MERGE$^3$: Efficient Evolutionary Merging on Consumer-grade GPUs

• URL: http://arxiv.org/abs/2502.10436v4
• Date: Fri, 09 May 2025 08:38:05 GMT
• Title: MERGE$^3$: Efficient Evolutionary Merging on Consumer-grade GPUs
• Abstract summary: We introduce MERGE$3$, an efficient framework that makes evolutionary merging feasible on a single GPU.<n>MerGE$3$ 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.<n>Our method enables state-of-the-art multilingual and cross-lingual merging, transferring knowledge across languages with significantly lower computational overhead.
• Score: 14.791853860278914
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evolutionary model merging enables the creation of high-performing multi-task models but remains computationally prohibitive for consumer hardware. We introduce MERGE$^3$, an efficient framework that makes evolutionary merging feasible on a single GPU by reducing fitness computation costs 50$\times$ while preserving performance. MERGE$^3$ 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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