@inproceedings{minut-etal-2025-mergenetic,
title = "Mergenetic: a Simple Evolutionary Model Merging Library",
author = "Minut, Adrian Robert and
Mencattini, Tommaso and
Santilli, Andrea and
Crisostomi, Donato and
Rodol{\`a}, Emanuele",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.55/",
doi = "10.18653/v1/2025.acl-demo.55",
pages = "572--582",
ISBN = "979-8-89176-253-4",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Mergenetic: a Simple Evolutionary Model Merging Library
%A Minut, Adrian Robert
%A Mencattini, Tommaso
%A Santilli, Andrea
%A Crisostomi, Donato
%A Rodolà, Emanuele
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F minut-etal-2025-mergenetic
%X 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.
%R 10.18653/v1/2025.acl-demo.55
%U https://aclanthology.org/2025.acl-demo.55/
%U https://doi.org/10.18653/v1/2025.acl-demo.55
%P 572-582
Markdown (Informal)
[Mergenetic: a Simple Evolutionary Model Merging Library](https://aclanthology.org/2025.acl-demo.55/) (Minut et al., ACL 2025)
ACL
- Adrian Robert Minut, Tommaso Mencattini, Andrea Santilli, Donato Crisostomi, and Emanuele Rodolà. 2025. Mergenetic: a Simple Evolutionary Model Merging Library. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 572–582, Vienna, Austria. Association for Computational Linguistics.