Summary of Pleas — Merging Models with Permutations and Least Squares, by Anshul Nasery et al.
PLeaS – Merging Models with Permutations and Least Squares
by Anshul Nasery, Jonathan Hayase, Pang Wei Koh, Sewoong Oh
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed PLeaS algorithm merges machine learning models by combining their functionalities, allowing practitioners to fine-tune open-source models on specialized tasks and datasets. The method relaxes constraints of prior approaches, enabling the merging of models from different base models into a single model of desired size. By maximizing alignment and minimizing approximation error, PLeaS produces performant merged models that improve over state-of-the-art methods by up to 15 percentage points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Merging machine learning models can combine their strengths, but most approaches only work for models fine-tuned from the same base model. A new method called PLeaS makes it possible to merge two models with different origins into one that’s just as good or even better. |
Keywords
* Artificial intelligence * Alignment * Machine learning
