Add metadata

README.md

@@ -1,59 +1,61 @@
----
-license: llama3.1
-datasets:
-- BAAI/Infinity-Instruct
-base_model:
-- meta-llama/Llama-3.1-8B-Instruct
-tags:
-- Instruct_Tuning
----
-
-# Shadow-FT
-
-  <a href="https://arxiv.org/pdf/2505.12716"><b>[📜 Paper]</b></a> •
-  <a href="https://huggingface.co/collections/taki555/shadow-ft-683288b49e1e5e1edcf03135"><b>[🤗 HF Models]</b></a> •
-  <a href="https://github.com/wutaiqiang/Shadow-FT"><b>[🐱 GitHub]</b></a>
-
-This repo contains the weights from our paper: <a href="https://arxiv.org/pdf/2505.12716" target="_blank">Shadow-FT: Tuning Instruct via Base</a> by <a href="https://wutaiqiang.github.io" target="_blank">Taiqiang Wu*</a> <a href="https://rummyyang.github.io/" target="_blank">Runming Yang*</a>, Jiayi Li, Pengfei Hu, Ngai Wong and Yujiu Yang.
-
-\* for equal contributions.
-
-
-
-## Overview
-
-<img src="framework.png" width="100%" />
-
-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-3.1-8B tuned on BAAI-2k subsets using Shadow-FT.
-
-<img src="performance.png" width="100%" />
-
-please refer to [our paper](https://arxiv.org/pdf/2505.12716) 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]`
+---
+license: llama3.1
+datasets:
+- BAAI/Infinity-Instruct
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+tags:
+- Instruct_Tuning
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Shadow-FT
+
+  <a href="https://arxiv.org/pdf/2505.12716"><b>[📜 Paper]</b></a> •
+  <a href="https://huggingface.co/collections/taki555/shadow-ft-683288b49e1e5e1edcf03135"><b>[🤗 HF Models]</b></a> •
+  <a href="https://github.com/wutaiqiang/Shadow-FT"><b>[🐱 GitHub]</b></a>
+
+This repo contains the weights from our paper: <a href="https://arxiv.org/pdf/2505.12716" target="_blank">Shadow-FT: Tuning Instruct via Base</a> by <a href="https://wutaiqiang.github.io" target="_blank">Taiqiang Wu*</a> <a href="https://rummyyang.github.io/" target="_blank">Runming Yang*</a>, Jiayi Li, Pengfei Hu, Ngai Wong and Yujiu Yang.
+
+\* for equal contributions.
+
+
+
+## Overview
+
+<img src="framework.png" width="100%" />
+
+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-3.1-8B tuned on BAAI-2k subsets using Shadow-FT.
+
+<img src="performance.png" width="100%" />
+
+please refer to [our paper](https://arxiv.org/pdf/2505.12716) 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]`