SINQ quantization strategy integration
Author: ChiaraBoretti
Pull request: yes

# What does this PR do?
**Summary**
This PR introduces an integration of the [SINQ](https://huggingface.co/papers/2509.22944) quantization method into the Hugging Face Transformers library. It follows the pattern of existing quantization integrations such as HQQ, AWQ, and others.
The goal is to enable users to configure and apply SINQ quantization directly through model configuration parameters, which can then be passed via the ```from_pretrained()``` function within the Transformers framework.

**Example Usage**
The pipeline then will be the following:
```bash
cfg = SinqConfig(
    nbits=4,
    group_size=64,
    tiling_mode="1D",
    method="sinq",     
    modules_to_not_convert=["lm_head"],
    device="cuda:1"
)

model_name = "Qwen/Qwen3-1.7B"
tok = AutoTokenizer.from_pretrained(model_name)

qmodel = AutoModelForCausalLM.from_pretrained(
    model_name,
    cache_dir=cache_dir, 
    torch_dtype=torch.bfloat16,
    quantization_config=cfg,
)
```

**Model saving and loading**
Once the model has been quantized, it can be saved locally or pushed to the Hugging Face Hub.
Moreover with this integration it is also possible to download a sinq quantized model directly from the hub and use it in inference.
```bash
hf_path = "HF_hub/path"
tok  = AutoTokenizer.from_pretrained(hf_path)
qmodel = AutoModelForCausalLM.from_pretrained(hf_path, device_map="cuda:0")
```
**Installation**
All the required dependencies are installed by installing the official SINQ python package.
```bash
pip install sinq
```

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Fixes #42116


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@SunMarc @MekkCyber

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Models:

- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier

Library:

- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh

Integrations:

- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo

Devices/Backends:

- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad 

Documentation: @stevhliu

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Comments:
**[For maintainers]** Suggested jobs to run (before merge)

run-slow: sinq

Hi @ChiaraBoretti ! Almost all quantization methods are refactored for the new weights loading api, if you are still interested in integrating this method we can help you if you have any questions ! The biggest change is how we moved away from using `create_quantized_param` to using a `ConversionOps` to quantize the weights. You can check the `finegrained_fp8` or `torchao` quantizer/integration files for some examples

Hi @MekkCyber,
yes, I’m still interested in integrating SINQ into Transformers’ quantization methods! Thanks for the references, now that everything has been merged and is ready, I'm going to look into them to better understand the refactor and how to adapt my code to it. I’ll reach out if I run into any issues or have questions about the integration. Thanks again!