huawei-csl/Qwen3-8B-PreSINQ-GGUF

<p align="center"> <img src="SINQGGUFHF.png" alt="Logo" style="max-width: 80%; height: auto;"> </p>

<p align="center">🐙 <a href="https://github.com/huawei-csl/SINQ">Github</a>&nbsp;&nbsp; | &nbsp;&nbsp;📄 <a href="http://arxiv.org/abs/2509.22944">Paper</a></p>

PreSINQ GGUF Quantized Qwen3-4B Model

This repository contains the official PreSINQ GGUF-quantized versions of the `Qwen3-8B` model. For a detailed explanation of PreSINQ strategy please refer to the the official SINQ repository. SINQ is a fast and high-quality quantization technique designed to significantly reduce Large Language Model size while preserving accuracy.

If you find this project useful, please consider giving a ⭐ to the official [SINQ](https://github.com/huawei-csl/SINQ) repository.

Model Details

• —Model Name: Qwen3-8B-PreSINQ-GGUF
• —Base Model: `Qwen/Qwen3-8B`
• —Task: Text Generation
• —Framework: PyTorch / Transformers
• —License: Apache-2.0
• —Quantized By: Huawei – Computing Systems Lab

How to Obtain the PreSINQ Model

The PreSINQ Qwen3-8B models are produced using the PreSINQ GGUF script available in the official SINQ repository.

The models provided here correspond to the best-performing configurations for each quantization type.

📊 Best PreSINQ Quantization Results (Qwen3-8B)

Results below are measured on the WikiText-2 test set.

However, you can generate good PreSINQ models (not the best one) faster by reducing the number of configurations explored during the PreSINQ script execution.

🚀 Usage

Usage Example

You can load and run the PreSINQ GGUF models using:

• —🤗 Transformers
• —llama.cpp
• —Any GGUF-compatible inference framework

🧾 How to Cite This Work

If you find SINQ useful in your research or applications:

• —Please give a ⭐ to the official SINQ repository
• —Cite our <a href="http://arxiv.org/abs/2509.22944" target="_blank"><strong>paper</strong></a>:

@misc{muller2025sinq,
      title={SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights}, 
      author={Lorenz K. Muller and Philippe Bich and Jiawei Zhuang and Ahmet Celik and Luca Benfenati and Lukas Cavigelli},
      year={2025},
      eprint={2509.22944},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={http://arxiv.org/abs/2509.22944}
}