Create README.md

README.md

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+---
+language: 
+- en
+license: apache-2.0
+tags:
+- quantization
+- sinq
+- int3
+- efficient-inference
+- text-generation
+- qwen
+- llm
+- compression
+---
+
+<p align="center" style="margin:0;">
+    <img src="logo.png" width="150" style="margin:0; padding:0;"/>
+</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>
+
+
+# SINQ 3-bit Quantized Qwen3-14B model
+
+This repository contains the official **3-bit quantized** version of the [`Qwen3-14B`](https://huggingface.co/Qwen/Qwen3-14B) model using the **SINQ (Sinkhorn-Normalized Quantization)** method.  
+SINQ is a novel, fast and high-quality quantization method designed to make any Large Language Models smaller while keeping their accuracy almost intact. 
+
+To support the project please put a star ⭐ in the official [SINQ](https://github.com/huawei-csl/SINQ) github repository. 
+
+## Model Details
+- **Model Name:** `Qwen3-14B-3bit-SINQ `
+- **Base Model:** [`Qwen/Qwen3-14B`](https://huggingface.co/Qwen/Qwen3-14B)
+- **Task:** Text Generation
+- **Framework:** PyTorch / Transformers
+- **License:** [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)
+- **Quantized By:** *Huawei - Computing System Lab*
+
+
+## Quantization Details
+
+- **Quantization Method:**  SINQ (Sinkhorn-Normalized Quantization)
+- **Precision:** INT3 
+- **Group Size:**  64 
+- **Framework:**  PyTorch 
+- **Quantization Library:**  `sinq` 
+
+---
+
+# 🚀 Usage</span>
+
+## Prerequisite
+Before running the quantization script, make sure the **SINQ** library is installed.
+Installation instructions and setup details are available in the [SINQ official github repository](https://github.com/huawei-csl/SINQ).
+
+## Usage example
+You can load and use the model with our wrapper based on the 🤗 Transformers library:
+
+```python
+from transformers import AutoTokenizer
+from sinq.patch_model import AutoSINQHFModel
+
+model_name = "huawei-cls/Qwen3-14B-3bit-SINQ"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+sinq_model = AutoSINQHFModel.from_quantized_safetensors(
+    model_name,
+    device="cuda:0",
+    compute_dtype=torch.bfloat16
+)
+
+prompt = "Explain neural network quantization in one sentence."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
+with torch.inference_mode():
+    out_ids = sinq_model.generate(**inputs, max_new_tokens=32, do_sample=False)
+print(tokenizer.decode(out_ids[0], skip_special_tokens=True))
+
+```
+
+<details>
+<summary><span style="font-size:1.1em; font-weight:bold;">🧩 Quantization Process</span></summary>
+
+The quantized model was obtained using the **SINQ** quantization library, following the steps below:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from sinq.patch_model import AutoSINQHFModel
+from sinq.sinqlinear import BaseQuantizeConfig
+
+# Load base model
+base_model_name = "Qwen/Qwen3-14B"
+model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype="float16")
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+
+# Apply 3-bit SINQ quantization
+quant_cfg = BaseQuantizeConfig(
+    nbits=3,            # quantization bit-width
+    group_size=64,     # group size
+    tiling_mode="1D",   # tiling strategy
+    method="sinq"       # quantization method ("asinq" for the calibrated version)
+)
+
+qmodel = AutoSINQHFModel.quantize_model(
+    model,
+    tokenizer=tokenizer,
+    quant_config=quant_cfg,
+    compute_dtype=torch.bfloat16,
+    device="cuda:0"
+)
+```
+
+> **Reproducibility Note**: This model was quantized using the SINQ implementation from commit [`14ad847`](https://github.com/huawei-csl/SINQ/commit/14ad847d0ab25f1794b8820506f59b5c9c1fc979) of the [SINQ](https://github.com/huawei-csl/SINQ) repository.  
+
+</details>
+
+</br>
+
+---
+
+# 🧾 How to Cite This Work
+
+If you find **SINQ** useful in your research or applications, please
+- Put a star ⭐ in the official [SINQ](https://github.com/huawei-csl/SINQ) github repository.
+- Cite our <a href="http://arxiv.org/abs/2509.22944" target="_blank"><strong>paper</strong></a>:
+
+```bibtex
+@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}
+}
+```