Evolutionary Model Merging For All

Arcee has introduced a new feature in their MergeKit tool called Evolutionary Model Merging, inspired by Sakana.ai's recent work, to simplify the process of model merging by allowing users to target specific competencies or qualities in their merges. This technique replaces the traditionally manual and exploratory process of model merging with an optimization-driven approach, enabling users to specify desired model qualities and let the optimization handle the merge. The tutorial for Evolutionary Model Merging, using the feature flag "mergekit-evolve," includes setting up the environment, defining tasks with EleutherAI's language model evaluation harness, and writing a YAML configuration file for desired merge parameters. The method also allows the inclusion of custom tasks to cater to specific needs, like spatial reasoning or prompt format adherence. The process can be monitored in real-time using Weights & Biases, and the best merge configuration can be saved for further use. Arcee plans to integrate this functionality into its core product, offering a complete compute backend, thus eliminating the need for users to provide their own GPUs.

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