Llama-3-8B-CoPE-64k-Instruct/README.md

base_model, language, library_name, pipeline_tag, license

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

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

✨ 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.

📖 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}
}