diff --git a/sharktank/sharktank/examples/export_paged_llm_v1.py b/sharktank/sharktank/examples/export_paged_llm_v1.py
index 08faa51ec..d81e88f99 100644
--- a/sharktank/sharktank/examples/export_paged_llm_v1.py
+++ b/sharktank/sharktank/examples/export_paged_llm_v1.py
@@ -15,11 +15,11 @@
 from sharktank.types import *
 
 # TODO: Should be using a base class with the protocol supported.
-from sharktank.models.llama.llama import LlamaModelConfig, PagedLlamaModelV1
+from ..models.llama.llama import LlamaModelConfig, PagedLlamaModelV1
 
 
 def main():
-    from sharktank.utils import cli
+    from ..utils import cli
 
     parser = cli.create_parser()
     cli.add_input_dataset_options(parser)
@@ -49,7 +49,6 @@ def main():
     dataset_type = cli.get_input_data_files(args)
     dataset_type = "irpa" if "irpa" in dataset_type else "gguf"
     dataset = cli.get_input_dataset(args)
-    print('\n'.join([x for x in dataset.root_theta.flatten() if x.endswith(".weight")]))
 
     hp = configs.LlamaHParams.from_gguf_props(dataset.properties)
     llama_config = LlamaModelConfig(hp)
diff --git a/sharktank/sharktank/examples/paged_llm_v1.py b/sharktank/sharktank/examples/paged_llm_v1.py
index ba4c8adc6..a9d14195c 100644
--- a/sharktank/sharktank/examples/paged_llm_v1.py
+++ b/sharktank/sharktank/examples/paged_llm_v1.py
@@ -15,15 +15,14 @@
 
 import torch
 
-from sharktank.layers import *
-from sharktank.types import *
+from ..layers import *
+from ..types import *
 
 # TODO: Should be using a base class with the protocol supported.
-from sharktank.models.llama.llama import *
-from sharktank.utils.debugging import trace_tensor
-from sharktank.utils.tokenizer import InferenceTokenizer, load_tokenizer
-from sharktank.utils.patching import SaveModuleResultTensorsPatch
-from sharktank.models.punet.tools.sample_data import get_random_inputs, load_inputs, save_outputs
+from ..models.llama.llama import *
+from ..utils.debugging import trace_tensor
+from ..utils.tokenizer import InferenceTokenizer
+
 
 class TorchGenerator:
     """Generator that runs directly on the Torch model."""
@@ -50,15 +49,12 @@ def block_seq_stride(self) -> int:
         return self.model.cache.block_seq_stride
 
     def begin_batch(self, prompts: list[str]):
-        #token_ids, seq_lens = self.tokenizer.encode(
-        #    prompts, pad_to_multiple_of=self.model.cache.pad_sequence_stride
-        #)
-
-        #token_ids = torch.tensor(token_ids, device=self.model.device)
-        #seq_lens = torch.tensor(seq_lens, device=self.model.device)
-        with safe_open("/home/nod/batch.safetensors", framework="pt", device="cpu") as st:
-            token_ids=st.get_tensor("batch").to(device=self.model.device)
-        seq_lens = torch.tensor([2048]).to(device=self.model.device)
+        token_ids, seq_lens = self.tokenizer.encode(
+            prompts, pad_to_multiple_of=self.model.cache.pad_sequence_stride
+        )
+
+        token_ids = torch.tensor(token_ids, device=self.model.device)
+        seq_lens = torch.tensor(seq_lens, device=self.model.device)
         if self.shared_cache_state is not None:
             cache_state = self.shared_cache_state
         else:
@@ -264,23 +260,19 @@ def main():
         intermediates_saver.patch_child_modules(model)
     generator = TorchGenerator(model, tokenizer)
 
-
     print(f":: Prompting:")
     for prompt in prompts:
         print(f"    {prompt.encode()}")
-    
 
     batch = generator.begin_batch(prompts)
     print(f":: Prompt tokens: {batch.token_ids}")
     batch.prefill()
-    intermediates_saver.save_file("/home/nod/stank.safetensors")
     print(batch.detokenize())
 
     if args.save_intermediates_path:
         intermediates_saver.save_file(
             args.save_intermediates_path + "_prefill.safetensors"
         )
-    exit()
     counter = 0
     while not batch.done:
         batch.decode()
diff --git a/sharktank/sharktank/layers/configs/llm_configs.py b/sharktank/sharktank/layers/configs/llm_configs.py
index 5a829c6de..ab548e286 100644
--- a/sharktank/sharktank/layers/configs/llm_configs.py
+++ b/sharktank/sharktank/layers/configs/llm_configs.py
@@ -41,9 +41,6 @@ class LlamaHParams:
     attention_layer_norm_rms_epsilon: float
     attention_head_count_kv: int
 
