From General to Expert: Custom Pruning LLMs Across Language, Domain, and Task

Yiran Zhao, Wenxuan Zhang, Guizhen Chen, Kenji Kawaguchi, Lidong Bing

Keywords: Large Language Models, Pruning, Expert Model

Abstract:

Large Language Models (LLMs) have transformed natural language processing, yet their substantial model sizes often demand significant computational resources. To conserve computing resources and increase inference speed, it is crucial to prune redundant parameters, especially for general users who often need expert models tailored to specific downstream scenarios. However, current pruning methods primarily focus on maintaining models' general capabilities, either requiring extensive post-training or performing poorly due to coarse-grained pruning. In this work, we design a $\underline{Cus}$tom $\underline{Prun}$ing method ($\texttt{Cus-Prun}$) to prune a large general model into a smaller expert model for specific scenarios. $\texttt{Cus-Prun}$ positions an expert model along the "language", "domain" and "task" dimensions. By identifying and pruning irrelevant neurons, it creates expert models without any post-training.
Our experiments demonstrate that $\texttt{Cus-Prun}$ consistently outperforms other methods, achieving minimal loss in both expert and general capabilities across various models from different model families and sizes.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 14096