Update README.md

README.md

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 <p align="center"><a href="https://portal.opencsg.com/models">[OpenCSG Community]</a>   <a href="https://github.com/yuyijiong/fineweb-edu-chinese">[👾github]</a>  <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[wechat]</a>  <a href="https://twitter.com/OpenCsg">[Twitter]</a> </p>
 
 </div>
+
+[📖Technical Report](https://arxiv.org/abs/2501.08197)
+
 **smoltalk-chinese** is a Chinese fine-tuning dataset constructed with reference to the SmolTalk dataset. It aims to provide high-quality synthetic data support for training large language models (LLMs). The dataset consists entirely of synthetic data, comprising over 700,000 entries. It is specifically designed to enhance the performance of Chinese LLMs across various tasks, improving their versatility and adaptability.
 
 ## Dataset Composition
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 ## 许可协议
 使用 Chinese SmolTalk数据集需要遵循 OpenCSG 社区许可证。Chinese SmolTalk数据集支持商业用途。如果您计划将 OpenCSG 模型或其衍生产品用于商业目的，您必须遵守 OpenCSG 社区许可证以及 Apache 2.0 许可证中的条款和条件。如用于商业用途，需发送邮件至 lorraineg@opencsg.com，并获得许可。
+
+## Citation
+
+```
+@misc{yu2025opencsgchinesecorpusseries,
+      title={OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for LLM Training}, 
+      author={Yijiong Yu and Ziyun Dai and Zekun Wang and Wei Wang and Ran Chen and Ji Pei},
+      year={2025},
+      eprint={2501.08197},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.08197}, 
+}
+```