README.md

Separate LoRA-GA from the PEFT library.

LoRA-GA: Low-Rank Adaptation with Gradient Approximation

• LoRA-GA: Low-Rank Adaptation with Gradient Approximation
  • Overview
  • Quick start
    • 1. Install custom peft
    • 2. Use LoRA-GA in peft
    • 3. Explanation
  • Examples
    • Multi-card training example
  • Note on Usage
  • Citation

Overview

We introduce a novel initialization method, LoRA-GA (Low Rank Adaptation with Gradient Approximation), which aligns the gradients of low-rank matrix product with those of full fine-tuning at the first step. Our extensive experiments demonstrate that LoRA-GA achieves a convergence rate comparable to that of full fine-tuning (hence being significantly faster than vanilla LoRA as well as various recent improvements) while simultaneously attaining comparable or even better performance. (Left) Training loss curves of Llama 2-7B on MetaMathQA to training steps. LoRA-GA converges as quickly as full fine-tuning and outperforms LoRA. (Right) Initialization procedures used in LoRA and LoRA-GA. The key difference is that LoRA-GA initializes adapters using the eigenvectors of the gradient matrix, as opposed to random initialization with a scaling factor.

Citation

@misc{wang2024loragalowrankadaptationgradient,
    title={LoRA-GA: Low-Rank Adaptation with Gradient Approximation},
    author={Shaowen Wang and Linxi Yu and Jian Li},
    year={2024},
    eprint={2407.05000},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    url={https://arxiv.org/abs/2407.05000},
}