LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913705303736320 |
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| author | Yang, Runming Wu, Taiqiang Wang, Jiahao Hu, Pengfei Wu, Yik-Chung Wong, Ngai Yang, Yujiu |
| author_facet | Yang, Runming Wu, Taiqiang Wang, Jiahao Hu, Pengfei Wu, Yik-Chung Wong, Ngai Yang, Yujiu |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06839 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models Yang, Runming Wu, Taiqiang Wang, Jiahao Hu, Pengfei Wu, Yik-Chung Wong, Ngai Yang, Yujiu Computation and Language Artificial Intelligence Machine Learning 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). |
| title | LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.06839 |