Model Merging in Production: Weight Averaging Your Way to a Multi-Task Specialist
By early 2024, the top of the Open LLM Leaderboard was dominated almost entirely by models that were never trained — they were merged. Teams were taking two or three fine-tuned variants of Mistral-7B, averaging their weights using a YAML config file, and beating purpose-trained models at a fraction of the compute cost. The technique looks trivially simple from the outside: add some tensors together, divide by two, ship it. The reality is more nuanced, and the failure modes are sharp enough to sink a production deployment if you don't understand what's happening under the hood.
This is a practical guide to model merging for ML engineers who want to use it in production: what the methods actually do mathematically, when they work, when they silently degrade, and how to pick the right tool for a given set of constituent models.
