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Model: haoranli-ml/Llama-3-8B-CoPE-64k-Instruct
Source: Original Platform

README.md

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+---
+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}
+}
+```