model-merging

You can merge fine-tuned models without retraining by creating a YAML merge configuration and executing the mergekit toolkit to blend domain-specific expertise into a single capable model.

Model merging combines multiple fine-tuned models into one capable model. Use it to build specialized systems blending math, coding, and chat expertise while saving computational resources and avoiding retraining.

The best way to combine fine-tuned models is using mergekit with a YAML configuration, applying methods like SLERP, TIES, or DARE to blend capabilities and reduce deployment costs.

Yes, you need architecture-compatible base models to execute model merging successfully. Mergekit requires compatible architectures alongside a YAML merge configuration to generate the merged model.

SLERP, TIES, and DARE are model merging methods that blend fine-tuned variants by interpolating or selecting parameters, allowing you to test multiple variants and preserve cross-domain capabilities.

Yes, you can rapidly experiment with multiple variants by blending fine-tuned models using mergekit. This lets you test different combinations and preserve skills across domains without retraining.