Merge pull request #1521 from kvcache-ai/Atream-patch-8

Add citation section to README

Author: Atream

README.md

@@ -185,6 +185,19 @@ You can find example rule templates for optimizing DeepSeek-V2 and Qwen2-57B-A14
 
 If you are interested in our design principles and the implementation of the injection framework, please refer to the [design document](doc/en/deepseek-v2-injection.md).
 
+## Citation
+
+If you use KTransformers for your research, please cite our [paper](https://madsys.cs.tsinghua.edu.cn/publication/ktransformers-unleashing-the-full-potential-of-cpu/gpu-hybrid-inference-for-moe-models/):
+
+```
+@inproceedings{10.1145/3731569.3764843,
+title = {KTransformers: Unleashing the Full Potential of CPU/GPU Hybrid Inference for MoE Models},
+author = {Chen, Hongtao and Xie, Weiyu and Zhang, Boxin and Tang, Jingqi and Wang, Jiahao and Dong, Jianwei and Chen, Shaoyuan and Yuan, Ziwei and Lin, Chen and Qiu, Chengyu and Zhu, Yuening and Ou, Qingliang and Liao, Jiaqi and Chen, Xianglin and Ai, Zhiyuan and Wu, Yongwei and Zhang, Mingxing},
+booktitle = {Proceedings of the ACM SIGOPS 31st Symposium on Operating Systems Principles},
+year = {2025}
+}
+```
+
 <h2 id="ack">Acknowledgment and Contributors</h2>
 
 The development of KTransformers is based on the flexible and versatile framework provided by Transformers. We also benefit from advanced kernels such as GGUF/GGML, Llamafile, Marlin, sglang and flashinfer. We are planning to contribute back to the community by upstreaming our modifications.