nbytes
-        return offset
-
-    def max_storage_size(self):
-        result = 0
-        result += 4 # nd
-        result += 4 # namelen
-        result += 4 # dtype
-        result += len(self.ne)*8 # ne
-        result += 48 # name (maximum as of commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9)
-        result += 31 # 32-byte alignment
-        result += self.nbytes
-        return result
-
-    def save_gguf(self, gguf_writer, name):
-        gguf_writer.add_tensor(
-            name=name,
-            tensor=self.data,
-            raw_shape=np.array(list(reversed(self.ne))),
-            raw_dtype=gguf.GGMLQuantizationType.F32)
-
-class OptimizationParamsV0:
-    def __init__(self):
-        pass
-
-    def load(self, data, offset):
-        self.type                 = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_threads            = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.past                 = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.delta                = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.print_forward_graph  = struct.unpack('<?', bytes(data[offset:offset + 1]))[0];  offset += 4 # 32bit-aligned
-        self.print_backward_graph = struct.unpack('<?', bytes(data[offset:offset + 1]))[0];  offset += 4 # 32bit-aligned
-        self.adam_n_iter          = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_sched           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_decay           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_alpha           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_beta1           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_beta2           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_eps             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_eps_f           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.adam_eps_g           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_m              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_n_iter         = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_max_linesearch = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_eps            = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_ftol           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_wolfe          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_min_step       = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_max_step       = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.lbfgs_linesearch     = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        return offset
-
-class OptimizationContext:
-    def __init__(self):
-        pass
-
-    def load(self, data, offset):
-        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
-        offset += 4
-
-        if self.version == 0:
-            params = OptimizationParamsV0()
-            offset = params.load(data, offset)
-            self.past = params.past
-            self.lbfgs_m = params.lbfgs_m
-            self.nx = struct.unpack('N', bytes(data[offset:offset + 8]))[0];  offset += 8
-            self.iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]);  offset += 4
-            self.type = params.type
-
-            self.adam_m  = Tensor('f', [self.nx])
-            self.adam_v  = Tensor('f', [self.nx])
-            self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
-
-            self.lbfgs_x    = Tensor('f', [self.nx])
-            self.lbfgs_xp   = Tensor('f', [self.nx])
-            self.lbfgs_g    = Tensor('f', [self.nx])
-            self.lbfgs_gp   = Tensor('f', [self.nx])
-            self.lbfgs_d    = Tensor('f', [self.nx])
-            self.lbfgs_pf   = Tensor('f', [self.past] if self.past > 0 else [])
-            self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
-            self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
-            self.lbfgs_lms  = Tensor('f', [self.nx, self.lbfgs_m])
-            self.lbfgs_lmy  = Tensor('f', [self.nx, self.lbfgs_m])
-
-            if self.type == 0:
-                # these tensors are stored, but we don't need their data
-                x  = Tensor('f', [self.nx])
-                g  = Tensor('f', [self.nx])
-                g2 = Tensor('f', [self.nx])
-                mh = Tensor('f', [self.nx])
-                vh = Tensor('f', [self.nx])
-
-                offset = x.load(data, offset)
-                offset = g.load(data, offset)
-                offset = g2.load(data, offset)
-                offset = self.adam_m.load(data, offset)
-                offset = self.adam_v.load(data, offset)
-                offset = mh.load(data, offset)
-                offset = vh.load(data, offset)
-                offset = self.adam_pf.load(data, offset)
-
-                self.adam_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.adam_fx_prev          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-            elif self.type == 1:
-                offset = self.lbfgs_x.load(data, offset)
-                offset = self.lbfgs_xp.load(data, offset)
-                offset = self.lbfgs_g.load(data, offset)
-                offset = self.lbfgs_gp.load(data, offset)
-                offset = self.lbfgs_d.load(data, offset)
-                offset = self.lbfgs_pf.load(data, offset)
-                offset = self.lbfgs_lmal.load(data, offset)
-                offset = self.lbfgs_lmys.load(data, offset)
-                offset = self.lbfgs_lms.load(data, offset)
-                offset = self.lbfgs_lmy.load(data, offset)
-
-                self.lbfgs_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_step             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_j                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_k                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_end              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-            else:
-                raise ValueError('Unknown optimizer type')
-
-
-        elif self.version == 1:
-            self.past    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.nx      = struct.unpack('N',  bytes(data[offset:offset + 8]))[0];  offset += 8
-            self.iter    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-            self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]);  offset += 4
-
-            self.adam_m  = Tensor('f', [self.nx])
-            self.adam_v  = Tensor('f', [self.nx])
-            self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
-
-            self.lbfgs_x    = Tensor('f', [self.nx])
-            self.lbfgs_xp   = Tensor('f', [self.nx])
-            self.lbfgs_g    = Tensor('f', [self.nx])
-            self.lbfgs_gp   = Tensor('f', [self.nx])
-            self.lbfgs_d    = Tensor('f', [self.nx])
-            self.lbfgs_pf   = Tensor('f', [self.past] if self.past > 0 else [])
-            self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
-            self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
-            self.lbfgs_lms  = Tensor('f', [self.nx, self.lbfgs_m])
-            self.lbfgs_lmy  = Tensor('f', [self.nx, self.lbfgs_m])
-
-            # forgot to save type in version 1:
-            # guess self.type from number of remaining bytes
-            size_type_0 = 12 + sum([t.max_storage_size() for t in
-                                    [self.adam_m, self.adam_v]
-                                    +([self.adam_pf] if (self.past > 0) else [])])
-            size_type_1 = 24 + sum([t.max_storage_size() for t in
-                                    [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,
-                                     self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,
-                                     self.lbfgs_lmal, self.lbfgs_lmys,
-                                     self.lbfgs_lms, self.lbfgs_lmy]
-                                     +([self.lbfgs_pf] if (self.past > 0) else [])])
-            # due to alignment padding the size might not by exact
-            # but the difference in size for both types is significant,
-            # so we can just use whichever is closest
-            remaining = len(data) - offset
-            if abs(remaining - size_type_0) < abs(remaining - size_type_1):
-                self.type = 0
-            else:
-                self.type = 1
-
-            if self.type == 0:
-                offset = self.adam_m.load(data, offset)
-                offset = self.adam_v.load(data, offset)
-                offset = self.adam_pf.load(data,offset)
-
-                self.adam_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.adam_fx_prev          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-            elif self.type == 1:
-                offset = self.lbfgs_x.load(data, offset)
-                offset = self.lbfgs_xp.load(data, offset)
-                offset = self.lbfgs_g.load(data, offset)
-                offset = self.lbfgs_gp.load(data, offset)
-                offset = self.lbfgs_d.load(data, offset)
-                offset = self.lbfgs_pf.load(data, offset)
-                offset = self.lbfgs_lmal.load(data, offset)
-                offset = self.lbfgs_lmys.load(data, offset)
-                offset = self.lbfgs_lms.load(data, offset)
-                offset = self.lbfgs_lmy.load(data, offset)
-
-                self.lbfgs_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_step             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_j                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_k                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_end              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-                self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
-
-        else:
-            raise ValueError('Invalid version of checkpoint file')
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_FILE_VERSION, 0)
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past)
-        gguf_writer.add_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx)
-        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter)
-        gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized)
-
-        if self.type == 0:
-            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, self.adam_fx_best)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, self.adam_fx_prev)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, self.adam_n_no_improvement)
-
-            self.adam_m.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS)
-            self.adam_v.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS)
-            if self.past > 0:
-                self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES)
-
-        elif self.type == 1:
-            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best)
-            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k)
-            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end)
-            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement)
-
-            self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS)
-            self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS)
-            self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS)
-            self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS)
-            self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION)
-            if self.past > 0:
-                self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES)
-            self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA)
-            self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS)
-            self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S)
-            self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y)
-        else:
-            raise ValueError('Unknown optimizer type')
-
-class ModelParams:
-    def __init__(self):
-        pass
-
-    def load(self, data, offset):
-        self.n_vocab = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_embd  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_mult  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_head  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_layer = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        self.n_rot   = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
-        return offset
-
-    def get_n_ff(self):
-        # struct my_llama_model::get_n_ff in train-text-from-scratch.cpp commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9
-        return ((2*(4*self.n_embd)//3 + self.n_mult - 1)//self.n_mult)*self.n_mult
-
-    def save_gguf(self, gguf_writer):
-        # self.n_vocab not saved
-        gguf_writer.add_embedding_length(self.n_embd)
-        gguf_writer.add_head_count(self.n_head)
-        gguf_writer.add_block_count(self.n_layer)
-        gguf_writer.add_rope_dimension_count(self.n_rot)
-        gguf_writer.add_feed_forward_length(self.get_n_ff())
-
-def tensor_name(key, bid=None):
-    return gguf.TENSOR_NAMES[key].format(bid=bid) + ".weight"
-
-class Layer:
-    def __init__(self, params, bid):
-        self.bid = bid
-        self.att_norm = Tensor('f', [params.n_embd])
-        self.wq       = Tensor('f', [params.n_embd, params.n_embd])
-        self.wk       = Tensor('f', [params.n_embd, params.n_embd])
-        self.wv       = Tensor('f', [params.n_embd, params.n_embd])
-        self.wo       = Tensor('f', [params.n_embd, params.n_embd])
-        self.ffn_norm = Tensor('f', [params.n_embd])
-        self.w1       = Tensor('f', [params.n_embd, params.get_n_ff()])
-        self.w2       = Tensor('f', [params.get_n_ff(), params.n_embd])
-        self.w3       = Tensor('f', [params.n_embd, params.get_n_ff()])
-
-    def load(self, data, offset):
-        offset = self.att_norm.load(data, offset)
-        offset = self.wq.load(data, offset)
-        offset = self.wk.load(data, offset)
-        offset = self.wv.load(data, offset)
-        offset = self.wo.load(data, offset)
-        offset = self.ffn_norm.load(data, offset)
-        offset = self.w1.load(data, offset)
-        offset = self.w2.load(data, offset)
-        offset = self.w3.load(data, offset)
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        self.att_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid))
-        self.wq.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q,    self.bid))
-        self.wk.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K,    self.bid))
-        self.wv.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V,    self.bid))
-        self.wo.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT,  self.bid))
-        self.ffn_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM,  self.bid))
-        self.w1.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE,  self.bid))
-        self.w2.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN,  self.bid))
-        self.w3.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP,    self.bid))
-
-class Model:
-    def __init__(self):
-        self.params = ModelParams()
-        self.layers = []
-
-    def load(self, data, offset):
-        offset = self.params.load(data, offset)
-
-        self.tok_embd = Tensor('f', [self.params.n_embd, self.params.n_vocab])
