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[Bug]: request reward model report 500 Internal Server Error #10444

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hrdxwandg opened this issue Nov 19, 2024 · 7 comments
Open
1 task done

[Bug]: request reward model report 500 Internal Server Error #10444

hrdxwandg opened this issue Nov 19, 2024 · 7 comments
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bug Something isn't working

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@hrdxwandg
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Your current environment

The output of `python collect_env.py` 
Your output of `python collect_env.py` here

Model Input Dumps

No response

🐛 Describe the bug

following the instruction #8700 (comment) , the reward model https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward-HF is a LlamaForCausalLM model, so I serve it with vllm add parameter --task embedding.

when I send a request, it encounter en error:
INFO: "POST /v1/embeddings HTTP/1.1" 500 Internal Server Error
ERROR 11-19 17:55:16 engine.py:135] TypeError("object of type 'NoneType' has no len()")
and then the server terminated

the shell script:

curl http://host:port/v1/embeddings \  
        -H "Content-Type: application/json" \
        -d '{
         "model": "Llama-3.1-Nemotron-70B-Reward-HF",
         "input": "Your text string goes here"                                                      
}'

or use python code same as https://github.com/vllm-project/vllm/blob/main/examples/openai_embedding_client.py

has the same error

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@hrdxwandg hrdxwandg added the bug Something isn't working label Nov 19, 2024
@hrdxwandg hrdxwandg mentioned this issue Nov 19, 2024
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@DarkLight1337
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Which version of vLLM are you using? Also please post the full stack trace.

@hrdxwandg
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Which version of vLLM are you using? Also please post the full stack trace.

vllm==0.6.4.post1

INFO:     172.16.8.237:39200 - "POST /v1/embeddings HTTP/1.1" 500 Internal Server Error
ERROR 11-19 19:08:00 engine.py:135] TypeError("object of type 'NoneType' has no len()")
ERROR 11-19 19:08:00 engine.py:135] Traceback (most recent call last):
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/engine/multiprocessing/engine.py", line 133, in start
ERROR 11-19 19:08:00 engine.py:135]     self.run_engine_loop()
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/engine/multiprocessing/engine.py", line 196, in run_engine_loop
ERROR 11-19 19:08:00 engine.py:135]     request_outputs = self.engine_step()
ERROR 11-19 19:08:00 engine.py:135]                       ^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/engine/multiprocessing/engine.py", line 214, in engine_step
ERROR 11-19 19:08:00 engine.py:135]     raise e
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/engine/multiprocessing/engine.py", line 205, in engine_step
ERROR 11-19 19:08:00 engine.py:135]     return self.engine.step()
ERROR 11-19 19:08:00 engine.py:135]            ^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/engine/llm_engine.py", line 1454, in step
ERROR 11-19 19:08:00 engine.py:135]     outputs = self.model_executor.execute_model(
ERROR 11-19 19:08:00 engine.py:135]               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/executor/distributed_gpu_executor.py", line 82, in execute_model
ERROR 11-19 19:08:00 engine.py:135]     driver_outputs = self._driver_execute_model(execute_model_req)
ERROR 11-19 19:08:00 engine.py:135]                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/executor/multiproc_gpu_executor.py", line 158, in _driver_execute_model
ERROR 11-19 19:08:00 engine.py:135]     return self.driver_worker.execute_model(execute_model_req)
ERROR 11-19 19:08:00 engine.py:135]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/worker/worker_base.py", line 319, in execute_model
ERROR 11-19 19:08:00 engine.py:135]     inputs = self.prepare_input(execute_model_req)
ERROR 11-19 19:08:00 engine.py:135]              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/worker/worker_base.py", line 307, in prepare_input
ERROR 11-19 19:08:00 engine.py:135]     return self._get_driver_input_and_broadcast(execute_model_req)
ERROR 11-19 19:08:00 engine.py:135]            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/worker/worker_base.py", line 269, in _get_driver_input_and_broadcast
ERROR 11-19 19:08:00 engine.py:135]     self.model_runner.prepare_model_input(
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/worker/embedding_model_runner.py", line 158, in prepare_model_input
ERROR 11-19 19:08:00 engine.py:135]     model_input = self._prepare_model_input_tensors(
ERROR 11-19 19:08:00 engine.py:135]                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/worker/model_runner.py", line 1206, in _prepare_model_input_tensors
ERROR 11-19 19:08:00 engine.py:135]     return builder.build()  # type: ignore
ERROR 11-19 19:08:00 engine.py:135]            ^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/worker/model_runner.py", line 896, in build
ERROR 11-19 19:08:00 engine.py:135]     attn_metadata = self.attn_metadata_builder.build(
ERROR 11-19 19:08:00 engine.py:135]                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/attention/backends/flash_attn.py", line 522, in build
ERROR 11-19 19:08:00 engine.py:135]     block_tables = make_tensor_with_pad(
ERROR 11-19 19:08:00 engine.py:135]                    ^^^^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/utils.py", line 806, in make_tensor_with_pad
ERROR 11-19 19:08:00 engine.py:135]     padded_x = make_ndarray_with_pad(x, pad, np_dtype, max_len=max_len)
ERROR 11-19 19:08:00 engine.py:135]                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135]   File "xxx/lib/python3.11/site-packages/vllm/utils.py", line 780, in make_ndarray_with_pad
ERROR 11-19 19:08:00 engine.py:135]     max_len = max(map(len, x), default=0)
ERROR 11-19 19:08:00 engine.py:135]               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
ERROR 11-19 19:08:00 engine.py:135] TypeError: object of type 'NoneType' has no len()
INFO:     Shutting down
INFO:     Waiting for application shutdown.
INFO:     Application shutdown complete.
INFO:     Finished server process [256667]
Process SpawnProcess-1:
Traceback (most recent call last):
  File "xxx/lib/python3.11/multiprocessing/process.py", line 317, in _bootstrap
    util._exit_function()
  File "xxx/lib/python3.11/multiprocessing/util.py", line 360, in _exit_function
    p.join()
  File "xxx/lib/python3.11/multiprocessing/process.py", line 149, in join
    res = self._popen.wait(timeout)
          ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "xxx/lib/python3.11/multiprocessing/popen_fork.py", line 43, in wait
    return self.poll(os.WNOHANG if timeout == 0.0 else 0)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "xxx/lib/python3.11/multiprocessing/popen_fork.py", line 27, in poll
    pid, sts = os.waitpid(self.pid, flag)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "xxx/lib/python3.11/site-packages/vllm/engine/multiprocessing/engine.py", line 351, in signal_handler
    raise KeyboardInterrupt("MQLLMEngine terminated")
KeyboardInterrupt: MQLLMEngine terminated
INFO 11-19 19:08:00 multiproc_worker_utils.py:133] Terminating local vLLM worker processes
wangdong@ai-t1 ~> xxx/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '

