FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing

• URL: http://arxiv.org/abs/2501.14713v2
• Date: Fri, 31 Jan 2025 17:38:07 GMT
• Title: FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing
• Authors: James Seale Smith, Chi-Heng Lin, Shikhar Tuli, Haris Jeelani, Shangqian Gao, Yilin Shen, Hongxia Jin, Yen-Chang Hsu,
• Abstract summary: We present a method to prune large language models (LLMs) that selectively prunes model blocks based on an importance score. We propose a principled metric to replace each pruned block using a weight-sharing mechanism. Empirical evaluations demonstrate substantial performance gains over existing methods.
• Score: 59.12511498024836
• License:
• Abstract: The rapid proliferation of large language models (LLMs) in natural language processing (NLP) has created a critical need for techniques that enable efficient deployment on memory-constrained devices without compromising performance. We present a method to prune LLMs that selectively prunes model blocks based on an importance score and replaces them with a low-parameter replacement strategy. Specifically, we propose a principled metric to replace each pruned block using a weight-sharing mechanism that leverages unpruned counterparts from the model and block-specific low-rank adapters. Furthermore, we facilitate the learning of these replacement blocks with output feature normalization and an adapter initialization scheme built on low-rank SVD reconstructions. Empirical evaluations demonstrate substantial performance gains over existing methods, achieving state-of-the-art performance on 5/6 benchmarks for a compression rate of 30% and 6/6 benchmarks for a compression rate of 40%. We also demonstrate that our approach can extend smaller models, boosting performance on 6/6 benchmarks using only ~0.3% tokens of extended training with minimal additional parameter costs.

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