Summary of What Matters For Model Merging at Scale?, by Prateek Yadav et al.
What Matters for Model Merging at Scale?
by Prateek Yadav, Tu Vu, Jonathan Lai, Alexandra Chronopoulou, Manaal Faruqui, Mohit Bansal, Tsendsuren Munkhdalai
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 A novel study systematically evaluates the utility of model merging at scale, exploring the impact of various factors such as base model quality, number of expert models, and model size on merged model performance. Four popular merging methods (Averaging, Task-Arithmetic, Dare, and TIES) are tested across different scales, from 1B to 64B parameters, and up to 8 expert models. The experiments examine held-in tasks and zero-shot generalization to unseen held-out tasks. Key findings include the importance of strong base models for effective merging, the facilitation of larger models, consistent improvement in generalization capabilities, and the ability to merge more expert models with larger models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model merging is a technique that combines multiple expert models into one more capable model. This study looks at how this works when you have many different models. The researchers tested four ways to merge these models: averaging, arithmetic, Dare, and TIES. They used models of different sizes and combined up to eight expert models. They also looked at how well the merged models did on tasks they were trained for and on new tasks they had never seen before. Some important findings include that having good starting models is important, larger models make it easier to merge, and the merged models get better at doing things they weren’t trained for. |
Keywords
» Artificial intelligence » Generalization » Zero shot
