Flexible and Effective Mixing of Large Language Models into a Mixture of Domain Experts
arXiv:2408.17280v22 citationsh-index: 28
AI Analysis
This work addresses the need for efficient MOE construction in machine learning, but it appears incremental as it focuses on tooling rather than novel algorithmic breakthroughs.
The authors tackled the problem of creating low-cost Mixture-of-Domain-Experts (MOE) from trained models by presenting a toolkit that supports mixing models or adapters, with extensive tests and architectural guidance provided.
We present a toolkit for creating low-cost Mixture-of-Domain-Experts (MOE) from trained models. The toolkit can be used for creating a mixture from models or from adapters. We perform extensive tests and offer guidance on defining the architecture of the resulting MOE using the toolkit. A public repository is available.