Update README.md

README.md

@@ -1,3 +1,93 @@
----
-license: apache-2.0
----
+---
+language:
+- en
+- zh
+license: apache-2.0
+base_model:
+- Qwen/Qwen3.6-27B
+tags:
+- qwen
+- qwen3.5
+- Dense
+- vLLM
+- SGLang
+pipeline_tag: image-text-to-text
+datasets:
+- nohurry/Opus-4.6-Reasoning-3000x-filtered
+---
+
+# 🌟Qwen3.6-27B-INT4-FOEM
+
+<div align="left">
+  <a href=https://ojs.aaai.org/index.php/AAAI/article/view/40123 target="_blank"><img src=https://img.shields.io/badge/Official%20Site-333399.svg?logo=homepage height=22px></a>
+  <a href=https://huggingface.co/Xingyu-Zheng/Qwen3.6-27B-INT4-FOEM target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a>
+  <a href=https://github.com/Xingyu-Zheng/FOEM target="_blank"><img src= https://img.shields.io/badge/Page-bb8a2e.svg?logo=github height=22px></a>
+  <a href=https://arxiv.org/abs/2507.11017 target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>
+</div>
+
+This is an unofficial quantized version of Qwen3.6-27B.
+
+### 🧠 Quantization Framework
+
+[GPTQModel](https://github.com/ModelCloud/GPTQModel)
+
+## 🗺️ Quantization Method
+
+[FOEM (AAAI 2026)](https://ojs.aaai.org/index.php/AAAI/article/view/40123)
+
+FOEM is an improved quantization method over GPTQ. The resulting model preserves the same inference structure as GPTQ, ensuring compatibility with existing deployment pipelines while achieving better accuracy.
+
+### 📚 Calibration Dataset
+
+We randomly sampled 512 examples from [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered).
+
+## 📋 Usage Example
+
+This model can be deployed using standard frameworks such as **vLLM**, just like other GPTQModel-quantized models.
+
+Example evaluation command:
+
+```bash
+lm-eval --model vllm --model_args pretrained=models/gptqmodel/Qwen3.6-27B-INT4-FOEM,tensor_parallel_size=1,gpu_memory_utilization=0.45 --tasks wikitext --batch_size 1
+```
+
+## 🙏 Acknowledgements
+Special thanks to [Qwen Team](https://huggingface.co/Qwen) for providing the original model: [Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B).
+
+## 📖 Citation
+
+If you use this model in your research or projects, please cite:
+
+```bibtex
+@misc{qwen3.6-27b,
+    title  = {{Qwen3.6-27B}: Flagship-Level Coding in a {27B} Dense Model},
+    author = {{Qwen Team}},
+    month  = {April},
+    year   = {2026},
+    url    = {https://qwen.ai/blog?id=qwen3.6-27b}
+}
+```
+
+```bibtex
+@misc{qubitium2024gptqmodel,
+  author = {ModelCloud.ai and qubitium@modelcloud.ai},
+  title = {GPT-QModel},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/modelcloud/gptqmodel}},
+  note = {Contact: qubitium@modelcloud.ai},
+  year = {2024},
+}
+```
+
+```bibtex
+@inproceedings{zheng2026first,
+  title={First-order error matters: Accurate compensation for quantized large language models},
+  author={Zheng, Xingyu and Qin, Haotong and Li, Yuye and Chu, Haoran and Wang, Jiakai and Guo, Jinyang and Magno, Michele and Liu, Xianglong},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={40},
+  number={34},
+  pages={28883--28891},
+  year={2026}
+}
+```