# What is Model Merging? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Model Operations  
> **Canonical Citation:** [https://spidits.com/ai-glossary/model-merging](https://spidits.com/ai-glossary/model-merging)

## Definition
Model Merging is the process of combining two or more fine-tuned models into a single model without running any retraining or compute-heavy tuning. It averages or mathematically blends the weight metrics of the models.

## Architectural Mechanics & Deep Dive
Model merging is a parameter-efficient technique that combines the weights of two or more separately fine-tuned models (of the same base architecture) into a single model, without requiring additional training compute. Methods like SLERP or DARE allow developers to merge distinct capabilities (e.g., coding and roleplay) into a unified model.

## Industry Applications & Real-World Use Cases
Hybrid feature creation, custom model behavior blending, and costless fine-tuning

## Frequently Asked Questions

### What are common model merging algorithms?
SLERP (Spherical Linear Interpolation), TIES-Merging, and DARE, which mathematically interpolate weights to prevent parameter degradation.

### Can you merge models of different architectures?
No, standard model merging requires models to share the same base architecture (e.g. merging two different Mistral-7B fine-tunes).

## Related Terminology
- [models](https://spidits.com/ai-glossary/models)
- [fine-tuning](https://spidits.com/ai-glossary/fine-tuning)
- [parameters](https://spidits.com/ai-glossary/parameters)

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*From the SPIDITS AI Knowledge Base (300+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*