-        self.norm     = Tensor('f', [self.params.n_embd])
-        self.output   = Tensor('f', [self.params.n_embd, self.params.n_vocab])
-
-        offset = self.tok_embd.load(data, offset)
-        offset = self.norm.load(data, offset)
-        offset = self.output.load(data, offset)
-
-        self.layers.clear()
-        for bid in range(self.params.n_layer):
-            layer = Layer(self.params, bid)
-            offset = layer.load(data, offset)
-            self.layers.append(layer)
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        self.params.save_gguf(gguf_writer)
-
-        self.tok_embd.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD))
-        self.norm.save_gguf    (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM))
-        self.output.save_gguf  (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT))
-
-        for layer in self.layers:
-            layer.save_gguf(gguf_writer)
-
-class Checkpoint:
-    def __init__(self):
-        self.model = Model()
-        self.opt_ctx = OptimizationContext()
-
-    def load(self, data, offset):
-        magic   = bytes(reversed(data[offset:offset + 4])); offset += 4
-        if magic != b'ggcp':
-            raise ValueError(f"File header magic indicates, that this is no checkpoint file. Expected 'ggcp', Got '{str(magic)}'")
-
-        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        if self.version != 0:
-            raise ValueError('Invalid version of checkpoint file')
-
-        self.train_its     = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        self.train_samples = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-        self.train_tokens  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
-
-        offset = self.model.load(data, offset)
-        offset = self.opt_ctx.load(data, offset)
-
-        return offset
-
-    def save_gguf(self, gguf_writer):
-        gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32)
-        gguf_writer.add_layer_norm_rms_eps(1e-5)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION,    0)
-        gguf_writer.add_string(LLM_KV_TRAINING_TYPE,            LLM_KV_TRAINING_TYPE_TRAIN_MODEL)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_ITERATION_COUNT, self.train_its)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_SAMPLE_COUNT,    self.train_samples)
-        gguf_writer.add_uint32(LLM_KV_TRAINING_TOKEN_COUNT,     self.train_tokens)
-        self.model.save_gguf(gguf_writer)
-        self.opt_ctx.save_gguf(gguf_writer)
-
-def handle_args():
-    parser = argparse.ArgumentParser(description = 'Convert train-text-from-scratch checkpoints to GGUF')
-    parser.add_argument('--input',  '-i', type = Path, help = 'Input train checkpoint filename', required=True)
-    parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename', required=True)
-    return parser.parse_args()
-
-def main():
-    cfg = handle_args()
-    data = np.memmap(cfg.input, mode = 'r')
-    chk = Checkpoint()
-    offset = 0
-    offset = chk.load(data, offset)
-    # we should have read all available data
-    assert(offset == len(data))
-
-    gguf_writer = gguf.GGUFWriter(cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
-    chk.save_gguf(gguf_writer)
-    print("    gguf: write header")
-    gguf_writer.write_header_to_file()
-    print("    gguf: write metadata")
-    gguf_writer.write_kv_data_to_file()
-    print("    gguf: write tensors")
-    gguf_writer.write_tensors_to_file()
-    gguf_writer.close()
-
-if __name__ == '__main__':
-    main()
diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
deleted file mode 100644
index b779f6bd49876b..00000000000000
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ /dev/null
@@ -1,1253 +0,0 @@
-#include "ggml.h"
-#include "ggml-alloc.h"
-#include "ggml-backend.h"
-#include "common.h"
-#include "train.h"
-#include "llama.h"
-#include <unordered_map>
-#include <vector>
-#include <cassert>
-#include <climits>
-#include <cstring>
-#include <cstdarg>
-#include <ctime>
-#include <random>
-#include <stdexcept>
-#include <algorithm>
-#include <string>
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
-struct my_llama_hparams {
-    uint32_t n_vocab = 32000;
-    uint32_t n_ctx   = 512;
-    uint32_t n_embd  = 4096;
-    uint32_t n_head  = 32;
-    uint32_t n_layer = 32;
-    uint32_t n_rot   = 64;
-    uint32_t n_ff    = 11008;
-
-    // float f_norm_eps     = 1e-5f; // falcon
-    float f_norm_rms_eps = 1e-5f; // llama
-
-    float rope_freq_base  = 10000.0f;
-    float rope_freq_scale = 1.0f;
-};
-
-struct my_llama_layer {
-    // normalization
-    struct ggml_tensor * attention_norm;
-
-    // attention
-    struct ggml_tensor * wq;
-    struct ggml_tensor * wk;
-    struct ggml_tensor * wv;
-    struct ggml_tensor * wo;
-
-    // normalization
-    struct ggml_tensor * ffn_norm;
-
-    // ff
-    struct ggml_tensor * ffn_gate; // w1
-    struct ggml_tensor * ffn_down; // w2
-    struct ggml_tensor * ffn_up;   // w3
-};
-
-struct my_llama_model {
-    struct ggml_context * ctx = NULL;
-    ggml_backend_buffer_t data = NULL;
-
-    my_llama_hparams hparams;
-
-    struct ggml_tensor * tok_embeddings;
-
-    struct ggml_tensor * norm;
-    struct ggml_tensor * output;
-
-    std::vector<my_llama_layer> layers;
-};
-
-// gguf constants (sync with gguf.py)
-static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL     = "train_model";
-static const char * LLM_KV_TRAINING_TYPE                 = "training.type";
-
-static const char * LLM_KV_GENERAL_NAME                = "general.name";
-static const char * LLM_KV_GENERAL_ARCHITECTURE        = "general.architecture";
-static const char * LLM_KV_GENERAL_FILE_TYPE           = "general.file_type";
-
-static const char * LLM_KV_CONTEXT_LENGTH              = "%s.context_length";
-static const char * LLM_KV_EMBEDDING_LENGTH            = "%s.embedding_length";
-static const char * LLM_KV_BLOCK_COUNT                 = "%s.block_count";
-static const char * LLM_KV_FEED_FORWARD_LENGTH         = "%s.feed_forward_length";
-static const char * LLM_KV_ATTENTION_HEAD_COUNT        = "%s.attention.head_count";
-static const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = "%s.attention.layer_norm_rms_epsilon";
-static const char * LLM_KV_ROPE_DIMENSION_COUNT        = "%s.rope.dimension_count";
-static const char * LLM_KV_ROPE_FREQ_BASE              = "%s.rope.freq_base"; // TODO load in llama.cpp
-static const char * LLM_KV_ROPE_SCALE_LINEAR           = "%s.rope.scale_linear";
-
-static const char * LLM_KV_TOKENIZER_MODEL             = "tokenizer.ggml.model";
-static const char * LLM_KV_TOKENIZER_LIST              = "tokenizer.ggml.tokens";
-static const char * LLM_KV_TOKENIZER_TOKEN_TYPE        = "tokenizer.ggml.token_type";
-static const char * LLM_KV_TOKENIZER_SCORES            = "tokenizer.ggml.scores";
-static const char * LLM_KV_TOKENIZER_MERGES            = "tokenizer.ggml.merges";
-static const char * LLM_KV_TOKENIZER_BOS_ID            = "tokenizer.ggml.bos_token_id";
-static const char * LLM_KV_TOKENIZER_EOS_ID            = "tokenizer.ggml.eos_token_id";
-static const char * LLM_KV_TOKENIZER_UNK_ID            = "tokenizer.ggml.unknown_token_id";
-static const char * LLM_KV_TOKENIZER_SEP_ID            = "tokenizer.ggml.seperator_token_id";
-static const char * LLM_KV_TOKENIZER_PAD_ID            = "tokenizer.ggml.padding_token_id";
-
-static const char * LLM_TENSOR_TOKEN_EMBD    = "token_embd";
-static const char * LLM_TENSOR_OUTPUT_NORM   = "output_norm";
-static const char * LLM_TENSOR_OUTPUT        = "output";
-static const char * LLM_TENSOR_ATTN_NORM     = "blk.%d.attn_norm";
-static const char * LLM_TENSOR_ATTN_Q        = "blk.%d.attn_q";
-static const char * LLM_TENSOR_ATTN_K        = "blk.%d.attn_k";
-static const char * LLM_TENSOR_ATTN_V        = "blk.%d.attn_v";
-static const char * LLM_TENSOR_ATTN_OUT      = "blk.%d.attn_output";
-static const char * LLM_TENSOR_FFN_NORM      = "blk.%d.ffn_norm";
-static const char * LLM_TENSOR_FFN_GATE      = "blk.%d.ffn_gate";
-static const char * LLM_TENSOR_FFN_DOWN      = "blk.%d.ffn_down";
-static const char * LLM_TENSOR_FFN_UP        = "blk.%d.ffn_up";
-
-static void print_params(struct my_llama_hparams * params) {
-    printf("%s: n_vocab: %u\n", __func__, params->n_vocab);
-    printf("%s: n_ctx:   %u\n", __func__, params->n_ctx);
-    printf("%s: n_embd:  %u\n", __func__, params->n_embd);
-    printf("%s: n_head:  %u\n", __func__, params->n_head);
-    printf("%s: n_ff:    %u\n", __func__, params->n_ff);
-    printf("%s: n_layer: %u\n", __func__, params->n_layer);
-    printf("%s: n_rot:   %u\n", __func__, params->n_rot);
-}
-
-static void set_param_model(struct my_llama_model * model) {
-    const auto& hparams = model->hparams;
-
-    const uint32_t n_layer = hparams.n_layer;
-
-    struct ggml_context* ctx = model->ctx;
-
-    ggml_set_param(ctx, model->tok_embeddings);
-    ggml_set_param(ctx, model->norm);
-    ggml_set_param(ctx, model->output);
-
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = model->layers[i];
-
-        ggml_set_param(ctx, layer.attention_norm);
-        ggml_set_param(ctx, layer.wq);
-        ggml_set_param(ctx, layer.wk);
-        ggml_set_param(ctx, layer.wv);
-        ggml_set_param(ctx, layer.wo);
-        ggml_set_param(ctx, layer.ffn_norm);
-        ggml_set_param(ctx, layer.ffn_gate);
-        ggml_set_param(ctx, layer.ffn_down);
-        ggml_set_param(ctx, layer.ffn_up);
-    }
-}
-
-static void init_model(struct my_llama_model * model) {
-    const auto & hparams = model->hparams;
-
-    const uint32_t n_embd  = hparams.n_embd;
-    const uint32_t n_layer = hparams.n_layer;
-    const uint32_t n_vocab = hparams.n_vocab;
-    const uint32_t n_ff    = hparams.n_ff;
-
-
-    std::vector<char> tn_buf;
-    tn_buf.resize(GGML_MAX_NAME);
-    auto tn = [&tn_buf](const char * key) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
-        return tn_buf.data();
-    };
-    auto tni = [&tn_buf](const char * key, int bid) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
-        std::string s = tn_buf.data();
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
-        return tn_buf.data();
-    };
-
-    // context for model tensors without their data
-    struct ggml_init_params ctx_model_params;
-    ctx_model_params.mem_size   = ggml_tensor_overhead()*2*(6 + n_layer*18);
-    ctx_model_params.mem_buffer = NULL;
-    ctx_model_params.no_alloc   = true;
-
-    struct ggml_context * ctx = ggml_init(ctx_model_params);
-    model->ctx = ctx;
-
-    model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
-    model->norm           = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-    model->output         = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
-
-    ggml_set_name(model->tok_embeddings, tn(LLM_TENSOR_TOKEN_EMBD));
-    ggml_set_name(model->norm,           tn(LLM_TENSOR_OUTPUT_NORM));
-    ggml_set_name(model->output,         tn(LLM_TENSOR_OUTPUT));
-
-    model->layers.resize(n_layer);
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = model->layers[i];
-
-        layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
-        layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-        layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-        layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-        layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
-
-        layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
-        layer.ffn_gate = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd,   n_ff);
-        layer.ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_F32,   n_ff, n_embd);
-        layer.ffn_up   = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd,   n_ff);
-
-        ggml_set_name(layer.attention_norm, tni(LLM_TENSOR_ATTN_NORM, i));
-
-        ggml_set_name(layer.wq,             tni(LLM_TENSOR_ATTN_Q, i));
-        ggml_set_name(layer.wk,             tni(LLM_TENSOR_ATTN_K, i));
-        ggml_set_name(layer.wv,             tni(LLM_TENSOR_ATTN_V, i));
-        ggml_set_name(layer.wo,             tni(LLM_TENSOR_ATTN_OUT, i));
-
-        ggml_set_name(layer.ffn_norm,       tni(LLM_TENSOR_FFN_NORM, i));
-
-        ggml_set_name(layer.ffn_gate,       tni(LLM_TENSOR_FFN_GATE, i));
-        ggml_set_name(layer.ffn_down,       tni(LLM_TENSOR_FFN_DOWN, i));
-        ggml_set_name(layer.ffn_up,         tni(LLM_TENSOR_FFN_UP, i));
-    }
-
-    set_param_model(model);
-
-    // allocate data
-    model->data = ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_cpu_buffer_type());
-}
-
-static void randomize_model(struct my_llama_model * model, int seed, float mean, float std, float min, float max) {
-    const auto & hparams = model->hparams;
-
-    const uint32_t n_layer = hparams.n_layer;
-
-    struct random_normal_distribution * rnd = init_random_normal_distribution(seed, mean, std, min, max);
-
-    randomize_tensor_normal(model->tok_embeddings, rnd);
-    randomize_tensor_normal(model->norm,           rnd);
-    randomize_tensor_normal(model->output,         rnd);
-
-    for (uint32_t i = 0; i < n_layer; ++i) {
-        auto & layer = model->layers[i];
-        randomize_tensor_normal(layer.attention_norm, rnd);
-
-        randomize_tensor_normal(layer.wq, rnd);
-        randomize_tensor_normal(layer.wk, rnd);
-        randomize_tensor_normal(layer.wv, rnd);
-        randomize_tensor_normal(layer.wo, rnd);
-
-        randomize_tensor_normal(layer.ffn_norm, rnd);
-
-        randomize_tensor_normal(layer.ffn_gate, rnd);
-        randomize_tensor_normal(layer.ffn_down, rnd);
-        randomize_tensor_normal(layer.ffn_up,   rnd);
-    }
-
-    free_random_normal_distribution(rnd);
-}
-
-static struct ggml_tensor * llama_build_train_graphs(
-        struct my_llama_model * model,
-        ggml_gallocr_t          alloc,
-        struct ggml_context   * ctx,
-        struct ggml_cgraph    * gf,
-        struct ggml_cgraph    * gb,
-        struct ggml_cgraph    * gb_tmp,
-        struct ggml_tensor  * * logits,
-        struct ggml_tensor    * tokens_input,
-        struct ggml_tensor    * targets,
-        const  int              n_tokens,
-        const  int              n_batch,
-        const  bool             enable_flash_attn,
-        const  bool             enable_checkpointing,
-        const  bool             measure_only) {
-
-    ggml_set_scratch(ctx, { 0, 0, nullptr, });
-    const int n_past = 0;
-    const int N = n_tokens;
-    const auto & hparams = model->hparams;
-    const int n_ctx      = hparams.n_ctx;
-    const int n_vocab    = hparams.n_vocab;
-    const int n_embd     = hparams.n_embd;
-    const int n_layer    = hparams.n_layer;
-    const int n_head     = hparams.n_head;
-    const int n_rot      = hparams.n_rot;
-    const int n_ff       = hparams.n_ff;
-    const float f_norm_rms_eps  = hparams.f_norm_rms_eps;
-    const float rope_freq_base  = hparams.rope_freq_base;
-    const float rope_freq_scale = hparams.rope_freq_scale;
-
-    auto set_name = [](struct ggml_tensor * t, const char * n) {
-        ggml_set_name(t, n);
-        if (t->grad) {
-            ggml_format_name(t->grad, "%s->grad", n);
-        }
-    };
-
-    // KQ_pos - contains the positions
-    struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N);
