Model Merging

In depth

Combining the weights of two or more models into a single model without further training, usually by averaging or interpolating their parameters. The 2022 model soups paper showed that averaging several fine-tunes of the same base model often beats the best individual fine-tune, with no extra inference cost, unlike an ensemble. It works best when the models share an architecture and a common starting checkpoint, and it is widely used in the open-weights community to blend skills from separate fine-tunes.

Primary source: arxiv.org