Mergenetic: a Simple Evolutionary Model Merging Library
Abstract
Mergenetic is an open-source library that combines model merging with evolutionary algorithms for flexible experimentation in language models, achieving competitive results with limited computational resources.
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.
Community
We propose a simple evolutionary model merging library.
You can merge existing checkpoints on Hugging Face hub to forge new models with superior performance compared to standard merging.
Github Repo: https://github.com/tommasomncttn/mergenetic
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Reinforced Model Merging (2025)
- SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging (2025)
- Model Assembly Learning with Heterogeneous Layer Weight Merging (2025)
- Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging (2025)
- Single-Input Multi-Output Model Merging: Leveraging Foundation Models for Dense Multi-Task Learning (2025)
- AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization (2025)
- CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging (2025)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper