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
this https URL .

使用深度学习模型的参数高效微调方法，通过引入残差路径和合并方法来改进低秩适应方法（LoRA），从而在没有额外可训练参数或推理成本的情况下，在较少的训练步骤中获得更好的结果。