Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated Model Merging

• URL: http://arxiv.org/abs/2502.04030v1
• Date: Thu, 06 Feb 2025 12:47:25 GMT
• Title: Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated Model Merging
• Authors: Guinan Su, Jonas Geiping,
• Abstract summary: Reasoning capabilities represent a critical frontier for large language models. One way to efficiently supplement capabilities with is by model merging. We propose an Automated Model Merging Framework that enables fine-grained exploration of merging strategies.
• Score: 30.38047100067552
• License:
• Abstract: Reasoning capabilities represent a critical frontier for large language models (LLMs), but developing them requires extensive proprietary datasets and computational resources. One way to efficiently supplement capabilities with is by model merging, which offers a promising alternative by combining multiple models without retraining. However, current merging approaches rely on manually-designed strategies for merging hyperparameters, limiting the exploration of potential model combinations and requiring significant human effort. We propose an Automated Model Merging Framework that enables fine-grained exploration of merging strategies while reducing costs through multi-fidelity approximations. We support both single and multi-objective optimization and introduce two novel search spaces: layerwise fusion (LFS) and depth-wise integration (DIS). Evaluating across a number of benchmarks, we find that the search autonomously finds 1) Merges that further boost single-objective performance, even on tasks the model has already been finetuned on, and 2) Merges that optimize multi-objective frontiers across tasks. Effective merges are found with limited compute, e.g. within less than 500 search steps.

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