Update README.md

README.md

@@ -7,12 +7,13 @@ base_model:
 ## haoranli-ml/Llama-3-8B-HardClip-64k-Base
 
 [![Paper](https://img.shields.io/badge/CoPE_paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/2602.05258)
+[![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/hrlics/CoPE)
 
 
 ### ✨  Overview
-**CoPE** is a plug-and-play enchancement of RoPE that *softly* clips the unstable low-frequency components, delivering consistent gains both **within the training context** and during **long-context extrapoaltion**.
+**CoPE** is a plug-and-play enhancement of RoPE that *softly* clips the unstable low-frequency components, delivering consistent gains both **within the training context** and during **long-context extrapoaltion**.
 
-With a simple yet effective soft clipping strategy, CoPE
+With a simple yet effective soft clipping strategy, CoPE:
 
 1️⃣ **Eliminates severe OOD outliers**, whose periods exceed the pre-training context window and are the primary cause of OOD extrapolation.
 
@@ -20,7 +21,7 @@ With a simple yet effective soft clipping strategy, CoPE
 
 3️⃣ **Prevents Spectral Leakage** induced by hard frequency truncation, which otherwise leads to long-range oscillatory ringing in the attention scores across relative token distances and introduces spurious correlations.
 
-
+For more details on training and evaluation, please refer to the [official GitHub repository](https://github.com/hrlics/CoPE).
 
 ### 📖 Citation
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