What is the role of model merging in creating custom offline AI models?
Model merging combines weights from multiple fine-tuned models to create a single model with combined capabilities without additional training. Techniques include linear interpolation averaging weights between models, SLERP for spherical linear interpolation producing smoother merges, TIES for resolving sign conflicts between model deltas, and DARE for randomly dropping parameters to reduce interference. For example, you can merge a general-purpose model with a code-specialized model and a math-specialized model to create a single model proficient in all three areas. MergeKit provides a command-line tool for performing various merge strategies. This is valuable for offline deployment because you can create a custom all-in-one model tailored to your specific needs instead of running multiple specialized models. The merged model runs with the same hardware requirements as the base model.