-    #    @staticmethod
-    #    def from_hf_props(p: dict[str, Any]):
-
     @staticmethod
     def from_gguf_props(p: dict[str, Any]):
         attention_head_count = _int_prop(p, "llama.attention.head_count")
diff --git a/sharktank/sharktank/layers/kv_cache.py b/sharktank/sharktank/layers/kv_cache.py
index 2bf1a0725..319f2353c 100644
--- a/sharktank/sharktank/layers/kv_cache.py
+++ b/sharktank/sharktank/layers/kv_cache.py
@@ -100,7 +100,6 @@ def __init__(
         self.seq_length = seq_length
         self.device = device
         self.dtype = dtype
-        print("cache dtype = ", dtype)
 
     @property
     def pad_sequence_stride(self) -> int:
diff --git a/sharktank/sharktank/layers/linear.py b/sharktank/sharktank/layers/linear.py
index d5f6d8a3b..926664d18 100644
--- a/sharktank/sharktank/layers/linear.py
+++ b/sharktank/sharktank/layers/linear.py
@@ -7,11 +7,8 @@
 from typing import Optional
 
 import torch
-from safetensors.torch import save_file
-from torch.nn import functional as F
 from .. import ops
 from .base import Theta, ThetaLayer
-from ..types.layout_utils import saturate_cast
 from ..types import (
     DynamicScaledQuantizer,
     QuantizedTensor,
@@ -44,7 +41,7 @@ def __init__(
     ):
         super().__init__(theta)
         self._simulate_native_quant = True
-        self.weight = self.theta_tensor(weight_name)  # .to(device="cuda:0")
+        self.weight = self.theta_tensor(weight_name)
         self.bias = None
         if bias_name in self.theta.keys:
             self.bias = self.theta_tensor(bias_name)
@@ -73,9 +70,9 @@ def forward(self, x):
             # TODO: probably need a way to only do q_input if exporting.
             print("qdq input")
             x = qdq_input.quantize(x).unpack().dequant()
-        # from torch.nn import functional as F
 
         y = ops.linear(x, weight, bias)
+
         # Unconditionally dequantize.
         # TODO: Support a q_output specifier that signals the layer to let
         # the QuantizedTensor escape.
diff --git a/sharktank/sharktank/layers/norm.py b/sharktank/sharktank/layers/norm.py
index 04eafaaad..68818a2d7 100644
--- a/sharktank/sharktank/layers/norm.py
+++ b/sharktank/sharktank/layers/norm.py
@@ -8,7 +8,6 @@
 
 from .. import ops
 from .base import Theta, ThetaLayer
-from safetensors.torch import save_file
 
 
 class RMSNormLayer(ThetaLayer):
@@ -35,9 +34,6 @@ def __init__(
 
     def forward(self, x: torch.Tensor):
         orig_dtype = x.dtype
-        print("norm dtype: ", self.dtype)
-        print("orgi_dtype: ", orig_dtype)
-
         x = x.to(self.dtype)
         norm = ops.rms_norm(x, self.weight, epsilon=self.epsilon)
         # Will automatically upcast to the dtype of the weight, which is
diff --git a/sharktank/sharktank/models/llama/llama.py b/sharktank/sharktank/models/llama/llama.py
index 0d7b717ee..ff3d9ed1a 100644
--- a/sharktank/sharktank/models/llama/llama.py
+++ b/sharktank/sharktank/models/llama/llama.py
@@ -8,7 +8,6 @@
 
 from dataclasses import dataclass
 import math
-from safetensors.torch import safe_open, save_file
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -36,7 +35,7 @@ class LlamaModelConfig:
     block_seq_stride: int = 16
 
