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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.

<Tip>

Learn how to quantize models in the Quantization guide.

</Tip>

QuantoConfig

[[autodoc]] QuantoConfig

AqlmConfig

[[autodoc]] AqlmConfig

VptqConfig

[[autodoc]] VptqConfig

AwqConfig

[[autodoc]] AwqConfig

EetqConfig

[[autodoc]] EetqConfig

GPTQConfig

[[autodoc]] GPTQConfig

BitsAndBytesConfig

[[autodoc]] BitsAndBytesConfig

HfQuantizer

[[autodoc]] quantizers.base.HfQuantizer

HiggsConfig

[[autodoc]] HiggsConfig

HqqConfig

[[autodoc]] HqqConfig

MetalConfig

[[autodoc]] MetalConfig

Mxfp4Config

[[autodoc]] Mxfp4Config

FbgemmFp8Config

[[autodoc]] FbgemmFp8Config

CompressedTensorsConfig

[[autodoc]] CompressedTensorsConfig

TorchAoConfig

[[autodoc]] TorchAoConfig

BitNetQuantConfig

[[autodoc]] BitNetQuantConfig

SpQRConfig

[[autodoc]] SpQRConfig

FineGrainedFP8Config

[[autodoc]] FineGrainedFP8Config

QuarkConfig

[[autodoc]] QuarkConfig

FourOverSixConfig

[[autodoc]] FourOverSixConfig

FPQuantConfig

[[autodoc]] FPQuantConfig

AutoRoundConfig

[[autodoc]] AutoRoundConfig

SinqConfig

[[autodoc]] SinqConfig