Logo

PreSINQ GGUF Quantized Qwen3-0.6B Model

This repository contains the official PreSINQ GGUF-quantized versions of the Qwen3-0.6B 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 repository.

Model Details

Model Name: Qwen3-0.6B-PreSINQ-GGUF
Base Model: Qwen/Qwen3-0.6B
Task: Text Generation
Framework: PyTorch / Transformers
License: Apache-2.0
Quantized By: Huawei – Computing Systems Lab

How to Obtain the PreSINQ Model

The PreSINQ Qwen3-0.6B 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 (Qwen3-0.6B)

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

Method	Bits	Size (GB)	Perplexity ↓
Baseline (FP16)	FP16	1.41	21.8769
Baseline + Q4_K_S	4-bit	0.45	24.3443
PreSINQ + Q4_K_S	4-bit	0.37	22.9176
Baseline + Q3_K_S	3-bit	0.37	35.5913
PreSINQ + Q3_K_S	3-bit	0.31	29.1805

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 paper:

@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}
}