-    ggml_set_input(KQ_pos);
-
-    // rope has so much parameters that we make a custom function for it
-    auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale]
-                (struct ggml_tensor * t) -> struct ggml_tensor * {
-        // not capturing these, to silcence warnings
-        const int rope_mode = 0;
-
-        return ggml_rope_ext(
-            ctx, t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f
-        );
-    };
-
-    set_name(tokens_input, "tokens_input");
-    set_name(targets,      "targets");
-
-    GGML_ASSERT(tokens_input->type == GGML_TYPE_I32);
-    struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch);  set_name(t00, "t00"); assert_shape_1d(t00, N*n_batch);
-    struct ggml_tensor * t01 = ggml_get_rows(ctx, model->tok_embeddings, t00); set_name(t01, "t01"); assert_shape_2d(t01, n_embd, N*n_batch);
-
-    struct ggml_tensor * cur = t01;
-
-    std::vector<struct ggml_tensor *> checkpoints;
-    checkpoints.push_back(tokens_input);
-    checkpoints.push_back(targets);
-    checkpoints.push_back(t00);
-    checkpoints.push_back(t01);
-
-    const float kv_scale = 1.0f/sqrtf(float(n_embd)/n_head);
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct my_llama_layer & layer = model->layers[il];
-        struct ggml_tensor * t02 = ggml_rms_norm     (ctx, cur, f_norm_rms_eps);                    set_name(t02, "t02");     assert_shape_2d(t02, n_embd, N*n_batch);
-        struct ggml_tensor * t03 = ggml_repeat       (ctx, layer.attention_norm, t02);              set_name(t03, "t03");     assert_shape_2d(t03, n_embd, N*n_batch);
-        struct ggml_tensor * t04 = ggml_mul          (ctx, t03, t02);                               set_name(t04, "t04");     assert_shape_2d(t04, n_embd, N*n_batch);
-        struct ggml_tensor * t05 = ggml_mul_mat      (ctx, layer.wq, t04);                          set_name(t05, "t05");     assert_shape_2d(t05, n_embd, N*n_batch);
-        struct ggml_tensor * t06 = ggml_reshape_4d   (ctx, t05, n_embd/n_head, n_head, N, n_batch); set_name(t06, "t06");     assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t07 = rope              (t06);                                         set_name(t07, "t07");     assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t08 = ggml_mul_mat      (ctx, layer.wk, t04);                          set_name(t08, "t08");     assert_shape_2d(t08, n_embd, N*n_batch);
-        struct ggml_tensor * t09 = ggml_reshape_4d   (ctx, t08, n_embd/n_head, n_head, N, n_batch); set_name(t09, "t09");     assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t10 = rope              (t09);                                         set_name(t10, "t10");     assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t11 = ggml_mul_mat      (ctx, t04, layer.wv);                          set_name(t11, "t11");     assert_shape_2d(t11, N*n_batch, n_embd);
-        struct ggml_tensor * t12 = ggml_reshape_4d   (ctx, t11, N, n_batch, n_embd/n_head, n_head); set_name(t12, "t12");     assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head);
-        struct ggml_tensor * t13 = ggml_permute      (ctx, t07, 0, 2, 1, 3);                        set_name(t13, "t13");     assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch);
-        struct ggml_tensor * t14 = ggml_permute      (ctx, t10, 0, 2, 1, 3);                        set_name(t14, "t14");     assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch);
-        struct ggml_tensor * t15 = ggml_permute      (ctx, t12, 0, 3, 1, 2);                        set_name(t15, "t15");     assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch);
-        struct ggml_tensor * t16;
-        if (enable_flash_attn) {
-            GGML_ASSERT(false && "TODO: ggml_flash_attn_ext() not yet supported");
-            //t16 = ggml_flash_attn(ctx, t13, t14, t15, true);                                        set_name(t16, "t16");     assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
-        } else {
-            struct ggml_tensor * t16_0 = ggml_mul_mat              (ctx, t14, t13);                 set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_1 = ggml_scale_inplace        (ctx, t16_0, kv_scale);          set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past);            set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch);
-            struct ggml_tensor * t16_3 = ggml_soft_max_inplace     (ctx, t16_2);                    set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch);
-            t16 = ggml_mul_mat(ctx, t15, t16_3);                                                    set_name(t16, "t16");     assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch);
-        }
-        struct ggml_tensor * t17 = ggml_permute      (ctx, t16, 0, 2, 1, 3);                        set_name(t17, "t17");     assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t18 = ggml_cont         (ctx, t17);                                    set_name(t18, "t18");     assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch);
-        struct ggml_tensor * t19 = ggml_reshape_2d   (ctx, t18, n_embd, N*n_batch);                 set_name(t19, "t19");     assert_shape_2d(t19, n_embd, N*n_batch);
-        struct ggml_tensor * t20 = ggml_mul_mat      (ctx, layer.wo, t19);                          set_name(t20, "t20");     assert_shape_2d(t20, n_embd, N*n_batch);
-        struct ggml_tensor * t21 = ggml_add          (ctx, t20, cur);                               set_name(t21, "t21");     assert_shape_2d(t21, n_embd, N*n_batch);
-        struct ggml_tensor * t22 = ggml_rms_norm     (ctx, t21, f_norm_rms_eps);                    set_name(t22, "t22");     assert_shape_2d(t22, n_embd, N*n_batch);
-        struct ggml_tensor * t23 = ggml_repeat       (ctx, layer.ffn_norm, t22);                    set_name(t23, "t23");     assert_shape_2d(t23, n_embd, N*n_batch);
-        struct ggml_tensor * t24 = ggml_mul          (ctx, t23, t22);                               set_name(t24, "t24");     assert_shape_2d(t24, n_embd, N*n_batch);
-        struct ggml_tensor * t25 = ggml_mul_mat      (ctx, layer.ffn_up, t24);                      set_name(t25, "t25");     assert_shape_2d(t25, n_ff, N*n_batch);
-        struct ggml_tensor * t26 = ggml_mul_mat      (ctx, layer.ffn_gate, t24);                    set_name(t26, "t26");     assert_shape_2d(t26, n_ff, N*n_batch);
-        struct ggml_tensor * t27 = ggml_silu         (ctx, t26);                                    set_name(t27, "t27");     assert_shape_2d(t27, n_ff, N*n_batch);
-        struct ggml_tensor * t28 = ggml_mul          (ctx, t27, t25);                               set_name(t28, "t28");     assert_shape_2d(t28, n_ff, N*n_batch);
-        struct ggml_tensor * t29 = ggml_mul_mat      (ctx, layer.ffn_down, t28);                    set_name(t29, "t29");     assert_shape_2d(t29, n_embd, N*n_batch);
-        struct ggml_tensor * t30 = ggml_add          (ctx, t29, t21);                               set_name(t30, "t30");     assert_shape_2d(t30, n_embd, N*n_batch);
-        cur = t30;
-        checkpoints.push_back(cur);
-    }
-    struct ggml_tensor * t31   = ggml_rms_norm          (ctx, cur, f_norm_rms_eps);                 set_name(t31, "t31");     assert_shape_2d(t31, n_embd, N*n_batch);
-    struct ggml_tensor * t32   = ggml_repeat            (ctx, model->norm, t31);                    set_name(t32, "t32");     assert_shape_2d(t32, n_embd, N*n_batch);
-    struct ggml_tensor * t33   = ggml_mul               (ctx, t32, t31);                            set_name(t33, "t33");     assert_shape_2d(t33, n_embd, N*n_batch);
-    struct ggml_tensor * t34   = ggml_mul_mat           (ctx, model->output, t33);                  set_name(t34, "t34");     assert_shape_2d(t34, n_vocab, N*n_batch);
-    struct ggml_tensor * t35   = ggml_reshape_3d        (ctx, t34, n_vocab, N, n_batch);            set_name(t35, "t35");     assert_shape_3d(t35, n_vocab, N, n_batch);
-    struct ggml_tensor * t36   = ggml_cross_entropy_loss(ctx, t35, targets);                        set_name(t36, "t36");     assert_shape_1d(t36, 1);
-
-    checkpoints.push_back(t31);
-    checkpoints.push_back(t32);
-    checkpoints.push_back(t33);
-    checkpoints.push_back(t34);
-    checkpoints.push_back(t35);
-    checkpoints.push_back(t36);
-
-    ggml_build_forward_expand(gf, t36);
-
-    if (enable_checkpointing) {
-        ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size());
-    } else {
-        ggml_graph_cpy(gf, gb);
-        ggml_build_backward_expand(ctx, gf, gb, true);
-    }
-
-    if (alloc) {
-        // make sure some tensors are not reallocated by inserting new temporary nodes depending on them
-        int n_leafs_before = gb->n_leafs;
-        int n_nodes_before = gb->n_nodes;
-        // output tensors
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, 1.0f));
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, 1.0f));
-        // input gradient
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, 1.0f));
-        // KQ_pos
-        ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, KQ_pos, 1.0f));
-        GGML_ASSERT(t36->grad->data == NULL && t36->grad->view_src == NULL);
-        ggml_set_input(t36->grad);
-
-        // allocating checkpoints in one block to reduce memory fragmentation
-        // note: they will be freed in reverse order
-        for (int i = 0; i < (int) checkpoints.size(); ++i) {
-            if (checkpoints[i]->data == NULL && checkpoints[i]->view_src == NULL) {
-                ggml_set_input(checkpoints[i]);
-            }
-        }
-
-        //int n_leafs_after = gb->n_leafs;
-        //int n_nodes_after = gb->n_nodes;
-        if (measure_only) {
-            // FIXME: will still allocate
-            ggml_gallocr_reserve(alloc, gb);
-        } else {
-            ggml_gallocr_alloc_graph(alloc, gb);
-
-            if (!measure_only) {
-                int * data = (int *) KQ_pos->data;
-                for (int i = 0; i < N; ++i) {
-                    data[i] = n_past + i;
-                }
-            }
-        }
-
-        // remove the additional nodes and leafs
-        for (int i = n_leafs_before; i < gb->n_leafs; ++i) {
-            gb->leafs[i] = NULL;
-        }
-        for (int i = n_nodes_before; i < gb->n_nodes; ++i) {
-            gb->nodes[i] = NULL;
-        }
-        gb->n_leafs = n_leafs_before;
-        gb->n_nodes = n_nodes_before;
-    }
-
-    *logits = t35;
-    return t36;
-}
-
-#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
-do { \
-    const std::string skey(key); \
-    const int kid = gguf_find_key(ctx, skey.c_str()); \
-    if (kid >= 0) { \
-        enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
-        if (ktype != (type)) { \
-            die_fmt("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype)); \
-        } \
-        (dst) = func(ctx, kid); \
-    } else if (req) { \
-        die_fmt("key not found in model: %s", skey.c_str()); \
-    } \
-} while (0)
-
-static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model) {
-    // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read
-    std::string arch;
-
-    std::vector<char> keybuf;
-    keybuf.resize(512);
-    auto kv = [&arch, &keybuf](const char * key) -> const char * {
-        snprintf(keybuf.data(), keybuf.size(), key, arch.c_str());
-        return keybuf.data();
-    };
-
-    std::vector<char> tn_buf;
-    tn_buf.resize(GGML_MAX_NAME);
-    auto tn = [&tn_buf](const char * key) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key);
-        return tn_buf.data();
-    };
-    auto tni = [&tn_buf](const char * key, int bid) -> const char * {
-        snprintf(tn_buf.data(), tn_buf.size(), key, bid);
-        std::string s = tn_buf.data();
-        snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str());
-        return tn_buf.data();
-    };
-
-    GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE);
-    GGML_ASSERT(arch == "llama");
-
-    uint32_t ftype_u;
-    GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE);
-    GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32);
-
-    // n_ctx was not saved in earlier checkpoint file versions, so we make it optional here
-    GGUF_GET_KEY(fctx, model->hparams.n_ctx,   gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH));
-
-    GGUF_GET_KEY(fctx, model->hparams.n_embd,  gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH));
-    GGUF_GET_KEY(fctx, model->hparams.n_ff,    gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH));
-    GGUF_GET_KEY(fctx, model->hparams.n_head,  gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
-    GGUF_GET_KEY(fctx, model->hparams.n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT));
-
-    model->hparams.n_rot = model->hparams.n_embd / model->hparams.n_head;
-    GGUF_GET_KEY(fctx, model->hparams.n_rot,   gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT));
-
-    float rope_freq_scale = 1.0f;
-    GGUF_GET_KEY(fctx, model->hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
-    GGUF_GET_KEY(fctx, model->hparams.rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
-    GGUF_GET_KEY(fctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
-    if (rope_freq_scale != 1.0f) {
-        model->hparams.rope_freq_scale = 1.0f / rope_freq_scale;
-    }
-
-    init_model(model);
-
-    copy_tensor_by_name(model->tok_embeddings, f_ggml_ctx, tn(LLM_TENSOR_TOKEN_EMBD));
-    copy_tensor_by_name(model->norm,           f_ggml_ctx, tn(LLM_TENSOR_OUTPUT_NORM));
-    copy_tensor_by_name(model->output,         f_ggml_ctx, tn(LLM_TENSOR_OUTPUT));
-
-    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
-        auto & layer = model->layers[i];
-
-        copy_tensor_by_name(layer.attention_norm, f_ggml_ctx, tni(LLM_TENSOR_ATTN_NORM, i));
-        copy_tensor_by_name(layer.wq,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_Q, i));
-        copy_tensor_by_name(layer.wk,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_K, i));
-        copy_tensor_by_name(layer.wv,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_V, i));
-        copy_tensor_by_name(layer.wo,             f_ggml_ctx, tni(LLM_TENSOR_ATTN_OUT, i));
-        copy_tensor_by_name(layer.ffn_norm,       f_ggml_ctx, tni(LLM_TENSOR_FFN_NORM, i));
-        copy_tensor_by_name(layer.ffn_gate,       f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i));
-        copy_tensor_by_name(layer.ffn_down,       f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i));
-        copy_tensor_by_name(layer.ffn_up,         f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i));
-    }
-}
-
-static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model) {
-    const char * arch = "llama";
-
-    enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
-
-    std::vector<char> keybuf;
-    keybuf.resize(512);
-    auto kv = [arch, &keybuf](const char * key) -> const char * {
-        snprintf(keybuf.data(), keybuf.size(), key, arch);
-        return keybuf.data();
-    };
-
-    // set arch
-    gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
-    gguf_set_val_str(fctx, LLM_KV_GENERAL_NAME, arch);
-    gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);
-
-    // set hparams
