ResLoRA: Identity Residual Mapping in Low-Rank Adaption
Shuhua Shi Shaohan Huang Minghui Song Zhoujun Li Zihan Zhang Haizhen Huang Furu Wei Weiwei Deng Feng Sun Qi Zhang
As one of the most popular parameter-efficient fine-tuning (PEFT) methods,
low-rank adaptation (LoRA) is commonly applied to fine-tune large language
models (LLMs). However, updating the weights of LoRA blocks effectively and
expeditiously is challenging due to the long calculation path in the original
model. To address this, we propose ResLoRA, an improved framework of LoRA. By
adding residual paths during training and using merging approaches to eliminate
these extra paths during inference, our method can achieve better results in
fewer training steps without any extra trainable parameters or inference cost
compared to LoRA. The experiments on NLG, NLU, and text-to-image tasks
demonstrate the effectiveness of our method. To the best of our knowledge,
ResLoRA is the first work that combines the residual path with LoRA. The code
of our method is available at
https://github.com/microsoft/LMOps/tree/main/reslora .