Summary of Emr-merging: Tuning-free High-performance Model Merging, by Chenyu Huang et al.
EMR-Merging: Tuning-Free High-Performance Model Merging
by Chenyu Huang, Peng Ye, Tao Chen, Tong He, Xiangyu Yue, Wanli Ouyang
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new model merging method called Elect, Mask & Rescale-Merging (EMR-Merging) that enables single models with multi-task capabilities without requiring additional data or training. By analyzing the existing model merging paradigm, the authors discover that using a single model’s weights can’t simulate all the models’ performance. To address this issue, EMR-Merging elects a unified model and generates lightweight task-specific modulators to align the direction and magnitude between the unified model and each specific model. The proposed method shows impressive performance compared to existing merging methods under various settings, including merging different numbers of vision, NLP, PEFT, and multi-modal models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about combining many AI models into one supermodel that can do lots of tasks. Right now, people are sharing their pre-trained models with each other, but it’s hard to combine them without sacrificing performance or needing more data. The authors looked at how people are currently combining models and found a way to improve it. They created a new method called EMR-Merging that takes the best parts of all the models and makes one supermodel that can do lots of tasks really well. |
Keywords
» Artificial intelligence » Mask » Multi modal » Multi task » Nlp