-    gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH),              model->hparams.n_ctx                  );
-    gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH),            model->hparams.n_embd                 );
-    gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH),         model->hparams.n_ff                   );
-    gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT),        model->hparams.n_head                 );
-    gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT),                 model->hparams.n_layer                );
-    gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT),        model->hparams.n_rot                  );
-
-    gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps         );
-    gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE),              model->hparams.rope_freq_base         ); // TODO load in llama.cpp
-    gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR),           1.0f / model->hparams.rope_freq_scale );
-
-    // set vocab by copying from vocab_model gguf file
-    {
-        struct gguf_init_params params = {
-            /*.no_alloc = */ false,
-            /*.ctx      = */ NULL,
-        };
-        struct gguf_context * vctx = gguf_init_from_file(fn_vocab_model, params);
-
-        const int token_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_LIST));
-        if (token_idx == -1) {
-            die("cannot find tokenizer vocab in model file");
-        }
-        const uint32_t n_vocab = gguf_get_arr_n(vctx, token_idx);
-
-        const int score_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_SCORES));
-        if (score_idx == -1) {
-            die("cannot find tokenizer scores in model file");
-        }
-
-        const float * scores = (const float * ) gguf_get_arr_data(vctx, score_idx);
-
-        const int toktype_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE));
-        if (toktype_idx == -1) {
-            die("cannot find token type list in GGUF file");
-        }
-
-        const int * toktypes = (const int * ) gguf_get_arr_data(vctx, toktype_idx);
-
-        std::string tokenizer_name;
-        GGUF_GET_KEY(vctx, tokenizer_name, gguf_get_val_str, GGUF_TYPE_STRING, true, kv(LLM_KV_TOKENIZER_MODEL));
-
-        gguf_set_val_str(fctx, kv(LLM_KV_TOKENIZER_MODEL), tokenizer_name.c_str());
-        gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_SCORES), GGUF_TYPE_FLOAT32, scores, n_vocab);
-        gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE), GGUF_TYPE_INT32, toktypes, n_vocab);
-
-        int32_t special_bos_id = 1;
-        int32_t special_eos_id = 2;
-        int32_t special_unk_id = 0;
-        int32_t special_sep_id = -1;
-        int32_t special_pad_id = -1;
-        if (tokenizer_name == "llama") {
-            // default special tokens
-            special_bos_id = 1;
-            special_eos_id = 2;
-            special_unk_id = 0;
-            special_sep_id = -1;
-            special_pad_id = -1;
-        } else if (tokenizer_name == "gpt2") {
-            // read and copy bpe merges
-            const int merges_keyidx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_MERGES));
-            if (merges_keyidx == -1) {
-                die("cannot find tokenizer merges in model file");
-            }
-
-            const int n_merges = gguf_get_arr_n(vctx, merges_keyidx);
-
-            std::vector<const char*> merges;
-            merges.resize(n_merges);
-            for (int i = 0; i < n_merges; i++) {
-                merges[i] = gguf_get_arr_str(vctx, merges_keyidx, i);
-            }
-            gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_MERGES), merges.data(), n_merges);
-
-            // default special tokens
-            special_bos_id = 11;
-            special_eos_id = 11;
-            special_unk_id = -1;
-            special_sep_id = -1;
-            special_pad_id = -1;
-        } else {
-            fprintf(stderr, "%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
-            fprintf(stderr, "%s: using default tokenizer: 'llama'", __func__);
-        }
-
-        std::vector<const char*> tokens;
-        tokens.resize(n_vocab);
-        for (uint32_t i = 0; i < n_vocab; i++) {
-            tokens[i] = gguf_get_arr_str(vctx, token_idx, i);
-        }
-        gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_LIST), tokens.data(), n_vocab);
-
-        GGUF_GET_KEY(vctx, special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
-        GGUF_GET_KEY(vctx, special_eos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_EOS_ID));
-        GGUF_GET_KEY(vctx, special_unk_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_UNK_ID));
-        GGUF_GET_KEY(vctx, special_sep_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_SEP_ID));
-        GGUF_GET_KEY(vctx, special_pad_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_PAD_ID));
-
-        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_BOS_ID), special_bos_id);
-        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_EOS_ID), special_eos_id);
-        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_UNK_ID), special_unk_id);
-        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_SEP_ID), special_sep_id);
-        gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_PAD_ID), special_pad_id);
-
-        gguf_free(vctx);
-    }
-
-    // add tensors
-    gguf_add_tensor(fctx, model->tok_embeddings);
-    gguf_add_tensor(fctx, model->norm);
-    gguf_add_tensor(fctx, model->output);
-    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
-        auto & layer = model->layers[i];
-
-
-        gguf_add_tensor(fctx, layer.attention_norm);
-        gguf_add_tensor(fctx, layer.wq);
-        gguf_add_tensor(fctx, layer.wk);
-        gguf_add_tensor(fctx, layer.wv);
-        gguf_add_tensor(fctx, layer.wo);
-        gguf_add_tensor(fctx, layer.ffn_norm);
-        gguf_add_tensor(fctx, layer.ffn_gate);
-        gguf_add_tensor(fctx, layer.ffn_down);
-        gguf_add_tensor(fctx, layer.ffn_up);
-    }
-}
-
-static void save_llama_model_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model) {
-    printf("%s: saving to %s\n", __func__, filename);
-    struct gguf_context * fctx = gguf_init_empty();
-
-    save_llama_model_gguf(fctx, fn_vocab_model, model);
-
-    // write file
-    const bool only_meta = false;
-    gguf_write_to_file(fctx, filename, only_meta);
-    gguf_free(fctx);
-}
-
-static void load_checkpoint_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct train_state * train) {
-    load_llama_model_gguf(fctx, f_ggml_ctx, model);
-    if (load_train_state_gguf(fctx, f_ggml_ctx, train)) {
-        std::string train_type = LLM_KV_TRAINING_TYPE_TRAIN_MODEL;
-        GGUF_GET_KEY(fctx, train_type, gguf_get_val_str, GGUF_TYPE_STRING, false, LLM_KV_TRAINING_TYPE);
-        GGML_ASSERT(train_type == LLM_KV_TRAINING_TYPE_TRAIN_MODEL);
-    } else {
-        printf("%s: loaded llama model as checkpoint\n", __func__);
-    }
-}
-
-static void save_checkpoint_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model, struct train_state * train) {
-    gguf_set_val_str(fctx, LLM_KV_TRAINING_TYPE, LLM_KV_TRAINING_TYPE_TRAIN_MODEL);
-    save_llama_model_gguf(fctx, fn_vocab_model, model);
-    save_train_state_gguf(fctx, train);
-}
-
-static bool load_checkpoint_file(const char * filename, struct my_llama_model * model, struct train_state * train) {
-    struct ggml_context * f_ggml_ctx;
-    struct gguf_init_params params;
-    params.no_alloc = false;
-    params.ctx = &f_ggml_ctx;
-    struct gguf_context * fctx = gguf_init_from_file(filename, params);
-    if (fctx == NULL) {
-        return false;
-    }
-
-    load_checkpoint_gguf(fctx, f_ggml_ctx, model, train);
-
-    gguf_free(fctx);
-    return true;
-}
-
-static void save_checkpoint_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model, struct train_state * train) {
-    printf("%s: saving to %s\n", __func__, filename);
-    struct gguf_context * fctx = gguf_init_empty();
-
-    save_checkpoint_gguf(fctx, fn_vocab_model, model, train);
-
-    // write file
-    const bool only_meta = false;
-    gguf_write_to_file(fctx, filename, only_meta);
-    gguf_free(fctx);
-}
-
-struct train_params {
-    struct train_params_common common;
-
-    const char * fn_vocab_model;
-    const char * fn_model_out;
-
-    bool only_write_model;
-
-    int n_ctx;
-    int n_embd;
-    int n_head;
-    int n_layer;
-    int n_ff;
-
-    float f_norm_rms_eps;
-    float rope_freq_base;
-    float rope_freq_scale;
-};
-
-static struct train_params get_default_train_params() {
-    struct train_params params;
-    params.common = get_default_train_params_common();
-    params.fn_vocab_model    = "ggml-vic7b-uncensored-q4_0.bin";
-    params.fn_model_out      = "ggml-checkpoint-f32.bin";
-
-    params.only_write_model = false;
-
-    params.n_ctx      =  128;
-    params.n_embd     =  256;
-    params.n_head     =    8;
-    params.n_layer    =   16;
-    params.n_ff       =  768;
-
-    params.f_norm_rms_eps  = 1e-5f;
-    params.rope_freq_base  = 10000.0f;
-    params.rope_freq_scale = 1.0f;
-
-    return params;
-}
-
-static void train_print_usage(int argc, char ** argv, const struct train_params * params) {
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h, --help                 show this help message and exit\n");
-
-    fprintf(stderr, "  --vocab-model FNAME        model path from which to load vocab (default '%s')\n", params->fn_vocab_model);
-    fprintf(stderr, "  --model-out FNAME          path to save ggml model (default '%s')\n", params->fn_model_out);
-    fprintf(stderr, "  --only-write-model         only save llama model, don't do any training. use this if you only want to convert a checkpoint to a model.\n");
-    fprintf(stderr, "  --embd N                   Embedding size used for new models (default %d)\n", params->n_embd);
-    fprintf(stderr, "  --ff N                     Feedforward size used for new models. (default %d)\n", params->n_ff);
-    fprintf(stderr, "  --head N                   Number of heads for new models (default %d)\n", params->n_head);
-    fprintf(stderr, "  --layer N                  Number of layers for new models (default %d)\n", params->n_layer);
-    fprintf(stderr, "  --norm-rms-eps F           RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps);
-    fprintf(stderr, "  --rope-freq-base F         Frequency base for ROPE (default %f)\n", params->rope_freq_base);
-    fprintf(stderr, "  --rope-freq-scale F        Frequency scale for ROPE (default %f)\n", params->rope_freq_scale);
-
-    print_common_train_usage(argc, argv, &params->common);
-}
-
-static bool train_params_parse(int argc, char ** argv, struct train_params * params) {
-    bool invalid_param = false;
-    std::string arg;
-    struct train_params default_params = get_default_train_params();
-    const std::string arg_prefix = "--";
-
-    for (int i = 1; i < argc; i++) {
-        arg = argv[i];
-        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-            std::replace(arg.begin(), arg.end(), '_', '-');
-        }
-
-        if (consume_common_train_arg(argc, argv, &i, &params->common, &invalid_param)) {
-            if (invalid_param) {
-                break;
-            } else if (params->common.print_usage) {
-                train_print_usage(argc, argv, &default_params);
-                exit(0);
-            }
-        } else if (arg == "--vocab-model") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_vocab_model = argv[i];
-        } else if (arg == "--model-out") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->fn_model_out = argv[i];
-        } else if (arg == "--only-write-model") {
-            params->only_write_model = true;
-        } else if (arg == "--embd") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_embd = std::stoi(argv[i]);
-        } else if (arg == "--ff") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_ff = std::stoi(argv[i]);
-        } else if (arg == "--head") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_head = std::stoi(argv[i]);
-        } else if (arg == "--layer") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->n_layer = std::stoi(argv[i]);
-        } else if (arg == "--norm-rms-eps") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->f_norm_rms_eps = std::stof(argv[i]);
-        } else if (arg == "--rope-freq-base") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->rope_freq_base = std::stof(argv[i]);
-        } else if (arg == "--rope-freq-scale") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params->rope_freq_scale = std::stof(argv[i]);
-        } else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            train_print_usage(argc, argv, &default_params);
-            exit(1);
-        }
-    }
-    if (invalid_param) {
-        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
-        train_print_usage(argc, argv, &default_params);
-        exit(1);
-    }
-    finish_processing_train_args(&params->common);
-
-    return true;
-}
-
-struct save_train_files_data {
-    const char            * fn_checkpoint_out;
-    const char            * fn_model_out;
-    const char            * fn_vocab_model;
-    const char            * pattern_fn_it;
-    const char            * fn_latest;
-    struct my_llama_model * model;
-};
-
-static void save_train_files(void * vdata, struct train_state * train) {
-    struct save_train_files_data * data   = (struct save_train_files_data *) vdata;
-    int64_t iter = train->opt->iter;
-
-    if (strlen(data->fn_checkpoint_out) > 0) {
-        save_checkpoint_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->fn_vocab_model, data->model, train);
-        save_checkpoint_file(get_train_filename(data->fn_checkpoint_out, data->pattern_fn_it, data->fn_latest, -1  ).c_str(), data->fn_vocab_model, data->model, train);
-
-    }
-    if (strlen(data->fn_model_out) > 0) {
-        save_llama_model_file(get_train_filename(data->fn_model_out, data->pattern_fn_it, data->fn_latest, iter).c_str(), data->fn_vocab_model, data->model);
-        save_llama_model_file(get_train_filename(data->fn_model_out, data->pattern_fn_it, data->fn_latest, -1  ).c_str(), data->fn_vocab_model, data->model);
-    }
-}
-
-static int64_t get_parameter_count(struct my_llama_model* model) {
-    int64_t nx = 0;
-    nx += ggml_nelements(model->tok_embeddings);
-    nx += ggml_nelements(model->norm);
-    nx += ggml_nelements(model->output);
-
-    for (uint32_t i = 0; i < model->layers.size(); ++i) {
-        auto & layer = model->layers[i];
-        nx += ggml_nelements(layer.attention_norm);
-        nx += ggml_nelements(layer.wq);
-        nx += ggml_nelements(layer.wk);
-        nx += ggml_nelements(layer.wv);
-        nx += ggml_nelements(layer.wo);
-        nx += ggml_nelements(layer.ffn_norm);
-        nx += ggml_nelements(layer.ffn_gate);
-        nx += ggml_nelements(layer.ffn_down);
-        nx += ggml_nelements(layer.ffn_up);
-    }
-    return nx;
-}
-
-int main(int argc, char ** argv) {
-    struct train_params params = get_default_train_params();
-
-    if (!train_params_parse(argc, argv, &params)) {
-        return 1;
-    }
-
-    if (params.common.seed == LLAMA_DEFAULT_SEED) {
-        params.common.seed = time(NULL);
-    }
