Model merging allows combining the capabilities of existing models into a new one - post hoc, without additional training. This has made it increasingly popular thanks to its low cost and the availability of libraries that support merging on consumer GPUs. Recent work shows that pairing merging with evolutionary algorithms can boost performance, but no framework currently supports flexible experimentation with such strategies in language models. We introduce Mergenetic, an open-source library for evolutionary model merging. Mergenetic enables easy composition of merging methods and evolutionary algorithms while incorporating lightweight fitness estimators to reduce evaluation costs. We describe its design and demonstrate that Mergenetic produces competitive results across tasks and languages using modest hardware.
Mergenetic: a Simple Evolutionary Model Merging Library
Mergenetic is an open-source library that combines model merging with evolutionary algorithms for flexible experimentation in language models, achieving competitive results with limited computational resources.
- Year
- 2025
- Venue
- arXiv 2025
- Authors
- 5
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2505.11427ARXIV-DEFAULT
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