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 is a prominent method that adds a small number of learnable parameters to the frozen pre-trained weights for parameter-efficient fine-tuning. Prompted by the question%2C ``Can we make its representation enough with LoRA weights solely at the final phase of finetuning without the pre-trained weights%3F'' In this work%2C we introduce Progressive Compression LoRA~(PC-LoRA)%2C which utilizes low-rank adaptation (LoRA) to simultaneously perform model compression and fine-tuning. The PC-LoRA method gradually removes the pre-trained weights during the training process%2C eventually leaving only the low-rank adapters in the end. Thus%2C these low-rank adapters replace the whole pre-trained weights%2C achieving the goals of compression and fine-tuning at the same time. Empirical analysis across various models demonstrates that PC-LoRA achieves parameter and FLOPs compression rates of 94.36%25%2F89.1%25 for vision models%2C e.g.%2C ViT-B%2C and 93.42%25%2F84.2%25 parameters and FLOPs compressions for language models%2C e.g.%2C BERT.?quality=80&w=800)
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