RUC & Xiaomi: Efficient Fine-Tuning 🙌🎉

📰 News

• 2025-4-29: Our paper has been accepted by IJCAI-25. Congratulations!
• 2025-3-31: Delivery of a Prototype System for Parameter-Efficient and Gradient Projection Methods: A Comprehensive Benchmark Against 10+ State-of-the-Art Efficient Fine-Tuning Approaches.
• 2024-12-30: Theoretical Insights into Fine-Tuning Attention Mechanism.

🎯 Introduction and Target

(1) Our insights (paper, in progress):

According to the traditional statistical learning viewpoint, performance can be defined by the sum of optimization error and generalization error. In (generalization, storage-friendly), we give Theorem 1 (Information-theoretic genralization bounds), showing that with the same $rr$ value, fine-tuning ${\mathbf{W}}_q,{\mathbf{W}}_v\backslash mathbf\{W\}\_q,\backslash mathbf\{W\}\_v$ consistently achieves results comparable to or even surpassing those of fine-tuning ${\mathbf{W}}_q,{\mathbf{W}}_k,{\mathbf{W}}_v\backslash mathbf\{W\}\_q,\backslash mathbf\{W\}\_k,\backslash mathbf\{W\}\_v$. This reduces the number of parameters for the same $rr$, while improving generalization bounds and potentially providing memory benefits. In (optimization, time-friendly), we discuss the learning dynamics in fine-tuning attention mechanism, and we illustrate Theorem 2 that the feature learning of attention mechanism is efficient when the learning rate for ${\mathbf{W}}_v\backslash mathbf\{W\}\_v$ should be generally much larger than that of ${\mathbf{W}}_q,{\mathbf{W}}_k\backslash mathbf\{W\}\_q,\backslash mathbf\{W\}\_k$ in fine-tuning. Building on our experimental and theoretical insights, one can develop new algorithms to improve the effectiveness (e.g., storage, and time) of fine-tuning.

(2) Target:

$\text{This project conducts comprehensive benchmarking of the following 10+ efficient fine-tuning methods.}\backslash text\{\backslash textcolor\{blue\}\{This\; project\; conducts\; comprehensive\; benchmarking\; of\; the\; following\; 10+\; efficient\; fine-tuning\; methods.\}\}$

Notably, our proposed approach maintains orthogonal compatibility and can be synergistically combined with any of these methods.

📖 10+ efficient fine-tuning methods

• LoRA (ICLR 2022)
• AdaLoRA (ICLR 2023)
• DoRA (ICML Oral)
• PiSSA (NeurIPS 2024)
• rsLoRA
• OLoRA
• EVA
• IA3
• SIFT (ICML 2024)
• Galore (ICML 2024 Oral)

⚙️ Install

1. To install the experiment, please install the pip file.

pip install -r requirements.txt

2. (Optional) For SIFT&Galore

git clone git@github.com:song-wx/SIFT.git
cd SIFT
pip install .

pip install galore-torch

🚀 Quick Start

Get Dataset

data_download.py

Usage

1. ensure execute permissions

   chmod +x xxx.sh  #xxx->your file name

2. Full-Finetuning, LoRA, AdaLoRA, DoRa, PiSSA, rsLoRA, OLoRA, EVA, SIFT

   # choose the target method_name and modules.
   EfficientFT/sh/roberta-base-peft.sh 
   EfficientFT/sh/llama-peft.sh

3. Galore.

   EfficientFT/sh/roberta_galore.sh

😊Some Results

📝 Citation

@article{yao2024theoretical,
  title={Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization},
  author={Yao, Xinhao and Qian, Hongjin and Hu, Xiaolin and Xu, Gengze and Liu, Yong and Liu, Wei and Luan, Jian and Wang, Bin},
  journal={arXiv preprint arXiv:2410.02247},
  year={2024}
}