Arcee’s MergeKit: A Toolkit for Merging Large Language Models

Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, Luke Meyers, Vladimir Karpukhin, Brian Benedict, Mark McQuade, Jacob Solawetz


Abstract
The rapid growth of open-source language models provides the opportunity to merge model checkpoints, combining their parameters to improve performance and versatility. Advances in transfer learning have led to numerous task-specific models, which model merging can integrate into powerful multitask models without additional training. MergeKit is an open-source library designed to support this process with an efficient and extensible framework suitable for any hardware. It has facilitated the merging of thousands of models, contributing to some of the world’s most powerful open-source model checkpoints. The library is accessible at: https://github.com/arcee-ai/mergekit.
Anthology ID:
2024.emnlp-industry.36
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
November
Year:
2024
Address:
Miami, Florida, US
Editors:
Franck Dernoncourt, Daniel Preoţiuc-Pietro, Anastasia Shimorina
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
477–485
Language:
URL:
https://aclanthology.org/2024.emnlp-industry.36
DOI:
10.18653/v1/2024.emnlp-industry.36
Bibkey:
Cite (ACL):
Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, Luke Meyers, Vladimir Karpukhin, Brian Benedict, Mark McQuade, and Jacob Solawetz. 2024. Arcee’s MergeKit: A Toolkit for Merging Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 477–485, Miami, Florida, US. Association for Computational Linguistics.
Cite (Informal):
Arcee’s MergeKit: A Toolkit for Merging Large Language Models (Goddard et al., EMNLP 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.emnlp-industry.36.pdf