Merge pull request #1522 from kvcache-ai/Atream-patch-9

Update README with Citation link

Author: Atream

README.md

@@ -9,7 +9,7 @@
 
 </p>
   <h3>A Flexible Framework for Experiencing Cutting-edge LLM Inference Optimizations</h3>
-  <strong><a href="#show-cases">🌟 Show Cases</a> | <a href="#quick-start">🚀 Quick Start</a> | <a href="#tutorial">📃 Tutorial</a> | <a href="https://github.com/kvcache-ai/ktransformers/discussions">💬  Discussion </a>|<a href="#FAQ"> 🙋 FAQ</a> </strong>
+  <strong><a href="#show-cases">🌟 Show Cases</a> | <a href="#quick-start">🚀 Quick Start</a> | <a href="#tutorial">📃 Tutorial</a> | <a href="#Citation">🔥  Citation </a> | <a href="https://github.com/kvcache-ai/ktransformers/discussions">💬  Discussion </a>|<a href="#FAQ"> 🙋 FAQ</a> </strong>
 </div>
 
 <h2 id="intro">🎉 Introduction</h2>
@@ -185,7 +185,7 @@ 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
+<h2 id="Citation">🔥 Citation</h2>
 
 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/):