Summary of Flexible and Effective Mixing Of Large Language Models Into a Mixture Of Domain Experts, by Rhui Dih Lee and Laura Wynter and Raghu Kiran Ganti
Flexible and Effective Mixing of Large Language Models into a Mixture of Domain Experts
by Rhui Dih Lee, Laura Wynter, Raghu Kiran Ganti
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 This paper introduces a toolkit for generating low-cost Mixture-of-Domain-Experts (MOE) from trained models, allowing users to create a mixture from models or adapters. The toolkit is designed to facilitate the creation of MOEs and provides guidance on defining the architecture of the resulting model. The authors perform extensive testing and share their findings in this paper. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to combine different AI models to get better results without having to train a whole new model from scratch. That’s what this toolkit does! It helps you create a special type of AI model called Mixture-of-Domain-Experts (MOE) using trained models or adapters. The authors tested their toolkit thoroughly and want to share the results with others. |
