ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs

• URL: http://arxiv.org/abs/2504.13237v1
• Date: Thu, 17 Apr 2025 16:39:36 GMT
• Title: ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMs
• Authors: Yan Yang, Yixia Li, Hongru Wang, Xuetao Wei, Jianqiao Yu, Yun Chen, Guanhua Chen,
• Abstract summary: ImPart is a novel importance-aware delta sparsification approach.<n>It adjusts sparsity ratios of different singular vectors based on their importance.
• Score: 9.435738597849447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the proliferation of task-specific large language models, delta compression has emerged as a method to mitigate the resource challenges of deploying numerous such models by effectively compressing the delta model parameters. Previous delta-sparsification methods either remove parameters randomly or truncate singular vectors directly after singular value decomposition (SVD). However, these methods either disregard parameter importance entirely or evaluate it with too coarse a granularity. In this work, we introduce ImPart, a novel importance-aware delta sparsification approach. Leveraging SVD, it dynamically adjusts sparsity ratios of different singular vectors based on their importance, effectively retaining crucial task-specific knowledge even at high sparsity ratios. Experiments show that ImPart achieves state-of-the-art delta sparsification performance, demonstrating $2\times$ higher compression ratio than baselines at the same performance level. When integrated with existing methods, ImPart sets a new state-of-the-art on delta quantization and model merging.

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