GLOSSARY
Model Merging
Combining the weights of multiple fine-tuned models into one — blending skills without paying for another training run.
Fine-tunes of the same base model share their skeleton, so their weights can be averaged, tasked-arithmetic'd or TIES-merged into a single model that carries several skills at once — a math tutor plus a coder, in one file, with zero training compute. Open-model communities merge constantly, and mergekit is the standard toolkit.
The catch is interference: skills that conflict produce a muddled hybrid, and merges can look great on benchmarks while feeling worse in use — evaluation still rules. Merging also raises open-questions about model lineage and licenses that commercial users should check before shipping.