Model Merging
Combining the weights of multiple fine-tuned AI models into a single model that inherits capabilities from all of them.
Definition
Model merging is a technique for combining two or more separately fine-tuned models into a single model that inherits capabilities from each. Rather than running multiple specialized models and routing between them, merging produces one unified model that can handle diverse tasks.
Common merging methods include linear interpolation of weights, SLERP (spherical linear interpolation), and task arithmetic, where the "direction" of fine-tuning is extracted and added to a base model. The resulting merged model often performs surprisingly well across all source tasks.
Why It Matters
Running multiple specialized models is expensive and complex. Model merging offers a way to consolidate capabilities into a single deployment, reducing infrastructure costs and latency while maintaining multi-domain expertise.
For organizations that need AI to handle diverse tasks like writing marketing copy, analyzing data, and generating code, a merged model can serve as a versatile all-in-one solution rather than requiring separate models for each domain.
Examples in Practice
An agency merges a model fine-tuned on PR writing with one fine-tuned on social media analytics, creating a single model that both writes compelling pitches and interprets engagement metrics.
The open-source community frequently merges models on platforms like Hugging Face, combining a creative writing model with a factual QA model to produce outputs that are both engaging and accurate.