EnnengYang/Awesome-Model-Merging-Methods-Theories-Applications
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. ACM Computing Surveys, 2026.
If you're working with large language models (LLMs) or multimodal LLMs and want to combine the strengths of several specialized models without retraining them from scratch, this resource helps you. It takes various pre-trained models and shows you how to 'merge' their knowledge, resulting in a single, more capable model. This is for machine learning practitioners, researchers, and engineers who build or deploy advanced AI systems.
689 stars. Actively maintained with 20 commits in the last 30 days.
Use this if you need to integrate diverse capabilities from multiple large language models or other machine learning models into a single unified model efficiently.
Not ideal if you are looking for a tool to train models from scratch or if your primary need is general-purpose model optimization unrelated to combining expertise.
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Mar 13, 2026
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