Jetson orin NX 8GB Jetpack 6.1 L4T_VERSION=36.4.0
When I run jetson-containers run $(autotag nano_llm) python3 -m nano_llm.chat --api=mlc --model princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT
I get the error
sing path “/data/models/mlc/dist/models/Sheared-LLaMA-2.7B-ShareGPT” for model “Sheared-LLaMA-2.7B-ShareGPT”
Target configured: cuda -keys=cuda,gpu -arch=sm_87 -max_num_threads=1024 -max_shared_memory_per_block=49152 -max_threads_per_block=1024 -registers_per_block=65536 -thread_warp_size=32
Automatically using target for weight quantization: cuda -keys=cuda,gpu -arch=sm_87 -max_num_threads=1024 -max_shared_memory_per_block=49152 -max_threads_per_block=1024 -registers_per_block=65536 -thread_warp_size=32
Get old param: 0%| | 0/197 [00:00<?, ?tensors/sStart computing and quantizing weights… This may take a while.00<?, ?tensors/s]
Get old param: 1%|▏ | 2/197 [00:02<03:55, 1.21s/tensors]/usr/local/lib/python3.10/dist-packages/mlc_llm/relax_model/param_manager.py:587: FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See pytorch/SECURITY.md at main · pytorch/pytorch · GitHub for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don’t have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
torch_params = torch.load(
Traceback (most recent call last):
File “/usr/lib/python3.10/runpy.py”, line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File “/usr/lib/python3.10/runpy.py”, line 86, in _run_code
exec(code, run_globals)
File “/opt/NanoLLM/nano_llm/chat/main.py”, line 32, in
model = NanoLLM.from_pretrained(
File “/opt/NanoLLM/nano_llm/nano_llm.py”, line 91, in from_pretrained
model = MLCModel(model_path, **kwargs)
File “/opt/NanoLLM/nano_llm/models/mlc.py”, line 60, in init
quant = MLCModel.quantize(self.model_path, self.config, method=quantization, max_context_len=max_context_len, **kwargs)
File “/opt/NanoLLM/nano_llm/models/mlc.py”, line 276, in quantize
subprocess.run(cmd, executable=‘/bin/bash’, shell=True, check=True)
File “/usr/lib/python3.10/subprocess.py”, line 526, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command 'python3 -m mlc_llm.build --model /data/models/mlc/dist/models/Sheared-LLaMA-2.7B-ShareGPT --quantization q4f16_ft --target cuda --use-cuda-graph --use-flash-attn-mqa --sep-embed --max-seq-len 4096 --artifact-path /data/models/mlc/dist/Sheared-LLaMA-2.7B-ShareGPT/ctx4096 ’ died with <Signals.SIGKILL: 9>.