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

• URL: http://arxiv.org/abs/2411.06839v2
• Date: Tue, 25 Feb 2025 06:42:57 GMT
• Title: LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models
• Authors: Runming Yang, Taiqiang Wu, Jiahao Wang, Pengfei Hu, Yik-Chung Wu, Ngai Wong, Yujiu Yang,
• Abstract summary: Knowledge distillation (KD) has been a predominant method for compressing Large Language Models (LLMs)<n>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.
• Score: 54.86076216773461
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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