@DarkLight1337
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Can you post the full output of collect_env.py? And does this happen with any other model?

@DarkLight1337
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Please also show the full command you used to launch the server.

@hrdxwandg
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Please also show the full command you used to launch the server.

CUDA_VISIBLE_DEVICES="4,5,6,7" python -u -m vllm.entrypoints.openai.api_server --task embedding --host 0.0.0.0 --port 8099 --model xxx/Llama-3.1-Nemotron-70B-Reward-HF --served-model-name Llama-3.1-Nemotron-70B-Reward-HF --tensor-parallel-size 4 --disable-log-requests --enable-prefix-caching

@hrdxwandg
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Can you post the full output of collect_env.py? And does this happen with any other model?

i do not test other model yet.

Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.11.9 (main, Apr 19 2024, 16:48:06) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-58-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB
GPU 4: NVIDIA A100-SXM4-80GB
GPU 5: NVIDIA A100-SXM4-80GB
GPU 6: NVIDIA A100-SXM4-80GB
GPU 7: NVIDIA A100-SXM4-80GB

Nvidia driver version: 555.42.02
cuDNN version: Probably one of the following:
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn.so.9.1.1
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_adv.so.9.1.1
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_cnn.so.9.1.1
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_engines_precompiled.so.9.1.1
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_engines_runtime_compiled.so.9.1.1
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_graph.so.9.1.1
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_heuristic.so.9.1.1
/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_ops.so.9.1.1
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn.so.9.1.1
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_adv.so.9.1.1
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_cnn.so.9.1.1
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_engines_precompiled.so.9.1.1
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_engines_runtime_compiled.so.9.1.1
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_graph.so.9.1.1
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_heuristic.so.9.1.1
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_ops.so.9.1.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   46 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          128
On-line CPU(s) list:             0-127
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
Stepping:                        6
BogoMIPS:                        5799.99
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm arch_capabilities
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       3 MiB (64 instances)
L1i cache:                       2 MiB (64 instances)
L2 cache:                        80 MiB (64 instances)
L3 cache:                        96 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-63
NUMA node1 CPU(s):               64-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] onnx==1.16.0
[pip3] pyzmq==25.1.2
[pip3] sentence-transformers==2.2.2
[pip3] torch==2.5.1
[pip3] torch-tb-profiler==0.4.3
[pip3] torchaudio==2.3.0
[pip3] torchvision==0.20.1
[pip3] transformers==4.47.0.dev0
[pip3] triton==3.1.0
[pip3] vllm-nccl-cu12==2.18.1.0.4.0
[conda] blas                      1.0                         mkl    conda-forge
[conda] cuda-cudart               11.8.89                       0    nvidia
[conda] cuda-cupti                11.8.87                       0    nvidia
[conda] cuda-libraries            11.8.0                        0    nvidia
[conda] cuda-nvrtc                11.8.89                       0    nvidia
[conda] cuda-nvtx                 11.8.86                       0    nvidia
[conda] cuda-opencl               12.4.127                      0    nvidia
[conda] cuda-runtime              11.8.0                        0    nvidia
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libcublas                 11.11.3.6                     0    nvidia
[conda] libcufft                  10.9.0.58                     0    nvidia
[conda] libcufile                 1.9.1.3                       0    nvidia
[conda] libcurand                 10.3.5.147                    0    nvidia
[conda] libcusolver               11.4.1.48                     0    nvidia
[conda] libcusparse               11.7.5.86                     0    nvidia
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] libnpp                    11.8.0.86                     0    nvidia
[conda] libnvfatbin               12.4.127                      0    nvidia
[conda] libnvjitlink              12.4.99                       0    nvidia
[conda] libnvjpeg                 11.9.0.86                     0    nvidia
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0           py311h5eee18b_1  
[conda] mkl_fft                   1.3.8           py311h5eee18b_0  
[conda] mkl_random                1.2.4           py311hdb19cb5_0  
[conda] numpy                     1.26.4          py311h08b1b3b_0  
[conda] numpy-base                1.26.4          py311hf175353_0  
[conda] nvidia-cublas-cu12        12.4.5.8                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.4.127                 pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.4.127                 pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.4.127                 pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.1.0.70                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.2.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.5.147               pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.6.1.9                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.3.1.170               pypi_0    pypi
[conda] nvidia-ml-py              12.560.30                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.21.5                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.4.127                 pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.4.127                 pypi_0    pypi
[conda] pytorch-cuda              11.8                 h7e8668a_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] pyzmq                     25.1.2          py311h6a678d5_0  
[conda] sentence-transformers     2.2.2                    pypi_0    pypi
[conda] torch                     2.5.1                    pypi_0    pypi
[conda] torch-tb-profiler         0.4.3                    pypi_0    pypi
[conda] torchaudio                2.3.0               py311_cu118    pytorch
[conda] torchvision               0.20.1                   pypi_0    pypi
[conda] transformers              4.47.0.dev0              pypi_0    pypi
[conda] triton                    3.1.0                    pypi_0    pypi
[conda] vllm-nccl-cu12            2.18.1.0.4.0             pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.4.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	NV12	NV12	NV12	NV12	NV12	NV12	NV12	0-127	0-1		N/A
GPU1	NV12	 X 	NV12	NV12	NV12	NV12	NV12	NV12	0-127	0-1		N/A
GPU2	NV12	NV12	 X 	NV12	NV12	NV12	NV12	NV12	0-127	0-1		N/A
GPU3	NV12	NV12	NV12	 X 	NV12	NV12	NV12	NV12	0-127	0-1		N/A
GPU4	NV12	NV12	NV12	NV12	 X 	NV12	NV12	NV12	0-127	0-1		N/A
GPU5	NV12	NV12	NV12	NV12	NV12	 X 	NV12	NV12	0-127	0-1		N/A
GPU6	NV12	NV12	NV12	NV12	NV12	NV12	 X 	NV12	0-127	0-1		N/A
GPU7	NV12	NV12	NV12	NV12	NV12	NV12	NV12	 X 	0-127	0-1		N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

LD_LIBRARY_PATH=xx/lib/python3.11/site-packages/cv2/../../lib64:/home/xxx/lib
CUDA_MODULE_LOADING=LAZY

@DarkLight1337
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Can you try running other models? Unfortunately I can't directly debug this as I don't have enough GPU memory to load 70B models.

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