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[FEAT]: EETQ quantizer support (#30262 )

* [FEAT]: EETQ quantizer support

* Update quantization.md

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* Update src/transformers/quantizers/quantizer_eetq.py

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* Update src/transformers/quantizers/quantizer_eetq.py

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* Update tests/quantization/eetq_integration/test_eetq.py

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* [FEAT]: EETQ quantizer support

* [FEAT]: EETQ quantizer support

* remove whitespaces

* update quantization.md

* style

* Update docs/source/en/quantization.md

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* add copyright

* Update quantization.md

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* Address the comments by amyeroberts

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---------

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: Marc Sun <marc@huggingface.co>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
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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

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