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* add quark quantizer * add quark doc * clean up doc * fix tests * make style * more style fixes * cleanup imports * cleaning * precise install * Update docs/source/en/quantization/quark.md Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * Update tests/quantization/quark_integration/test_quark.py Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * Update src/transformers/utils/quantization_config.py Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * remove import guard as suggested * update copyright headers * add quark to transformers-quantization-latest-gpu Dockerfile * make tests pass on transformers main + quark==0.7 * add missing F8_E4M3 and F8_E5M2 keys from str_to_torch_dtype --------- Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> Co-authored-by: Bowen Bao <bowenbao@amd.com> Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com> |
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| .. | ||
| transformers-all-latest-gpu | ||
| transformers-doc-builder | ||
| transformers-gpu | ||
| transformers-past-gpu | ||
| transformers-pytorch-amd-gpu | ||
| transformers-pytorch-deepspeed-amd-gpu | ||
| transformers-pytorch-deepspeed-latest-gpu | ||
| transformers-pytorch-deepspeed-nightly-gpu | ||
| transformers-pytorch-gpu | ||
| transformers-pytorch-tpu | ||
| transformers-quantization-latest-gpu | ||
| transformers-tensorflow-gpu | ||
| consistency.dockerfile | ||
| custom-tokenizers.dockerfile | ||
| examples-tf.dockerfile | ||
| examples-torch.dockerfile | ||
| exotic-models.dockerfile | ||
| jax-light.dockerfile | ||
| pipeline-tf.dockerfile | ||
| pipeline-torch.dockerfile | ||
| quality.dockerfile | ||
| README.md | ||
| tf-light.dockerfile | ||
| torch-jax-light.dockerfile | ||
| torch-light.dockerfile | ||
| torch-tf-light.dockerfile | ||
Dockers for transformers
In this folder you will find various docker files, and some subfolders.
- dockerfiles (ex:
consistency.dockerfile) present under~/dockerare used for our "fast" CIs. You should be able to use them for tasks that only need CPU. For exampletorch-lightis a very light weights container (703MiB). - subfloder contain dockerfiles used for our
slowCIs, which can be used for GPU tasks, but they are BIG as they were not specifically designed for a single model / single task. Thus the~/docker/transformers-pytorch-gpuincludes additional dependencies to allow us to run ALL model tests (saylibrosaortesseract, which you do not need to run LLMs)
Note that in both case, you need to run uv pip install -e ., which should take around 5 seconds. We do it outside the dockerfile for the need of our CI: we checkout a new branch each time, and the transformers code is thus updated.
We are open to contribution, and invite the community to create dockerfiles with potential arguments that properly choose extras depending on the model's dependencies! 🤗