-    printf("%s: seed: %u\n", __func__, params.common.seed);
-    srand(params.common.seed);
-
-    struct llama_model_params mparams = llama_model_default_params();
-    mparams.vocab_only = true;
-
-    struct llama_context_params cparams = llama_context_default_params();
-
-    struct llama_model * lmodel = llama_load_model_from_file(params.fn_vocab_model, mparams);
-    struct llama_context * lctx = llama_new_context_with_model(lmodel, cparams);
-
-    struct my_llama_model model;
-    model.hparams.n_vocab = llama_n_vocab(lmodel);
-    model.hparams.n_ctx   = params.common.n_ctx;
-    model.hparams.n_embd  = params.n_embd;
-    model.hparams.n_head  = params.n_head;
-    model.hparams.n_layer = params.n_layer;
-    model.hparams.n_ff    = params.n_ff;
-    // llama.cpp requires n_rot to be exactly n_embd / n_head
-    model.hparams.n_rot   = model.hparams.n_embd / model.hparams.n_head;
-    model.hparams.f_norm_rms_eps  = params.f_norm_rms_eps;
-    model.hparams.rope_freq_base  = params.rope_freq_base;
-    model.hparams.rope_freq_scale = params.rope_freq_scale;
-
-    struct train_state      * train = init_train_state();
-    struct ggml_opt_context * opt   = train->opt;
-
-    // set opt params from command line
-    opt->params = ggml_opt_default_params(GGML_OPT_TYPE_ADAM);
-    opt->params.print_forward_graph     = false;
-    opt->params.print_backward_graph    = false;
-    opt->params.graph_size              = LLAMA_TRAIN_MAX_NODES;
-    opt->params.n_threads               = params.common.n_threads;
-    opt->params.past                    = params.common.opt_past;
-    opt->params.delta                   = params.common.opt_delta;
-    opt->params.max_no_improvement      = params.common.opt_max_no_improvement;
-    opt->params.n_gradient_accumulation = params.common.n_gradient_accumulation;
-    opt->params.adam.n_iter             = params.common.adam_n_iter;
-    opt->params.adam.sched              = 1.0f;
-    opt->params.adam.alpha              = params.common.adam_alpha;
-    opt->params.adam.decay              = params.common.adam_decay;
-    opt->params.adam.decay_min_ndim     = params.common.adam_decay_min_ndim;
-    opt->params.adam.beta1              = params.common.adam_beta1;
-    opt->params.adam.beta2              = params.common.adam_beta2;
-    opt->params.adam.gclip              = params.common.adam_gclip;
-    opt->params.adam.eps_f              = params.common.adam_eps_f;
-
-    printf("%s: init model\n", __func__);
-    bool existed = load_checkpoint_file(params.common.fn_checkpoint_in, &model, train);
-    if (existed) {
-        // overwrite last n_ctx with user provided n_ctx
-        if (params.common.custom_n_ctx) {
-            model.hparams.n_ctx = params.common.n_ctx;
-        }
-
-        const bool opt_past_changed = opt->params.past != params.common.opt_past;
-
-        if (opt_past_changed) {
-            die("Optimizer parameter '--opt-past N' differs from checkpoint file. To use different value train from scratch with empty input checkpoint, e.g --checkpoint-in ''. Aborting");
-            // need to discard previous optimizer past function value statistics and opt_init with new shapes
-            // TODO
-        }
-    } else {
-        init_model(&model);
-        randomize_model(&model, params.common.seed, 0.0f, 1.0f, -1.0f, +1.0f);
-        if (!params.only_write_model) {
-            ggml_opt_init(opt->ctx, opt, opt->params, get_parameter_count(&model));
-        }
-    }
-    opt->iter = train->train_its;
-
-    print_params(&model.hparams);
-    printf("%s: total train_iterations %llu\n", __func__, (long long unsigned) train->train_its);
-    printf("%s: seen train_samples     %llu\n", __func__, (long long unsigned) train->train_samples);
-    printf("%s: seen train_tokens      %llu\n", __func__, (long long unsigned) train->train_tokens);
-    printf("%s: completed train_epochs %llu\n", __func__, (long long unsigned) train->train_epochs);
-    printf("%s: model_size = %zu bytes (%.1f MB)\n", __func__, (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)), (float) (ggml_used_mem(model.ctx) + ggml_backend_buffer_get_size(model.data)) / (1024.0f*1024.0f));
-
-    if (params.only_write_model) {
-        save_train_files_data save_data;
-        save_data.fn_checkpoint_out = "";
-        save_data.fn_model_out      = params.fn_model_out;
-        save_data.fn_vocab_model    = params.fn_vocab_model;
-        save_data.pattern_fn_it     = params.common.pattern_fn_it;
-        save_data.fn_latest         = params.common.fn_latest;
-        save_data.model             = &model;
-
-        save_train_files(&save_data, train);
-
-        free_train_state(train);
-        ggml_free(model.ctx);
-        llama_free(lctx);
-        llama_free_model(lmodel);
-        return 0;
-    }
-
-    printf("%s: opt_size  = %zu bytes (%.1f MB)\n", __func__, ggml_get_mem_size(opt->ctx), (float) ggml_get_mem_size(opt->ctx) / (1024.0f*1024.0f));
-    printf("%s: opt iter %d\n", __func__, opt->iter);
-
-    int n_tokens = model.hparams.n_ctx;
-    int n_vocab  = model.hparams.n_vocab;
-    int n_batch  = params.common.n_batch;
-
-    // context for input tensors without their data
-    struct ggml_init_params ctx_input_params = {
-        ggml_tensor_overhead() * 2, // mem_size
-        NULL,                       // mem_buffer
-        true,                       // no_alloc
-    };
-    struct ggml_context * ctx_input = ggml_init(ctx_input_params);
-
-    // the input tensors
-    struct ggml_tensor * tokens_input  = ggml_new_tensor_2d(ctx_input, GGML_TYPE_I32, n_tokens, n_batch);
-    struct ggml_tensor * target_probs  = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
-
-    // measure required memory for input tensors
-    // allocate input tensors
-    ggml_backend_buffer_t input_data = ggml_backend_alloc_ctx_tensors_from_buft(ctx_input, ggml_backend_cpu_buffer_type());
-    size_t max_input_size = ggml_backend_buffer_get_size(input_data);
-    printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
-
-    // context for compute tensors without their data
-    const size_t estimated_compute_size_wo_data = (
-            2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() +
-            (params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true))
-    );
-    struct ggml_init_params ctx_compute_params = {
-        estimated_compute_size_wo_data, // mem_size
-        NULL,                           // mem_buffer
-        true,                           // no_alloc
-    };
-    struct ggml_context * ctx_compute = NULL;
-
-    struct ggml_tensor * loss   = NULL;
-    struct ggml_tensor * logits = NULL;
-
-    struct ggml_cgraph * gf     = NULL;
-    struct ggml_cgraph * gb     = NULL;
-    struct ggml_cgraph * gb_tmp = NULL;
-
-    // measure required memory for compute tensors
-    size_t best_compute_size = SIZE_MAX;
-    enum ggml_cgraph_eval_order best_order = GGML_CGRAPH_EVAL_ORDER_COUNT;
-    // find best evaluation order
-    for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
-        ctx_compute = ggml_init(ctx_compute_params);
-        ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-        gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-        gf->order = (enum ggml_cgraph_eval_order) order;
-        gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-        gb_tmp = params.common.use_checkpointing
-            ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
-            : NULL;
-        loss = llama_build_train_graphs(
-            &model, alloc, ctx_compute,
-            gf, gb, gb_tmp,
-            &logits, tokens_input, target_probs,
-            n_tokens, n_batch,
-            params.common.use_flash,
-            params.common.use_checkpointing,
-            true
-        );
-        size_t max_compute_size = ggml_gallocr_get_buffer_size(alloc, 0); // FIXME: this will still allocate the buffer
-        if (max_compute_size < best_compute_size) {
-            best_compute_size = max_compute_size;
-            best_order = gf->order;
-        }
-        ggml_free(ctx_compute);
-    }
-    size_t max_compute_size = best_compute_size;
-    printf("%s: compute_size = %zu bytes (%.1f MB)\n", __func__, max_compute_size, (float) max_compute_size / (1024.0f*1024.0f));
-    printf("%s: evaluation order = %s\n", __func__,
-        (best_order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? "LEFT_TO_RIGHT" :
-        (best_order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? "RIGHT_TO_LEFT" :
-        "invalid");
-
-    // allocate compute tensors
-    ctx_compute = ggml_init(ctx_compute_params);
-    ggml_gallocr_t alloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
-    gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-    gf->order = best_order;
-    gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
-    gb_tmp = params.common.use_checkpointing
-        ? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
-        : NULL;
-    loss = llama_build_train_graphs(
-        &model, alloc, ctx_compute,
-        gf, gb, gb_tmp,
-        &logits, tokens_input, target_probs,
-        n_tokens, n_batch,
-        params.common.use_flash,
-        params.common.use_checkpointing,
-        false
-    );
-
-    std::vector<llama_token> train_tokens;
-    std::vector<size_t> train_samples_begin;
-    std::vector<size_t> train_samples_size;
-    printf("%s: tokenize training data\n", __func__);
-    tokenize_file(lctx,
-            params.common.fn_train_data,
-            params.common.sample_start,
-            params.common.include_sample_start,
-            params.common.overlapping_samples,
-            n_tokens,
-            train_tokens,
-            train_samples_begin,
-            train_samples_size);
-    GGML_ASSERT(train_samples_begin.size() == train_samples_size.size());
-
-    printf("%s: number of training tokens: %zu\n", __func__, train_tokens.size());
-
-    size_t shuffle_samples_hash = compute_samples_hash(params.common.fn_train_data, train_samples_begin.data(), train_samples_size.data(), train_samples_size.size());
-    const bool changed_train_data = (shuffle_samples_hash != train->shuffle_samples_hash) || (train->shuffle_sample_count != train_samples_size.size());
-    if (changed_train_data) {
-        printf("%s: train data seems to have changed. restarting shuffled epoch.\n", __func__);
-    }
-    if (params.common.force_reshuffle) {
-        printf("%s: forced reshuffling of data. restarting with newly shuffled epoch.\n", __func__);
-    }
-    if ((train->shuffle_rng_state_current == "") || changed_train_data || params.common.force_reshuffle) {
-        train->shuffle_rng_state_current = mt19937_seed_to_state(params.common.seed);
-        train->shuffle_sample_count = train_samples_size.size();
-        train->shuffle_next_sample = 0;
-        train->shuffle_samples_hash = shuffle_samples_hash;
-    }
-    std::vector<size_t> train_shuffled_samples_offs;
-    std::vector<size_t> train_shuffled_samples_begin;
-    std::vector<size_t> train_shuffled_samples_size;
-    train_shuffled_samples_offs.resize(train_samples_begin.size());
-    train_shuffled_samples_begin.resize(train_samples_begin.size());
-    train_shuffled_samples_size.resize(train_samples_size.size());
-    train->shuffle_rng_state_next = shuffle_samples(
-        train->shuffle_rng_state_current,
-        train_shuffled_samples_offs.data(),
-        train_shuffled_samples_begin.data(),
-        train_shuffled_samples_size.data(),
-        train_samples_begin.data(),
-        train_samples_size.data(),
-        train_samples_size.size());
-    printf("%s: begin training\n", __func__);
-
-    save_train_files_data save_data;
-    save_data.fn_checkpoint_out = params.common.fn_checkpoint_out;
-    save_data.fn_model_out      = params.fn_model_out;
-    save_data.fn_vocab_model    = params.fn_vocab_model;
-    save_data.pattern_fn_it     = params.common.pattern_fn_it;
-    save_data.fn_latest         = params.common.fn_latest;
-    save_data.model             = &model;
-
-    struct train_opt_callback_data opt_cb_data;
-    opt_cb_data.params                 = &params.common;
-    opt_cb_data.train                  = train;
-    opt_cb_data.save_cb                = &save_train_files;
-    opt_cb_data.save_data              = &save_data;
-    opt_cb_data.lctx                   = lctx;
-    opt_cb_data.last_save_iter         = opt->iter;
-    opt_cb_data.tokens_data            = train_tokens.data();
-    opt_cb_data.tokens_size            = train_tokens.size();
-    opt_cb_data.samples_begin          = train_samples_begin.data();
-    opt_cb_data.samples_size           = train_samples_size.data();
-    opt_cb_data.shuffled_samples_offs  = train_shuffled_samples_offs.data();
-    opt_cb_data.shuffled_samples_begin = train_shuffled_samples_begin.data();
-    opt_cb_data.shuffled_samples_size  = train_shuffled_samples_size.data();
-    opt_cb_data.samples_count          = train_samples_size.size();
-    opt_cb_data.tokens_input           = tokens_input;
-    opt_cb_data.target_probs           = target_probs;
-    opt_cb_data.first_iter             = opt->iter;
-    opt_cb_data.first_epoch            = train->train_epochs;
-    opt_cb_data.iter_at_last_epoch     = -1;
-    opt_cb_data.last_time              = ggml_time_ms();
-    opt_cb_data.millis_per_iter        = 0.0;
-
-    // measure required memory for work buffer
-    size_t max_work_size = ggml_graph_plan(gb, params.common.n_threads).work_size + GGML_OBJECT_SIZE;
-    printf("%s: work_size = %zu bytes (%.1f MB)\n", __func__, max_work_size, (float) max_work_size / (1024.0f*1024.0f));
-
-    // context for work buffer
-    struct ggml_init_params ctx_work_params = {
-        max_work_size, // mem_size
-        NULL,          // mem_buffer
-        false,         // no_alloc
-    };
-    struct ggml_context * ctx_work = ggml_init(ctx_work_params);
-
-    int64_t t0 = ggml_time_ms();
-
-    ggml_opt_resume_g(ctx_work, opt, loss, gf, gb, &train_opt_callback, (void *) &opt_cb_data);
-
-    ggml_free(ctx_work);
-    ggml_free(ctx_compute);
-    ggml_free(ctx_input);
-
-    int64_t t1 = ggml_time_ms();
-    printf("%s: total training time: ", __func__);
-    print_duration((double) (t1 - t0));
-    printf("\n");
-
-    int new_iters = opt->iter - opt_cb_data.last_save_iter;
-    if (new_iters > 0) {
-        train->train_its     += new_iters;
-        train->train_tokens  += new_iters * opt->params.n_gradient_accumulation * n_batch * n_tokens;
-
-        save_train_files(&save_data, train);
-        opt_cb_data.last_save_iter = opt->iter;
-    }
-
-    ggml_free(opt->ctx);
-    free_train_state(train);
-    ggml_free(model.ctx);
-    llama_free(lctx);
-    llama_free_model(lmodel);
-    return 0;
-}

From eddcb5238b2e09a37798b87cde1244017a194bcc Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 25 Jul 2024 12:37:42 +0300
Subject: [PATCH 118/125] ggml : add and use ggml_cpu_has_llamafile() (#8664)

---
 ggml/include/ggml.h | 1 +
 ggml/src/ggml.c     | 8 ++++++++
 src/llama.cpp       | 6 +-----
 3 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 2fdb9fa40274bc..548661b9bb6368 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -2400,6 +2400,7 @@ extern "C" {
     GGML_API int ggml_cpu_has_vsx        (void);
