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. A video demo showcasing its main features is also provided.
Mergenetic: a Simple Evolutionary Model Merging Library / Minut, Adrian Robert; Mencattini, Tommaso; Santilli, Andrea; Crisostomi, Donato; Rodola, Emanuele. - (2025), pp. 572-582. (Intervento presentato al convegno Association for Computational Linguistics tenutosi a Vienna; Austria) [10.18653/v1/2025.acl-demo.55].
Mergenetic: a Simple Evolutionary Model Merging Library
Minut, Adrian Robert
Primo
Membro del Collaboration Group
;Santilli, AndreaMembro del Collaboration Group
;Crisostomi, DonatoMembro del Collaboration Group
;Rodola, EmanueleSupervision
2025
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. A video demo showcasing its main features is also provided.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


