---
base_model:
- meta-llama/Meta-Llama-3-8B
language:
- en
library_name: transformers
pipeline_tag: text-generation
license: llama3
---

## haoranli-ml/Llama-3-8B-CoPE-64k-Instruct

[![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 enhancement of RoPE that *softly* clips the unstable low-frequency components, delivering consistent gains both **within the training context** and during **long-context extrapolation**.

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.

2️⃣ **Refines Long-range Semantic Signals** by alleviating the secret *long-term decay of semantic attention* introduced by RoPE.

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
```
@article{li2026cope,
  title={CoPE: Clipped RoPE as A Scalable Free Lunch for Long Context LLMs},
  author={Li, Haoran and Ren, Sucheng and Yuille, Alan and Wang, Feng},
  journal={arXiv preprint arXiv:2602.05258},
  year={2026}
}
```