     # Either "paged" or "direct".
-    kv_cache_type: str = "direct"
+    kv_cache_type: str = "paged"
 
     # The device on which to place intermediate state.
     device: Optional[torch.device] = None
@@ -114,17 +113,15 @@ def __init__(self, theta: Theta, config: LlamaModelConfig):
             activation_dtype=config.activation_dtype,
             attention_dtype=config.attention_dtype,
         )
-        self.hf = False
         self.config = config
         self.hp = hp
         self.cache = config.create_kv_cache()
         self.activation_dtype = config.activation_dtype
         self.use_hf = config.use_hf
 
-        key = "token_embd"
         self.add_module(
             "token_embedding",
-            TokenEmbeddingLayer(theta(key), dtype=config.activation_dtype),
+            TokenEmbeddingLayer(theta("token_embd"), dtype=config.activation_dtype),
         )
         self.add_module(
             "attention_embedding",
diff --git a/sharktank/sharktank/models/llama/tools/import_quark_dataset.py b/sharktank/sharktank/models/llama/tools/import_quark_dataset.py
index 3ef763779..fcf38571b 100644
--- a/sharktank/sharktank/models/llama/tools/import_quark_dataset.py
+++ b/sharktank/sharktank/models/llama/tools/import_quark_dataset.py
@@ -4,20 +4,14 @@
 # See https://llvm.org/LICENSE.txt for license information.
 # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 
-"""Imports Brevitas pre-processed weights and quantization config into a
-Dataset.
+"""Imports quark pre-processed weights and quantization config into a
+Dataset of the gguf format.
 
 Usage:
-  python -m sharktank.models.punet.import_hf_dataset \
-    --output-irpa-file ~/models/punet/punet_fp16.irpa \
-    --config-json ~/models/stable-diffusion-xl-base-1.0/unet/config.json
+  python -m sharktank.models.llama.tools.import_quark_dataset \
+    --params=llama2-7b-fp8.safetensors --output-irpa-file=new.irpa \
+    --config-json=../llama2/config.json
 
-The resulting dataset has all tensors as nested in the original model.
-Properties are separated into a "meta" dict (for "_" prefixed props) and an
-"hparams" dict.
-
-Default flag values assume that there is a quant_param.json and
-params.safetensors adjacent to the HF config.json file.
 """
 from typing import Optional
 
diff --git a/sharktank/sharktank/ops/default_impls.py b/sharktank/sharktank/ops/default_impls.py
index d8a582035..a61f35591 100644
--- a/sharktank/sharktank/ops/default_impls.py
+++ b/sharktank/sharktank/ops/default_impls.py
@@ -182,10 +182,11 @@ def scaled_dot_product_attention(q, k, v, a) -> Tensor:
 @rms_norm.override(Tensor, Tensor)
 def rms_norm_default(x, weight, *, epsilon: float) -> Tensor:
     x = unbox_tensor(x)
-    weight = unbox_tensor(weight).to(device=x.device)
+    weight = unbox_tensor(weight)
     variance = x.pow(2).mean(-1, keepdim=True)
     output = x * torch.rsqrt(variance + epsilon)
-    output = weight * output.to(torch.float16)
+    # The cast here is to match the hf implementation, affects numerics
+    output = weight * output.to(weight.dtype)
     return output
 
 
diff --git a/sharktank/sharktank/types/quantizers.py b/sharktank/sharktank/types/quantizers.py
index bd976c7dc..6dcc35140 100644
--- a/sharktank/sharktank/types/quantizers.py
+++ b/sharktank/sharktank/types/quantizers.py
@@ -139,8 +139,7 @@ def _quantize_raw_tensor(self, t: torch.Tensor, *, name: str) -> QuantizedTensor
         if axis is None:
             # Per tensor.
             if offset is None:
-                print(self._scale)
-                print(self.dtype)
+                # Changed to t/reciprocal because narrow float types are garbage
                 qs = saturate_cast(
                     t / self._reciprocal_scale,
                     dtype=self.dtype,
@@ -148,7 +147,7 @@ def _quantize_raw_tensor(self, t: torch.Tensor, *, name: str) -> QuantizedTensor
                 )
             else:
                 qs = saturate_cast(
-                    t * self._scale + offset,
+                    t / self._reciprocal_scale + offset,
                     dtype=self.dtype,
                     disable_saturate=self._disable_saturate,
                 )