Abstract
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.
Adrian R. Minut
PhD Student
Tommaso Mencattini
Research Intern
Andrea Santilli
Research Scientist, Nous Research
PhD Student passionate about natural language processing, representation learning and machine intelligence.
Donato Crisostomi
PhD Student
PhD student @ Sapienza, University of Rome | former Applied Science intern @ Amazon Search, Luxembourg | former Research Science intern @ Amazon Alexa, Turin
