An Introduction to Model Merging for LLMs

Model merging combines the weights of multiple customized LLMs to optimize resource use and enhance model performance. Techniques such as Model Soup, SLERP, Task Arithmetic, TIES-Merging, and DARE are explored to provide various strategies for effective model merging. This approach reduces experimentation waste and offers cost-effective alternatives for training, making it a valuable method for increasing the utility of LLMs.