Fully deprecate AutoGPTQ and AutoAWQ for GPT-QModel

[Qubitium]

Remove autogptq clutter and autogptq related configs that are not worth adding backward compat.

GPTQModel has a slight project name change (pypi package and import name stays the same) to GPT-QModel with - as we now have added awq/AutoAWQ into our repo and will be making pr soon to address awq loading using GPT-QModel.

GPTQConfig has the most important changes in this PR:

# New GPTQConfig Property. Applicable for sister Peft/Optimum PRs
act_group_aware (`bool`, *optional*, defaults to `True`):
    Use GAR (group aware activation order) during quantization. Has measurable positive impact on quantization
    quality. Only applicable when `desc_act = False`. Will forced to be `False` when `desc_act = True`.
    
    
# Removed GPTQConfig Properties:
use_cuda_fp16
use_exllama
exllama_config

The 3 removed properties are all related kernel selection. These 3 are a hot potatoe mess and legacy from autogptq. GPT-QModel uses unified backend (existing) property to select kernels. There were compat codes written to convert these 3 properties to backend behind the scenes in 2024 but no longer relevant for 2025.

Note:

• Transformers/Optimum/Peft CI tests should never check for kernel.QUANT_TYPE (str). GPTQ-QModel will return best performing kernel for the relevant module and it may be different per module due to in/out features and other gptq/module properties in relation to device type + dtype + many factors.
• CI tests should only assert check for kernel.QUANT_TYPE if the test specifies a specific kernel via backend selection.

[Rocketknight1]

cc @MekkCyber for quantization

[Qubitium]

We have begun AutoAWQ deprecation as well.

• Fused module codes have all been removed. AutoAWQ used to do quant linear level fusing but I do not believe that this is maintainable or good since if SGLang/vLLM adopts Transformers v5 for model loading, they will do their own auto fusing and the quant module should not interfere with this.

• IPEX is deprecated by Intel and we have a new AwqTorchFused kernel (based on same Intel TorchFused kernel for GPTQ) so any code/unit tests for IPEX now will point to AwqTorchFused kernel.

[MekkCyber]

Hi @Qubitium ! Thanks a lot for working on this! Quick question, what do you mean by AutoAWQ being part of GPT-QModel now? Did you integrate the entire library (including the transformers dependency, like AutoAWQ does), or did you just port over the linear layers, kernels, and related components?

[Qubitium]

LMK when we can merge !

We are performing final CI run in gpt-qmodel and once that passes. I will push 5.6.0 release asap to pypi so this PR can be ready for merge.

[Qubitium]

@SunMarc Ready. GPT-QModel v5.6.0 has been released with wheels currently building slowly for all the python/torch versions (may take a few hours): https://github.com/ModelCloud/GPTQModel/releases/tag/v5.6.0

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[MekkCyber]

Thank you all for your work! I only have 2 nits

[MekkCyber]

Thank you for iterating lgtm

[github-actions]

[For maintainers] Suggested jobs to run (before merge)

run-slow: autoawq, gptq

[github-actions]

View the CircleCI Test Summary for this PR:

https://huggingface.co/spaces/transformers-community/circle-ci-viz?pr=41567&sha=e16e48

[SunMarc]

Thanks ! About gptq-model installation, would it be possible at some point to fetch the correct wheels depending on the user setup like torch using pip ? This is actually a quite a barrier for new users, or try to give more instructions ?

[BenjaminBossan]

Just to be sure, this will be part of transformers v5?

[Qubitium]

Thanks ! About gptq-model installation, would it be possible at some point to fetch the correct wheels depending on the user setup like torch using pip ? This is actually a quite a barrier for new users, or try to give more instructions ?

Right now the pip install script already auto download the precompiled gpt-qmodel whl (125mb) from gitbub releases, if we have the env python,torch,cuda version match. But yeah, it is painful to install if you have to compile from src ranging from 10-20 minutes for compile.

[SunMarc]

Just to be sure, this will be part of transformers v5?

Yes !

[SunMarc]

Right now the pip install script already auto download the precompiled gpt-qmodel whl (125mb) from gitbub releases, if we have the env python,torch,cuda version match. But yeah, it is painful to install if you have to compile from src ranging from 10-20 minutes for compile.

Okay, I must have a mismatched values hence it wasn't downloading the pre-compiled wheels. Is there a way to return a warning if this is not the case ?

[Qubitium]

Okay, I must have a mismatched values hence it wasn't downloading the pre-compiled wheels. Is there a way to return a warning if this is not the case ?

We missed building torch 2.9.1 whl for 5.6.0 so that is likely the cause. Will push 5.6.2 to resolve this other misc setup issues other users have reported plus logwarning when setup cannt match downloaded whl.