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, Andrea
Membro del Collaboration Group
;
Crisostomi, Donato
Membro del Collaboration Group
;
Rodola, Emanuele
Supervision
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.
2025
Association for Computational Linguistics
Model Merging; Evolutionary Model Merging; Large Language Models; Efficient Methods
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1750756
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