@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://preview.aclanthology.org/ingestion-acl-25/2025.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."
}