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Upload EssentialAI_rnj-1-instruct_0.txt with huggingface_hub

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  1. EssentialAI_rnj-1-instruct_0.txt +2 -2
EssentialAI_rnj-1-instruct_0.txt CHANGED
@@ -11,7 +11,7 @@ pipe(messages)
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  ERROR:
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  Traceback (most recent call last):
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- File "/tmp/EssentialAI_rnj-1-instruct_0PTKveY.py", line 26, in <module>
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  pipe = pipeline("text-generation", model="EssentialAI/rnj-1-instruct")
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  File "/tmp/.cache/uv/environments-v2/9ed1d8af37d02702/lib/python3.13/site-packages/transformers/pipelines/__init__.py", line 1229, in pipeline
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  return pipeline_class(model=model, framework=framework, task=task, **kwargs)
@@ -50,4 +50,4 @@ Traceback (most recent call last):
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  ^^^^^^^^^^^^^
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  )
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  ^
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- torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 62.69 MiB is free. Process 2506836 has 22.23 GiB memory in use. Of the allocated memory 21.99 GiB is allocated by PyTorch, and 617.50 KiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)
 
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  ERROR:
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  Traceback (most recent call last):
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+ File "/tmp/EssentialAI_rnj-1-instruct_01TjqEM.py", line 26, in <module>
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  pipe = pipeline("text-generation", model="EssentialAI/rnj-1-instruct")
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  File "/tmp/.cache/uv/environments-v2/9ed1d8af37d02702/lib/python3.13/site-packages/transformers/pipelines/__init__.py", line 1229, in pipeline
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  return pipeline_class(model=model, framework=framework, task=task, **kwargs)
 
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  ^^^^^^^^^^^^^
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  )
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  ^
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+ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 64.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 62.69 MiB is free. Process 3735213 has 22.23 GiB memory in use. Of the allocated memory 21.99 GiB is allocated by PyTorch, and 617.50 KiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)