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arxiv: 2505.11427 · v1 · pith:64VEC4WJnew · submitted 2025-05-16 · 💻 cs.LG · cs.AI· cs.NE

Mergenetic: a Simple Evolutionary Model Merging Library

classification 💻 cs.LG cs.AIcs.NE
keywords mergingevolutionarymergeneticmodelalgorithmslibrarymodelsacross
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Model Merging: Foundations and Algorithms

    cs.LG 2026-05 unverdicted novelty 6.0

    New cycle-consistent optimization, task vector theory, singular vector decompositions, adaptive routing, and efficient evolutionary search provide foundations for merging neural network weights across tasks.

  2. Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities

    cs.LG 2024-08 accept novelty 4.0

    The paper introduces a new taxonomy for model merging methods and reviews their applications in LLMs, MLLMs, continual learning, multi-task learning, and other subfields while outlining open challenges.