license: llama3.2
datasets:
- BAAI/Infinity-Instruct
base_model:
- meta-llama/Llama-3.2-1B-Instruct
tags:
- Instruct_Tuning
Shadow-FT
[📜 Paper] • [🤗 HF Models] • [🐱 GitHub]
This repo contains the weights from our paper: Shadow-FT: Tuning Instruct via Base by Taiqiang Wu* Runming Yang*, Jiayi Li, Pengfei Hu, Ngai Wong and Yujiu Yang.
* for equal contributions.
Overview
Observation:
Directly tuning the INSTRUCT (i.e., instruction tuned) models often leads to marginal improvements and even performance degeneration.
Paired BASE models, the foundation for these INSTRUCT variants, contain highly similar weight values (i.e., less than 2% on average for Llama 3.1 8B).
$\Rightarrow$ We propose the Shadow-FT framework to tune the INSTRUCT models by leveraging the corresponding BASE models. The key insight is to fine-tune the BASE model, and then directly graft the learned weight updates to the INSTRUCT model.
Performance
This repository contains the Llama-3.2-1B tuned on BAAI-2k subsets using Shadow-FT.
please refer to our paper for details.
☕️ Citation
If you find this repository helpful, please consider citing our paper:
@article{wu2025shadow,
title={Shadow-FT: Tuning Instruct via Base},
author={Wu, Taiqiang and Yang, Runming and Li, Jiayi and Hu, Pengfei and Wong, Ngai and Yang, Yujiu},
journal={arXiv preprint arXiv:2505.12716},
year={2025}
}
For any questions, please pull an issue or email at [email protected]