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arXiv · 2505.21835

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models

Xiangyu Chen·Jing Liu·Ye Wang·Matthew Brand·Pu·Wang·Toshiaki Koike-Akino
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Abstract

To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine-tuning the model. However, sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step. In this work, we aim to reduce this gap, by directly constructing a smaller model while guided by the downstream task. We propose to jointly fine-tune and compress the model by gradually distilling it to a pruned low-rank structure. Experiments demonstrate that joint fine-tuning and compression significantly outperforms other sequential compression methods.

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Xiangyu Chen, Jing Liu, Ye Wang, Matthew Brand, Pu, Wang, Toshiaki Koike-Akino. 2025-05-27. TuneComp: Joint Fine-tuning and Compression for Large Foundation Models. https://arxiv.org/abs/2505.21835

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