text
stringlengths 0
696
|
|---|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
non_blocking,
|
^^^^^^^^^^^^^
|
)
|
^
|
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 14.69 MiB is free. Process 25490 has 22.28 GiB memory in use. Of the allocated memory 18.42 GiB is allocated by PyTorch, and 3.62 GiB 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)
|
Everything was good in inclusionAI_Ling-lite-base-1.5_1.txt
|
Traceback (most recent call last):
|
File "/tmp/inclusionAI_Ling-lite-base_0guz83Z.py", line 13, in <module>
|
pipe = pipeline("text-generation", model="inclusionAI/Ling-lite-base", trust_remote_code=True)
|
File "/tmp/.cache/uv/environments-v2/94d848f1d3db5c3e/lib/python3.13/site-packages/transformers/pipelines/__init__.py", line 1210, in pipeline
|
return pipeline_class(model=model, framework=framework, task=task, **kwargs)
|
File "/tmp/.cache/uv/environments-v2/94d848f1d3db5c3e/lib/python3.13/site-packages/transformers/pipelines/text_generation.py", line 121, in __init__
|
super().__init__(*args, **kwargs)
|
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
|
File "/tmp/.cache/uv/environments-v2/94d848f1d3db5c3e/lib/python3.13/site-packages/transformers/pipelines/base.py", line 1043, in __init__
|
self.model.to(self.device)
|
~~~~~~~~~~~~~^^^^^^^^^^^^^
|
File "/tmp/.cache/uv/environments-v2/94d848f1d3db5c3e/lib/python3.13/site-packages/transformers/modeling_utils.py", line 4333, in to
|
return super().to(*args, **kwargs)
|
~~~~~~~~~~^^^^^^^^^^^^^^^^^
|
File "/tmp/.cache/uv/environments-v2/94d848f1d3db5c3e/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1369, in to
|
return self._apply(convert)
|
~~~~~~~~~~~^^^^^^^^^
|
File "/tmp/.cache/uv/environments-v2/94d848f1d3db5c3e/lib/python3.13/site-packages/torch/nn/modules/module.py", line 928, in _apply
|
module._apply(fn)
|
~~~~~~~~~~~~~^^^^
|
File "/tmp/.cache/uv/environments-v2/94d848f1d3db5c3e/lib/python3.13/site-packages/torch/nn/modules/module.py", line 928, in _apply
|
module._apply(fn)
|
~~~~~~~~~~~~~^^^^
|
File "/tmp/.cache/uv/environments-v2/94d848f1d3db5c3e/lib/python3.13/site-packages/torch/nn/modules/module.py", line 928, in _apply
|
module._apply(fn)
|
~~~~~~~~~~~~~^^^^
|
[Previous line repeated 4 more times]
|
File "/tmp/.cache/uv/environments-v2/94d848f1d3db5c3e/lib/python3.13/site-packages/torch/nn/modules/module.py", line 955, in _apply
|
param_applied = fn(param)
|
File "/tmp/.cache/uv/environments-v2/94d848f1d3db5c3e/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1355, in convert
|
return t.to(
|
~~~~^
|
device,
|
^^^^^^^
|
dtype if t.is_floating_point() or t.is_complex() else None,
|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
non_blocking,
|
^^^^^^^^^^^^^
|
)
|
^
|
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 2.69 MiB is free. Process 25773 has 22.29 GiB memory in use. Of the allocated memory 18.46 GiB is allocated by PyTorch, and 3.59 GiB 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)
|
Everything was good in inclusionAI_Ling-lite-base_1.txt
|
Traceback (most recent call last):
|
File "/tmp/inclusionAI_Ling-lite_0sLFCL6.py", line 13, in <module>
|
pipe = pipeline("text-generation", model="inclusionAI/Ling-lite", trust_remote_code=True)
|
File "/tmp/.cache/uv/environments-v2/546d718608379bcd/lib/python3.13/site-packages/transformers/pipelines/__init__.py", line 1210, in pipeline
|
return pipeline_class(model=model, framework=framework, task=task, **kwargs)
|
File "/tmp/.cache/uv/environments-v2/546d718608379bcd/lib/python3.13/site-packages/transformers/pipelines/text_generation.py", line 121, in __init__
|
super().__init__(*args, **kwargs)
|
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
|
File "/tmp/.cache/uv/environments-v2/546d718608379bcd/lib/python3.13/site-packages/transformers/pipelines/base.py", line 1043, in __init__
|
self.model.to(self.device)
|
~~~~~~~~~~~~~^^^^^^^^^^^^^
|
File "/tmp/.cache/uv/environments-v2/546d718608379bcd/lib/python3.13/site-packages/transformers/modeling_utils.py", line 4333, in to
|
return super().to(*args, **kwargs)
|
~~~~~~~~~~^^^^^^^^^^^^^^^^^
|
File "/tmp/.cache/uv/environments-v2/546d718608379bcd/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1369, in to
|
return self._apply(convert)
|
~~~~~~~~~~~^^^^^^^^^
|
File "/tmp/.cache/uv/environments-v2/546d718608379bcd/lib/python3.13/site-packages/torch/nn/modules/module.py", line 928, in _apply
|
module._apply(fn)
|
~~~~~~~~~~~~~^^^^
|
File "/tmp/.cache/uv/environments-v2/546d718608379bcd/lib/python3.13/site-packages/torch/nn/modules/module.py", line 928, in _apply
|
module._apply(fn)
|
~~~~~~~~~~~~~^^^^
|
File "/tmp/.cache/uv/environments-v2/546d718608379bcd/lib/python3.13/site-packages/torch/nn/modules/module.py", line 928, in _apply
|
module._apply(fn)
|
~~~~~~~~~~~~~^^^^
|
[Previous line repeated 4 more times]
|
File "/tmp/.cache/uv/environments-v2/546d718608379bcd/lib/python3.13/site-packages/torch/nn/modules/module.py", line 955, in _apply
|
param_applied = fn(param)
|
File "/tmp/.cache/uv/environments-v2/546d718608379bcd/lib/python3.13/site-packages/torch/nn/modules/module.py", line 1355, in convert
|
return t.to(
|
~~~~^
|
device,
|
^^^^^^^
|
dtype if t.is_floating_point() or t.is_complex() else None,
|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
non_blocking,
|
^^^^^^^^^^^^^
|
)
|
^
|
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 14.69 MiB is free. Process 25489 has 22.28 GiB memory in use. Of the allocated memory 18.42 GiB is allocated by PyTorch, and 3.62 GiB 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)
|
Everything was good in inclusionAI_Ling-lite_1.txt
|
No suitable GPU found for inclusionAI/Ling-plus-base | 708.37 GB VRAM requirement
|
No suitable GPU found for inclusionAI/Ling-plus-base | 708.37 GB VRAM requirement
|
No suitable GPU found for inclusionAI/Ling-plus | 708.37 GB VRAM requirement
|
No suitable GPU found for inclusionAI/Ling-plus | 708.37 GB VRAM requirement
|
Traceback (most recent call last):
|
File "/tmp/jina-embeddings-v4_0sguDsi.py", line 12, in <module>
|
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v4", trust_remote_code=True)
|
File "/tmp/.cache/uv/environments-v2/3dff24d90291b0dd/lib/python3.13/site-packages/transformers/models/auto/auto_factory.py", line 582, in from_pretrained
|
model_class = get_class_from_dynamic_module(
|
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