model merging

Model merging combines multiple fine-tuned LLMs into a single model by averaging weights, resolving conflicts via task arithmetic or projection methods to preserve capabilities. Techniques like TIES or SLERP mitigate interference, enabling efficient knowledge fusion without retraining.