     GGML_API int ggml_cpu_has_matmul_int8(void);
     GGML_API int ggml_cpu_has_cann       (void);
+    GGML_API int ggml_cpu_has_llamafile  (void);
 
     //
     // Internal types and functions exposed for tests and benchmarks
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index dbb3a3ebe1cca4..f65837e856ac35 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -22005,6 +22005,14 @@ int ggml_cpu_has_cann(void) {
 #endif
 }
 
+int ggml_cpu_has_llamafile(void) {
+#if defined(GGML_USE_LLAMAFILE)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_gpublas(void) {
     return ggml_cpu_has_cuda() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl();
 }
diff --git a/src/llama.cpp b/src/llama.cpp
index 9e502018dfb764..80235ae19b2705 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -19146,11 +19146,7 @@ const char * llama_print_system_info(void) {
     s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
     s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";
     s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | ";
-#ifdef GGML_USE_LLAMAFILE
-    s += "LLAMAFILE = 1 | ";
-#else
-    s += "LLAMAFILE = 0 | ";
-#endif
+    s += "LLAMAFILE = "   + std::to_string(ggml_cpu_has_llamafile())   + " | ";
 
     return s.c_str();
 }

From ed67bcb24f2d6ac0072cae72620b2bd971741b98 Mon Sep 17 00:00:00 2001
From: Chen Xi <xi2.chen@intel.com>
Date: Thu, 25 Jul 2024 11:45:18 +0000
Subject: [PATCH 119/125] [SYCL] fix multi-gpu issue on sycl (#8554)

---------

Signed-off-by: Chen Xi <xi2chen@intel.com>
Co-authored-by: Meng, Hengyu <hengyu.meng@intel.com>
---
 docs/backend/SYCL.md               |  36 ++++------
 ggml/src/ggml-sycl/common.hpp      |   2 +-
 ggml/src/ggml-sycl/dpct/helper.hpp | 101 +++++++++++++++++++++++++----
 src/llama.cpp                      |   4 +-
 4 files changed, 102 insertions(+), 41 deletions(-)

diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md
index 885983e92277eb..d36ac0a158dd4f 100644
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -293,31 +293,26 @@ Similar to the native `sycl-ls`, available SYCL devices can be queried as follow
 ```sh
 ./build/bin/llama-ls-sycl-device
 ```
-A example of such log in a system with 1 *intel CPU* and 1 *intel GPU* can look like the following:
+This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following:
 ```
-found 6 SYCL devices:
+found 2 SYCL devices:
+
 |  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
 |ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
 |--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
 | 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
 | 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
-| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
-| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
-| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
-| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
 ```
 
-| Attribute              | Note                                                        |
-|------------------------|-------------------------------------------------------------|
-| compute capability 1.3 | Level-zero driver/runtime, recommended                      |
-| compute capability 3.0 | OpenCL driver/runtime, slower than level-zero in most cases |
 
 4. Launch inference
 
 There are two device selection modes:
 
 - Single device: Use one device target specified by the user.
-- Multiple devices: Automatically select the devices with the same largest Max compute-units.
+- Multiple devices: Automatically choose the devices with the same backend.
+
+In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR.
 
 | Device selection | Parameter                              |
 |------------------|----------------------------------------|
@@ -474,33 +469,26 @@ Similar to the native `sycl-ls`, available SYCL devices can be queried as follow
 build\bin\ls-sycl-device.exe
 ```
 
-The output of this command in a system with 1 *intel CPU* and 1 *intel GPU* would look like the following:
+This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following:
 ```
-found 6 SYCL devices:
+found 2 SYCL devices:
 |  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
 |ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|
 |--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
 | 0|[level_zero:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       1.3|        512|    1024|     32|    16225243136|
 | 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
-| 2|    [opencl:gpu:0]|               Intel(R) Arc(TM) A770 Graphics|       3.0|        512|    1024|     32|    16225243136|
-| 3|    [opencl:gpu:1]|                    Intel(R) UHD Graphics 770|       3.0|         32|     512|     32|    53651849216|
-| 4|    [opencl:cpu:0]|         13th Gen Intel(R) Core(TM) i7-13700K|       3.0|         24|    8192|     64|    67064815616|
-| 5|    [opencl:acc:0]|               Intel(R) FPGA Emulation Device|       1.2|         24|67108864|     64|    67064815616|
 
 ```
 
-| Attribute              | Note                                                      |
-|------------------------|-----------------------------------------------------------|
-| compute capability 1.3 | Level-zero running time, recommended                      |
-| compute capability 3.0 | OpenCL running time, slower than level-zero in most cases |
-
 
 4. Launch inference
 
 There are two device selection modes:
 
-- Single device: Use one device assigned by user.
-- Multiple devices: Automatically choose the devices with the same biggest Max compute units.
+- Single device: Use one device assigned by user. Default device id is 0.
+- Multiple devices: Automatically choose the devices with the same backend.
+
+In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR.
 
 | Device selection | Parameter                              |
 |------------------|----------------------------------------|
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index 68d41411b5ece6..397bd98dd22a99 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -267,7 +267,7 @@ struct ggml_backend_sycl_context {
 
     queue_ptr stream(int device, int stream) {
         if (qptrs[device][stream] == nullptr) {
-            qptrs[device][stream] = &(dpct::get_current_device().default_queue());
+            qptrs[device][stream] = &(dpct::get_device(device).default_queue());
         }
         return qptrs[device][stream];
     }
diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp
index 31df1cb9e2cf48..4aaa76bfbbc73e 100644
--- a/ggml/src/ggml-sycl/dpct/helper.hpp
+++ b/ggml/src/ggml-sycl/dpct/helper.hpp
@@ -588,7 +588,7 @@ namespace dpct
         out = prop;
     }
 
-   /// dpct device extension
+    /// dpct device extension
     class device_ext : public sycl::device {
       typedef std::mutex mutex_type;
 
@@ -697,7 +697,7 @@ namespace dpct
         std::unique_lock<mutex_type> lock(m_mutex);
         lock.unlock();
         for (auto &q : _queues) {
-          q.wait_and_throw();
+            q.wait_and_throw();
         }
         // Guard the destruct of current_queues to make sure the ref count is
         // safe.
@@ -734,7 +734,12 @@ namespace dpct
 
       void destroy_queue(sycl::queue queue) {
         std::lock_guard<mutex_type> lock(m_mutex);
-        _queues.clear();
+        _queues.erase(std::remove_if(_queues.begin(), _queues.end(),
+                                    [=](const sycl::queue &q) -> bool
+                                    {
+                                        return q == queue;
+                                    }),
+                    _queues.end());
       }
       void set_saved_queue(sycl::queue q) {
         std::lock_guard<mutex_type> lock(m_mutex);
@@ -764,13 +769,13 @@ namespace dpct
         if (enable_exception_handler) {
           eh = exception_handler;
         }
-        auto q = sycl::queue(*this, eh,
-                             sycl::property_list(
+        _queues.push_back(sycl::queue(
+            *this, eh,
+            sycl::property_list(
 #ifdef DPCT_PROFILING_ENABLED
-                                 sycl::property::queue::enable_profiling(),
+                sycl::property::queue::enable_profiling(),
 #endif
-                                 properties...));
-        _queues.push_back(q);
+                properties...)));
 
         return _queues.back();
       }
@@ -783,8 +788,8 @@ namespace dpct
         if (enable_exception_handler) {
           eh = exception_handler;
         }
-        _queues.push_back(
-            sycl::queue(device, eh,
+        _queues.push_back(sycl::queue(
+            device, eh,
                         sycl::property_list(
 #ifdef DPCT_PROFILING_ENABLED
                             sycl::property::queue::enable_profiling(),
@@ -855,15 +860,75 @@ namespace dpct
         unsigned int get_device_id(const sycl::device &dev)
         {
             unsigned int id = 0;
-            for (auto dev_item : _devs)
+            for (auto &dev_item : _devs)
             {
                 if (*dev_item == dev)
                 {
-                    break;
+                    return id;
                 }
                 id++;
             }
-            return id;
+            return -1;
+        }
+
+        inline std::string get_preferred_gpu_platform_name() {
+            std::string result;
+
+            std::string filter = "level-zero";
+            char* env = getenv("ONEAPI_DEVICE_SELECTOR");
+            if (env) {
+                if (std::strstr(env, "level_zero")) {
+                    filter = "level-zero";
+                }
+                else if (std::strstr(env, "opencl")) {
+                    filter = "opencl";
+                }
+                else if (std::strstr(env, "cuda")) {
+                    filter = "cuda";
+                }
+                else if (std::strstr(env, "hip")) {
+                    filter = "hip";
+                }
+                else {
+                    throw std::runtime_error("invalid device filter: " + std::string(env));
+                }
+            }
+
+            auto plaform_list = sycl::platform::get_platforms();
+
+            for (const auto& platform : plaform_list) {
+                auto devices = platform.get_devices();
+                auto gpu_dev = std::find_if(devices.begin(), devices.end(), [](const sycl::device& d) {
+                    return d.is_gpu();
+                });
+
+                if (gpu_dev == devices.end()) {
+                    // cout << "platform [" << platform_name
+                    //      << "] does not contain GPU devices, skipping\n";
+                    continue;
+                }
+
+                auto platform_name = platform.get_info<sycl::info::platform::name>();
+                std::string platform_name_low_case;
+                platform_name_low_case.resize(platform_name.size());
+
+                std::transform(
+                    platform_name.begin(), platform_name.end(), platform_name_low_case.begin(), ::tolower);
+
+                if (platform_name_low_case.find(filter) == std::string::npos) {
+                    // cout << "platform [" << platform_name
+                    //      << "] does not match with requested "
+                    //      << filter << ", skipping\n";
+                    continue;
+                }
+
+                result = platform_name;
+            }
+
+            if (result.empty())
+                throw std::runtime_error("can not find preferred GPU platform");
+
+            return result;
         }
 
         template <class DeviceSelector>
@@ -930,10 +995,15 @@ namespace dpct
             // Keep track of the number of devices per backend
             std::map<sycl::backend, size_t> DeviceNums;
             std::map<std::string, std::vector<sycl::device>> backend_devices;
+            auto preferred_platform_name = get_preferred_gpu_platform_name();
 
             while (!Platforms.empty()) {
                 auto Platform = Platforms.back();
                 Platforms.pop_back();
+                auto platform_name = Platform.get_info<sycl::info::platform::name>();
+                if (platform_name.compare(preferred_platform_name) != 0) {
+                    continue;
+                }
                 auto devices = Platform.get_devices();
                 std::string backend_type = get_device_backend_and_type(devices[0]);
                 for (const auto &device : devices) {
@@ -1989,6 +2059,11 @@ namespace dpct
         return dev_mgr::instance().current_device();
     }
 
+    static inline device_ext &get_device(unsigned int id)
+    {
+        return dev_mgr::instance().get_device(id);
+    }
+
     static inline sycl::queue &get_in_order_queue()
     {
         return dev_mgr::instance().current_device().in_order_queue();
diff --git a/src/llama.cpp b/src/llama.cpp
index 80235ae19b2705..972f870b072b86 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -16643,9 +16643,7 @@ struct llama_context * llama_new_context_with_model(
             for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) {
                 ggml_backend_t backend = ggml_backend_sycl_init(i);
                 if (backend == nullptr) {
-                    int id_list[GGML_SYCL_MAX_DEVICES];
-                    ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES);
-                    LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, id_list[i], i);
+                    LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d for No.%d backend\n", __func__, i, i);
                     llama_free(ctx);
                     return nullptr;
                 }

From 88954f7fbd31aeb8c75140edee03e7a8ad5e2d9c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 25 Jul 2024 18:57:44 +0300
Subject: [PATCH 120/125] tests : fix printfs (#8068)

---
 tests/test-chat-template.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index 46a7d3aea8f67a..a8222caeefb884 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -146,7 +146,7 @@ int main(void) {
     auto fmt_sys = [&](std::string tmpl) {
         auto output = llama_chat_format_single(nullptr, tmpl, chat2, sys_msg, false);
         printf("fmt_sys(%s) : %s\n", tmpl.c_str(), output.c_str());
-        printf("-------------------------\n", output.c_str());
+        printf("-------------------------\n");
         return output;
     };
     assert(fmt_sys("chatml") == "<|im_start|>system\nYou are a helpful assistant<|im_end|>\n");
@@ -165,7 +165,7 @@ int main(void) {
     auto fmt_single = [&](std::string tmpl) {
         auto output = llama_chat_format_single(nullptr, tmpl, chat2, new_msg, true);
         printf("fmt_single(%s) : %s\n", tmpl.c_str(), output.c_str());
-        printf("-------------------------\n", output.c_str());
+        printf("-------------------------\n");
         return output;
     };
     assert(fmt_single("chatml") == "\n<|im_start|>user\nHow are you<|im_end|>\n<|im_start|>assistant\n");

From bf5a81df375f1c71e41462e1f48d57db359c9e80 Mon Sep 17 00:00:00 2001
From: "Andreas (Andi) Kunar" <andreask@msn.com>
Date: Thu, 25 Jul 2024 18:01:00 +0200
Subject: [PATCH 121/125] ggml : fix build on Windows with Snapdragon X (#8531)

* Improvements for Windows with Snapdragon X

* Revert "Improvements for Windows with Snapdragon X"

This reverts commit bf21397ae5ea7c73d3494db3b91505599909227d.

