What is Model Merge?
Model merging involves combining two or more neural network models into a superior, unified model – retaining the strengths or qualities of each. As seen in the image, this process requires aligning the models’ parameters, such as their weights, and blending them effectively. By merging models, you can reduce computational costs, avoid retraining from scratch, and create highly adaptable models suited for various applications. This technique is particularly useful when models excel in different tasks, as the merged model can generalize better across a wider range of scenarios.
To achieve this, you can use Arcee AIs open source toolkit, MergeKit. By loading only the tensors necessary for each individual operation into working memory, MergeKit can scale from a high-end research cluster all the way down to a personal laptop with no GPU and limited RAM.
Learn more about Model Merging in our paper.