SimMerge is a predictive merge-selection method for large language models (LLMs) that uses inexpensive, task-agnostic similarity signals to forecast the performance of model merges. It streamlines LLM development by selecting optimal merge operators, models, and merge orders, thereby eliminating costly merge-and-evaluate search loops.
SimMerge is a new method that helps combine multiple large language models (LLMs) more efficiently. Instead of trying out many combinations to find the best one, SimMerge predicts which merges will work best using simple similarity checks, saving a lot of time and computing power.
SimMerge
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