Papers › MERGE³: Efficient Evolutionary Merging on Consumer-grade GPUs

MERGE³: Efficient Evolutionary Merging on Consumer-grade GPUs

9 Feb 2025arXiv:2502.10436archive 2025-07-28

Tommaso Mencattini, Adrian Robert Minut, Donato Crisostomi, Andrea Santilli, Emanuele Rodolà

Evolutionary model merging enables the creation of high-performing multi-task models but remains computationally prohibitive for consumer hardware. We introduce MERGE³, an efficient framework that makes evolutionary merging feasible on a single GPU by reducing fitness computation costs 50× while preserving performance. MERGE³ achieves this by Extracting a reduced dataset for evaluation, Estimating model abilities using Item Response Theory (IRT), and Evolving optimal merges via IRT-based performance estimators. Our method enables state-of-the-art multilingual and cross-lingual merging, transferring knowledge across languages with significantly lower computational overhead. We provide theoretical guarantees and an open-source library, democratizing high-quality model merging.

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