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* rebasing changes * fixing style * adding some doc to functions * remove bitblas * change dtype * fixing check_code_quality * fixing import order * adding doc to tree * Small update on BitLinear * adding some tests * sorting imports * small update * reformatting * reformatting * reformatting with ruff * adding assert * changes after review * update disk offloading * adapting after review * Update after review * add is_serializable back * fixing style * adding serialization test * make style * small updates after review
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Quantization
Quantization techniques reduce 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.
QuantoConfig
autodoc QuantoConfig
AqlmConfig
autodoc AqlmConfig
AwqConfig
autodoc AwqConfig
EetqConfig
autodoc EetqConfig
GPTQConfig
autodoc GPTQConfig
BitsAndBytesConfig
autodoc BitsAndBytesConfig
HfQuantizer
autodoc quantizers.base.HfQuantizer
HqqConfig
autodoc HqqConfig
FbgemmFp8Config
autodoc FbgemmFp8Config
CompressedTensorsConfig
autodoc CompressedTensorsConfig
TorchAoConfig
autodoc TorchAoConfig
BitNetConfig
autodoc BitNetConfig