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arXiv · 2411.06839

LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models

Runming Yang·Taiqiang Wu·Jiahao Wang·Pengfei Hu·Yik-Chung Wu·Ngai Wong·Yujiu Yang
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Abstract

Knowledge distillation (KD) has been a predominant method for compressing Large Language Models (LLMs). In this paper, we first revisit KD and Low-Rank Adaption (LoRA) and demonstrate that they follow the same paradigm. Inspired by this observation, we propose a parameter-efficient knowledge distillation method, LLM-NEO, which integrates LoRA into KD to improve the efficiency of knowledge transfer. After that, we summarize some valuable guidelines for the hyperparameters in LLM-NEO. Experimental results on compressing Llama 2 and Llama 3.2 show that LLM-NEO outperforms various baselines. Further analysis demonstrates the robustness of the proposed LLM-NEO on variants of LoRA. The code and trained models are available at [Github](https://github.com/yang3121099/LLM-Neo).

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Runming Yang, Taiqiang Wu, Jiahao Wang, Pengfei Hu, Yik-Chung Wu, Ngai Wong, Yujiu Yang. 2024-11-11. LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models. https://arxiv.org/abs/2411.06839

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