Upload 2026-01-14/runs/26774-20999632595/ci_results_run_models_gpu/model_results.json with huggingface_hub
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2026-01-14/runs/26774-20999632595/ci_results_run_models_gpu/model_results.json
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"trace": "(line 575) AssertionError: Tensor-likes are not close!"
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"line": "tests/models/detr/test_modeling_detr.py::DetrModelIntegrationTestsTimmBackbone::test_inference_object_detection_head",
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"trace": "(line 609) AssertionError: Tensor-likes are not close!"
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{
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"line": "tests/models/detr/test_modeling_detr.py::DetrModelIntegrationTestsTimmBackbone::test_inference_panoptic_segmentation_head",
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"trace": "(line 667) AssertionError: Tensor-likes are not close!"
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},
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{
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"line": "tests/models/detr/test_modeling_detr.py::DetrModelIntegrationTests::test_inference_no_head",
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"trace": "(line 781) AssertionError: Tensor-likes are not close!"
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"line": "tests/models/detr/test_modeling_detr.py::DetrModelIntegrationTestsTimmBackbone::test_inference_no_head",
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"trace": "(line 575) AssertionError: Tensor-likes are not close!"
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},
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{
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"line": "tests/models/detr/test_modeling_detr.py::DetrModelIntegrationTestsTimmBackbone::test_inference_object_detection_head",
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"trace": "(line 609) AssertionError: Tensor-likes are not close!"
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{
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"line": "tests/models/detr/test_modeling_detr.py::DetrModelIntegrationTestsTimmBackbone::test_inference_panoptic_segmentation_head",
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"trace": "(line 667) AssertionError: Tensor-likes are not close!"
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},
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{
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"line": "tests/models/detr/test_modeling_detr.py::DetrModelIntegrationTests::test_inference_no_head",
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"trace": "(line 781) AssertionError: Tensor-likes are not close!"
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| 90 |
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"line": "tests/models/llava/test_modeling_llava.py::LlavaForConditionalGenerationIntegrationTest::test_pixtral",
|
| 213 |
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"trace": "(line 692) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 140.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 92.69 MiB is free. Process 33037 has 22.21 GiB memory in use. Of the allocated memory 21.81 GiB is allocated by PyTorch, and 8.00 MiB 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)"
|
| 214 |
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|
| 215 |
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|
| 216 |
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"line": "tests/models/llava/test_modeling_llava.py::LlavaForConditionalGenerationIntegrationTest::test_pixtral_4bit",
|
| 217 |
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"trace": "(line 692) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.25 GiB. GPU 0 has a total capacity of 22.30 GiB of which 82.69 MiB is free. Process 33037 has 22.21 GiB memory in use. Of the allocated memory 21.82 GiB is allocated by PyTorch, and 7.99 MiB 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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}
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{
|
| 222 |
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"line": "tests/models/llava/test_modeling_llava.py::LlavaForConditionalGenerationIntegrationTest::test_pixtral",
|
| 223 |
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"trace": "(line 692) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 40.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 14.69 MiB is free. Process 44596 has 22.28 GiB memory in use. Of the allocated memory 21.77 GiB is allocated by PyTorch, and 6.88 MiB 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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},
|
| 225 |
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{
|
| 226 |
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"line": "tests/models/llava/test_modeling_llava.py::LlavaForConditionalGenerationIntegrationTest::test_pixtral_4bit",
|
| 227 |
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"trace": "(line 692) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.25 GiB. GPU 0 has a total capacity of 22.30 GiB of which 4.69 MiB is free. Process 44596 has 22.29 GiB memory in use. Of the allocated memory 21.78 GiB is allocated by PyTorch, and 6.86 MiB 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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}
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]
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"multi": "https://github.com/huggingface/transformers/actions/runs/20999632595/job/60366001229"
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},
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| 235 |
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"captured_info": {}
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| 236 |
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}
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}
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