* Improvements for Windows with Snapdragon X

* WOA build clarifications

* WIndows on ARM build clarifications

* cmake build for Windows clarifications

* Update docs/build.md

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: AndreasKunar <andreaskmsn.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 docs/build.md           | 13 ++++++++++++-
 ggml/src/ggml-aarch64.c | 12 ++++++------
 2 files changed, 18 insertions(+), 7 deletions(-)

diff --git a/docs/build.md b/docs/build.md
index 916fcf22d7924d..d9d12c46707bd7 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -16,7 +16,7 @@ In order to build llama.cpp you have four different options.
       make
       ```
 
-  - On Windows:
+  - On Windows (x86/x64 only, arm64 requires cmake):
 
     1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
     2. Extract `w64devkit` on your pc.
@@ -60,6 +60,17 @@ In order to build llama.cpp you have four different options.
       cmake -B build -G "Xcode"
       cmake --build build --config Debug
       ```
+    - Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers:
+      - Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/de/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...):
+        - Tab Workload: Desktop-development with C++
+        - Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang)
+      - Please remember to always use a Developer Command Prompt / PowerShell for VS2022 for git, build, test
+      - For Windows on ARM (arm64, WoA) build with:
+        ```bash
+        cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF
+        cmake --build build-arm64-windows-llvm-release
+        ```
+        Note: Building for arm64 could also be done just with MSVC (with the build-arm64-windows-MSVC preset, or the standard CMake build instructions). But MSVC does not support inline ARM assembly-code, used e.g. for the accelerated Q4_0_4_8 CPU kernels.
 
 -   Using `gmake` (FreeBSD):
 
diff --git a/ggml/src/ggml-aarch64.c b/ggml/src/ggml-aarch64.c
index 26535b1c432ba6..af53dea172459a 100644
--- a/ggml/src/ggml-aarch64.c
+++ b/ggml/src/ggml-aarch64.c
@@ -392,7 +392,7 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void *
 #if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
     GGML_ASSERT(!(ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) &&
                 "__ARM_NEON and __ARM_FEATURE_MATMUL_INT8 defined, use the Q4_0_4_8 quantization format for optimal performance");
-#elif defined(__ARM_NEON) && defined(__aarch64__)
+#elif defined(__ARM_NEON) && defined(__aarch64__) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
     const void * b_ptr = vx;
     const void * a_ptr = vy;
     float * res_ptr = s;
@@ -501,7 +501,7 @@ void ggml_gemv_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void *
                     "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
     }
 #endif
-#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
     const void * b_ptr = vx;
     const void * a_ptr = vy;
     float * res_ptr = s;
@@ -613,7 +613,7 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
     UNUSED(ncols_interleaved);
     UNUSED(blocklen);
 
-#if defined(__ARM_FEATURE_SVE)
+#if defined(__ARM_FEATURE_SVE) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
     if (svcntw() == 8) {
         const void * b_ptr = vx;
         const void * a_ptr = vy;
@@ -753,7 +753,7 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void *
 #if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
     GGML_ASSERT(!(ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) &&
                 "__ARM_NEON and __ARM_FEATURE_MATMUL_INT8 defined, use the Q4_0_4_8 quantization format for optimal performance");
-#elif defined(__ARM_NEON) && defined(__aarch64__)
+#elif defined(__ARM_NEON) && defined(__aarch64__) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
     const void * b_ptr = vx;
     const void * a_ptr = vy;
     float * res_ptr = s;
@@ -1271,7 +1271,7 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * restrict s, size_t bs, const void *
                     "__ARM_FEATURE_SVE defined, use the Q4_0_8_8 quantization format for optimal performance");
     }
 #endif
-#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
     const void * b_ptr = vx;
     const void * a_ptr = vy;
     float * res_ptr = s;
@@ -1727,7 +1727,7 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void *
     UNUSED(ncols_interleaved);
     UNUSED(blocklen);
 
-#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
+#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8) && ! ((defined(_MSC_VER)) && ! defined(__clang__))
     if (svcntw() == 8) {
         const void * b_ptr = vx;
         const void * a_ptr = vy;

From 4226a8d10e3904db3a1297919fe6c7f06beba6c0 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 25 Jul 2024 19:57:31 +0300
Subject: [PATCH 122/125] llama : fix build + fix fabs compile warnings (#8683)

ggml-ci
---
 src/llama-grammar.h |  2 --
 src/llama.cpp       | 10 ++--------
 2 files changed, 2 insertions(+), 10 deletions(-)

diff --git a/src/llama-grammar.h b/src/llama-grammar.h
index 8e578e09f8c2dc..695ea0632bb84c 100644
--- a/src/llama-grammar.h
+++ b/src/llama-grammar.h
@@ -13,8 +13,6 @@ struct llama_grammar {
     llama_partial_utf8 partial_utf8;
 };
 
-struct llama_grammar * llama_get_grammar(struct llama_context * ctx);
-
 //
 // internal API
 //
diff --git a/src/llama.cpp b/src/llama.cpp
index 972f870b072b86..bc0183741db4e4 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -2657,7 +2657,6 @@ struct llama_context {
     llama_context(const llama_model & model)
         : model(model)
         , sampling(llama_n_vocab(&model))
-        , grammar()
         , t_start_us(model.t_start_us)
         , t_load_us(model.t_load_us) {}
 
@@ -2675,7 +2674,6 @@ struct llama_context {
 
     struct llama_cparams        cparams;
     struct llama_sampling       sampling;
-    struct llama_grammar        grammar;
     struct llama_kv_cache       kv_self;
     struct llama_control_vector cvec;
 
@@ -14048,7 +14046,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
                             f = -INFINITY;
                         } else {
                             if (hparams.use_alibi) {
-                                f = -fabs(lctx.kv_self.cells[i].pos - pos);
+                                f = -std::abs(lctx.kv_self.cells[i].pos - pos);
                             } else {
                                 f = 0.0f;
                             }
@@ -14102,7 +14100,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
                         for (int s = 0; s < batch.n_seq_id[i]; ++s) {
                             if (batch.seq_id[i][s] == seq_id) {
                                 if (hparams.use_alibi) {
-                                    f = -fabs(batch.pos[i] - batch.pos[j]);
+                                    f = -std::abs(batch.pos[i] - batch.pos[j]);
                                 } else {
                                     f = 0.0f;
                                 }
@@ -16833,10 +16831,6 @@ const struct llama_vocab * llama_get_vocab(const struct llama_context * ctx) {
     return &ctx->model.vocab;
 }
 
-struct llama_grammar * llama_get_grammar(struct llama_context * ctx) {
-    return &ctx->grammar;
-}
-
 uint32_t llama_n_ctx(const struct llama_context * ctx) {
     return ctx->cparams.n_ctx;
 }

From 49ce0ab6d45402e8bb622bf86f86529f2b0ba552 Mon Sep 17 00:00:00 2001
From: DavidKorczynski <david@adalogics.com>
Date: Thu, 25 Jul 2024 22:23:05 +0100
Subject: [PATCH 123/125] ggml: handle ggml_init failure to fix NULL pointer
 deref (#8692)

`ggml_init` can fail if no unused context is found. In that case, a NULL-pointer deref will happen later in the code during a call to `ggml_set_on_alloc`.

This fixes it by bailing out if no context is found.
---
 ggml/src/ggml.c | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index f65837e856ac35..29afcc7f8978bd 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -21096,6 +21096,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
         };
 
         *params.ctx = ggml_init(pdata);
+        if (*params.ctx == NULL) {
+            fprintf(stderr, "%s: failed to initialize context\n", __func__);
+            fclose(file);
+            gguf_free(ctx);
+            return NULL;
+        }
 
         struct ggml_context * ctx_data = *params.ctx;
 

From 41cd47caab88c442edc50e90c8d8d0ac3e82768d Mon Sep 17 00:00:00 2001
From: Xuan Son Nguyen <thichthat@gmail.com>
Date: Thu, 25 Jul 2024 23:49:39 +0200
Subject: [PATCH 124/125] examples : export-lora : fix issue with quantized
 base models (#8687)

---
 examples/export-lora/export-lora.cpp | 64 +++++++++++++++++-----------
 1 file changed, 39 insertions(+), 25 deletions(-)

diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp
index 124ee167d2255f..150f7e8d58f206 100644
--- a/examples/export-lora/export-lora.cpp
+++ b/examples/export-lora/export-lora.cpp
@@ -211,8 +211,9 @@ struct lora_merge_ctx {
             }
         }
 
-        // if true, this tensor can be lora-merged. if false, we skip merging and just copy data to outfile
-        std::vector<std::pair<struct ggml_tensor *, bool>> base_tensors;
+        // mapping base tensor to out tensor (same shape with base, but different type)
+        // if out_tensor == nullptr, we only copy it
+        std::vector<std::pair<struct ggml_tensor *, struct ggml_tensor *>> base_to_out_tensors;
         for (auto & it : base_model.tensors) {
             bool t_a = true;
             bool t_b = true;
@@ -221,22 +222,22 @@ struct lora_merge_ctx {
                 t_b &= nullptr != adapter->get_tensor(it.first + ".lora_b");
             }
             auto base_tensor = it.second;
-            struct ggml_tensor * out_tensor;
             if (!t_a && !t_b) {
                 // only copy
-                out_tensor = ggml_dup_tensor(ctx_out_ggml, base_tensor);
-                ggml_set_name(out_tensor, base_tensor->name);
-                base_tensors.push_back(std::make_pair(out_tensor, false));
+                struct ggml_tensor * cpy_tensor = ggml_dup_tensor(ctx_out_ggml, base_tensor);
+                ggml_set_name(cpy_tensor, base_tensor->name);
+                base_to_out_tensors.push_back(std::make_pair(cpy_tensor, nullptr));
+                gguf_add_tensor(ctx_out, cpy_tensor);
             } else if (t_a && t_b) {
                 // need merging
-                out_tensor = ggml_dup_tensor(ctx_out_ggml, base_tensor);
-                out_tensor->type = get_out_tensor_type(base_tensor);
+                struct ggml_tensor * out_tensor = ggml_new_tensor(
+                    ctx_out_ggml, get_out_tensor_type(base_tensor), GGML_MAX_DIMS, base_tensor->ne);
                 ggml_set_name(out_tensor, base_tensor->name);
-                base_tensors.push_back(std::make_pair(out_tensor, true));
+                base_to_out_tensors.push_back(std::make_pair(base_tensor, out_tensor));
+                gguf_add_tensor(ctx_out, out_tensor);
             } else {
                 throw std::runtime_error("tensor " + it.first + " missing either lora_a or lora_b");
             }
-            gguf_add_tensor(ctx_out, out_tensor);
         }
 
         // placeholder for the meta data
@@ -247,9 +248,9 @@ struct lora_merge_ctx {
 
         // process base model tensors
         size_t n_merged = 0;
-        for (auto & it : base_tensors) {
-            if (it.second) {
-                merge_tensor(it.first);
+        for (auto & it : base_to_out_tensors) {
+            if (it.second != nullptr) {
+                merge_tensor(it.first, it.second);
                 n_merged++;
             } else {
                 copy_tensor(it.first);
@@ -265,7 +266,7 @@ struct lora_merge_ctx {
         }
 
         printf("%s : merged %ld tensors with lora adapters\n", __func__, n_merged);
-        printf("%s : wrote %ld tensors to output file\n", __func__, base_tensors.size());
+        printf("%s : wrote %ld tensors to output file\n", __func__, base_to_out_tensors.size());
     }
 
     void copy_tensor(struct ggml_tensor * base) {
@@ -276,7 +277,7 @@ struct lora_merge_ctx {
         zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len);
     }
 
-    void merge_tensor(struct ggml_tensor * base) {
+    void merge_tensor(struct ggml_tensor * base, struct ggml_tensor * out) {
         std::string name_base(base->name);
         std::string name_lora_a = name_base + ".lora_a";
         std::string name_lora_b = name_base + ".lora_b";
@@ -287,14 +288,14 @@ struct lora_merge_ctx {
         std::vector<struct ggml_tensor *> inp_a(adapters.size());
         std::vector<struct ggml_tensor *> inp_b(adapters.size());
         struct ggml_init_params params {
-            /*.mem_size   =*/ ggml_tensor_overhead()*(1+adapters.size()*2),
+            /*.mem_size   =*/ ggml_tensor_overhead()*(2+adapters.size()*2),
             /*.mem_buffer =*/ NULL,
             /*.no_alloc   =*/ true,
         };
