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transformers/docs/source/en/main_classes/quantization.md
Wenhua Cheng b3492ff9f7
Add AutoRound quantization support (#37393) 
* add auto-round support

* Update src/transformers/quantizers/auto.py

Co-authored-by: Ilyas Moutawwakil <57442720+IlyasMoutawwakil@users.noreply.github.com>

* fix style issue

Signed-off-by: wenhuach <wenhuach87@gmail.com>

* tiny change

* tiny change

* refine ut and doc

* revert unnecessary change

* tiny change

* try to fix style issue

* try to fix style issue

* try to fix style issue

* try to fix style issue

* try to fix style issue

* try to fix style issue

* try to fix style issue

* fix doc issue

* Update tests/quantization/autoround/test_auto_round.py

* fix comments

* Update tests/quantization/autoround/test_auto_round.py

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* Update tests/quantization/autoround/test_auto_round.py

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* update doc

* Update src/transformers/quantizers/quantizer_auto_round.py

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* update

* update

* fix

* try to fix style issue

* Update src/transformers/quantizers/auto.py

Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>

* Update docs/source/en/quantization/auto_round.md

Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>

* Update docs/source/en/quantization/auto_round.md

Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>

* Update docs/source/en/quantization/auto_round.md

Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>

* update

* fix style issue

* update doc

* update doc

* Refine the doc

* refine doc

* revert one change

* set sym to True by default

* Enhance the unit test's robustness.

* update

* add torch dtype

* tiny change

* add awq convert test

* fix typo

* update

* fix packing format issue

* use one gpu

---------

Signed-off-by: wenhuach <wenhuach87@gmail.com>
Co-authored-by: Ilyas Moutawwakil <57442720+IlyasMoutawwakil@users.noreply.github.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
Co-authored-by: Shen, Haihao <haihao.shen@intel.com>
2025-04-22 13:56:54 +02:00

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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

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

FbgemmFp8Config

autodoc FbgemmFp8Config

CompressedTensorsConfig

autodoc CompressedTensorsConfig

TorchAoConfig

autodoc TorchAoConfig

BitNetConfig

autodoc BitNetConfig

SpQRConfig

autodoc SpQRConfig

FineGrainedFP8Config

autodoc FineGrainedFP8Config

QuarkConfig

autodoc QuarkConfig

AutoRoundConfig

autodoc AutoRoundConfig