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* aqlm init * calibration and dtypes * docs * Readme update * is_aqlm_available * Simpler link in docs * Test TODO real reference * init _import_structure fix * AqlmConfig autodoc * integration aqlm * integrations in tests * docstring fix * legacy typing * Less typings * More kernels information * Performance -> Accuracy * correct tests * remoced multi-gpu test * Update docs/source/en/quantization.md Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> * Update src/transformers/utils/quantization_config.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Brought back multi-gpu tests * Update src/transformers/integrations/aqlm.py Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> * Update tests/quantization/aqlm_integration/test_aqlm.py Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com> --------- Co-authored-by: Andrei Panferov <blacksamorez@yandex-team.ru> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
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Quantization
Quantization techniques reduces memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Transformers supports the AWQ and GPTQ quantization algorithms and it supports 8-bit and 4-bit quantization with bitsandbytes.
Quantization techniques that aren't supported in Transformers can be added with the [HfQuantizer
] class.
Learn how to quantize models in the Quantization guide.
AqlmConfig
autodoc AqlmConfig
AwqConfig
autodoc AwqConfig
GPTQConfig
autodoc GPTQConfig
BitsAndBytesConfig
autodoc BitsAndBytesConfig
HfQuantizer
autodoc quantizers.base.HfQuantizer