         struct ggml_context * ctx = ggml_init(params);
 
         // alloc tensors
-        struct ggml_tensor * inp = ggml_dup_tensor(ctx, base);
+        struct ggml_tensor * inp_base = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, base->ne);
         for (size_t i = 0; i < adapters.size(); ++i) {
             auto t_a = adapters[i]->get_tensor(name_lora_a);
             auto t_b = adapters[i]->get_tensor(name_lora_b);
@@ -303,9 +304,21 @@ struct lora_merge_ctx {
         }
         ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
 
-        // load data to backend buffer
+        // load base tensor to backend buffer
         base_model.read_tensor_data(name_base, read_buf);
-        ggml_backend_tensor_set(inp, read_buf.data(), 0, ggml_nbytes(inp));
+        if (base->type != GGML_TYPE_F32) {
+            // optionally dequantize it
+            printf("%s :   + dequantize base tensor from %s to F32\n", __func__, ggml_type_name(base->type));
+            auto nels = ggml_nelements(inp_base);
+            ggml_type_traits_t qtype = ggml_internal_get_type_traits(base->type);
+            std::vector<uint8_t> dequant_buf(nels * sizeof(float));
+            qtype.to_float(read_buf.data(), (float *)dequant_buf.data(), nels);
+            ggml_backend_tensor_set(inp_base, dequant_buf.data(), 0, dequant_buf.size());
+        } else {
+            ggml_backend_tensor_set(inp_base, read_buf.data(), 0, ggml_nbytes(inp_base));
+        }
+
+        // load lora tensors to backend buffer
         for (size_t i = 0; i < adapters.size(); ++i) {
             adapters[i]->read_tensor_data(name_lora_a, read_buf);
             ggml_backend_tensor_set(inp_a[i], read_buf.data(), 0, ggml_nbytes(inp_a[i]));
@@ -325,20 +338,21 @@ struct lora_merge_ctx {
             };
             struct ggml_context * ctx0 = ggml_init(params0);
             gf = ggml_new_graph(ctx0);
-            struct ggml_tensor * cur = inp;
+            struct ggml_tensor * cur = inp_base;
             for (size_t i = 0; i < adapters.size(); ++i) {
-                struct ggml_tensor * a_T = ggml_cont(ctx0, ggml_transpose(ctx0, inp_a[i]));
-                struct ggml_tensor * delta = ggml_mul_mat(ctx0, a_T, inp_b[i]);
+                struct ggml_tensor * a_T = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32)));
+                struct ggml_tensor * delta = ggml_mul_mat(ctx0, a_T, ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32));
                 // scale
                 const float alpha = adapters[i]->alpha;
                 const float rank  = (float) inp_b[i]->ne[0];
                 const float scale = alpha ? adapters[i]->scale * alpha / rank : adapters[i]->scale;
                 delta = ggml_scale(ctx0, delta, scale);
-                cur = ggml_add(ctx0, cur, delta);
-                printf("%s :   + merging from adapter[%ld]\n", __func__, i);
+                cur = ggml_add(ctx0, delta, cur);
+                printf("%s :   + merging from adapter[%ld] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type));
                 printf("%s :     input_scale=%f calculated_scale=%f rank=%d\n", __func__, adapters[i]->scale, scale, (int) inp_b[i]->ne[0]);
             }
-            cur = ggml_cast(ctx0, cur, get_out_tensor_type(base));
+            cur = ggml_cast(ctx0, cur, out->type);
+            printf("%s :   + output type is %s\n", __func__, ggml_type_name(out->type));
             ggml_build_forward_expand(gf, cur);
             ggml_free(ctx0);
         }

From 01aec4a6310ab0160483196db0e726d78d4c94b6 Mon Sep 17 00:00:00 2001
From: Yaiko <elyaiko@hotmail.com>
Date: Thu, 25 Jul 2024 18:10:16 -0400
Subject: [PATCH 125/125] server : add Speech Recognition & Synthesis to UI
 (#8679)

* server : add Speech Recognition & Synthesis to UI

* server : add Speech Recognition & Synthesis to UI (fixes)
---
 examples/server/public/index.html | 180 +++++++++++++++++++++++++++---
 1 file changed, 164 insertions(+), 16 deletions(-)

diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 48628a960de1c5..07fec6a38bbcde 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -1,5 +1,4 @@
 <html>
-
 <head>
   <meta charset="UTF-8">
   <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
@@ -132,12 +131,20 @@
       align-items: stretch;
     }
 
-    .right {
+    .message-controls {
       display: flex;
-      flex-direction: row;
-      gap: 0.5em;
       justify-content: flex-end;
     }
+    .message-controls > div:nth-child(2) {
+      display: flex;
+      flex-direction: column;
+      gap: 0.5em;
+    }
+    .message-controls > div:nth-child(2) > div {
+      display: flex;
+      margin-left: auto;
+      gap: 0.5em;
+    }
 
     fieldset {
       border: none;
@@ -276,6 +283,7 @@
 
     import { llama } from './completion.js';
     import { SchemaConverter } from './json-schema-to-grammar.mjs';
+
     let selected_image = false;
     var slot_id = -1;
 
@@ -447,6 +455,9 @@
 
     /* END: Support for storing prompt templates and parameters in browsers LocalStorage */
 
+    const tts = window.speechSynthesis;
+    const ttsVoice = signal(null)
+
     const llamaStats = signal(null)
     const controller = signal(null)
 
@@ -596,8 +607,51 @@
       });
     }
 
+    const SpeechRecognition = window.SpeechRecognition || window.webkitSpeechRecognition;
+    const talkRecognition = SpeechRecognition ? new SpeechRecognition() : null;
     function MessageInput() {
-      const message = useSignal("")
+      const message = useSignal("");
+
+      const talkActive = useSignal(false);
+      const sendOnTalk = useSignal(false);
+      const talkStop = (e) => {
+        if (e) e.preventDefault();
+
+        talkActive.value = false;
+        talkRecognition?.stop();
+      }
+      const talk = (e) => {
+        e.preventDefault();
+
+        if (talkRecognition)
+          talkRecognition.start();
+        else
+          alert("Speech recognition is not supported by this browser.");
+      }
+      if(talkRecognition) {
+        talkRecognition.onstart = () => {
+          talkActive.value = true;
+        }
+        talkRecognition.onresult = (e) => {
+          if (event.results.length > 0) {
+            message.value = event.results[0][0].transcript;
+            if (sendOnTalk.value) {
+              submit(e);
+            }
+          }
+        }
+        talkRecognition.onspeechend = () => {
+          talkStop();
+        }
+      }
+
+      const ttsVoices = useSignal(tts?.getVoices() || []);
+      const ttsVoiceDefault = computed(() => ttsVoices.value.find(v => v.default));
+      if (tts) {
+        tts.onvoiceschanged = () => {
+          ttsVoices.value = tts.getVoices();
+        }
+      }
 
       const submit = (e) => {
         stop(e);
@@ -624,11 +678,45 @@
                value="${message}"
             />
           </div>
-          <div class="right">
-            <button type="submit" disabled=${generating.value}>Send</button>
-            <button onclick=${uploadImage}>Upload Image</button>
-            <button onclick=${stop} disabled=${!generating.value}>Stop</button>
-            <button onclick=${reset}>Reset</button>
+          <div class="message-controls">
+            <div> </div>
+            <div>
+              <div>
+                <button type="submit" disabled=${generating.value || talkActive.value}>Send</button>
+                <button disabled=${generating.value || talkActive.value} onclick=${uploadImage}>Upload Image</button>
+                <button onclick=${stop} disabled=${!generating.value}>Stop</button>
+                <button onclick=${reset}>Reset</button>
+              </div>
+              <div>
+                <a href="#" style="cursor: help;" title="Help" onclick=${e => {
+                  e.preventDefault();
+                  alert(`STT supported by your browser: ${SpeechRecognition ? 'Yes' : 'No'}\n` +
+                  `(TTS and speech recognition are not provided by llama.cpp)\n` +
+                  `Note: STT requires HTTPS to work.`);
+                }}>[?]</a>
+                <button disabled=${generating.value} onclick=${talkActive.value ? talkStop : talk}>${talkActive.value ? "Stop Talking" : "Talk"}</button>
+                <div>
+                  <input type="checkbox" id="send-on-talk" name="send-on-talk" checked="${sendOnTalk}" onchange=${(e) => sendOnTalk.value = e.target.checked} />
+                  <label for="send-on-talk" style="line-height: initial;">Send after talking</label>
+                </div>
+              </div>
+              <div>
+                <a href="#" style="cursor: help;" title="Help" onclick=${e => {
+                  e.preventDefault();
+                  alert(`TTS supported by your browser: ${tts ? 'Yes' : 'No'}\n(TTS and speech recognition are not provided by llama.cpp)`);
+                }}>[?]</a>
+                <label for="tts-voices" style="line-height: initial;">Bot Voice:</label>
+                <select id="tts-voices" name="tts-voices" onchange=${(e) => ttsVoice.value = e.target.value} style="max-width: 100px;">
+                  <option value="" selected="${!ttsVoice.value}">None</option>
+                  ${[
+                    ...(ttsVoiceDefault.value ? [ttsVoiceDefault.value] : []),
+                    ...ttsVoices.value.filter(v => !v.default),
+                  ].map(
+                    v => html`<option value="${v.name}" selected="${ttsVoice.value === v.name}">${v.name} (${v.lang}) ${v.default ? '(default)' : ''}</option>`
+                  )}
+                </select>
+              </div>
+            </div>
           </div>
         </form>
       `
@@ -659,26 +747,86 @@
         }
       }, [messages])
 
+      const ttsChatLineActiveIx = useSignal(undefined);
+      const ttsChatLine = (e, ix, msg) => {
+        if (e) e.preventDefault();
+
+        if (!tts || !ttsVoice.value || !('SpeechSynthesisUtterance' in window)) return;
+
+        const ttsVoices = tts.getVoices();
+        const voice = ttsVoices.find(v => v.name === ttsVoice.value);
+        if (!voice) return;
+
+        if (ttsChatLineActiveIx.value !== undefined) {
+          tts.cancel();
+          if (ttsChatLineActiveIx.value === ix) {
+            ttsChatLineActiveIx.value = undefined;
+            return;
+          }
+        }
+
+        ttsChatLineActiveIx.value = ix;
+        let ttsUtter = new SpeechSynthesisUtterance(msg);
+        ttsUtter.voice = voice;
+        ttsUtter.onend = e => {
+          ttsChatLineActiveIx.value = undefined;
+        };
+        tts.speak(ttsUtter);
+      }
+
       const isCompletionMode = session.value.type === 'completion'
+
+      // Try play the last bot message
+      const lastCharChatLinesIxs = useSignal([]);
+      const lastCharChatLinesIxsOld = useSignal([]);
+      useEffect(() => {
+        if (
+          !isCompletionMode
+          && lastCharChatLinesIxs.value.length !== lastCharChatLinesIxsOld.value.length
+          && !generating.value
+        ) {
+          const ix = lastCharChatLinesIxs.value[lastCharChatLinesIxs.value.length - 1];
+          if (ix !== undefined) {
+            const msg = messages[ix];
+            ttsChatLine(null, ix, Array.isArray(msg) ? msg[1].map(m => m.content).join('') : msg);
+          }
+
+          lastCharChatLinesIxsOld.value = structuredClone(lastCharChatLinesIxs.value);
+        }
+      }, [generating.value]);
+
       const chatLine = ([user, data], index) => {
         let message
-        const isArrayMessage = Array.isArray(data)
+        const isArrayMessage = Array.isArray(data);
+        const text = isArrayMessage ?
+            data.map(msg => msg.content).join('') :
+            data;
         if (params.value.n_probs > 0 && isArrayMessage) {
           message = html`<${Probabilities} data=${data} />`
         } else {
-          const text = isArrayMessage ?
-            data.map(msg => msg.content).join('') :
-            data;
           message = isCompletionMode ?
             text :
             html`<${Markdownish} text=${template(text)} />`
         }
+
+        const fromBot = user && user === '{{char}}';
+        if (fromBot && !lastCharChatLinesIxs.value.includes(index))
+          lastCharChatLinesIxs.value.push(index);
+
         if (user) {
-          return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>`
+          return html`
+          <div>
+            <p key=${index}><strong>${template(user)}:</strong> ${message}</p>
+            ${
+              fromBot && ttsVoice.value
+              && html`<button disabled=${generating.value} onclick=${e => ttsChatLine(e, index, text)} aria-label=${ttsChatLineActiveIx.value === index ? 'Pause' : 'Play'}>${ ttsChatLineActiveIx.value === index ? '⏸️' : '▶️' }</div>`
+            }
+          </div>
+          `;
         } else {
           return isCompletionMode ?
             html`<span key=${index}>${message}</span>` :
-            html`<p key=${index}>${message}</p>`
+            html`<div><p key=${index}>${message}</p></div>`
         }
       };