Hybrid-LoRA selectively full fine-tunes modules with high sensitivity to low-rank adaptation using a novel score and applies LoRA elsewhere, matching full fine-tuning at 10% budget and outperforming PEFT baselines by up to 5.65%.
Ruiyi Zhang, Rushi Qiang, Sai Ashish Somayajula, and Pengtao Xie
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Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
Hybrid-LoRA selectively full fine-tunes modules with high sensitivity to low-rank adaptation using a novel score and applies LoRA elsewhere, matching full fine-tuning at 10% budget and outperforming PEFT baselines by up to 5.65%.