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Task Vector Quantization for Memory-Efficient Model Merging

Published in arXiv (Cornell University) • Mar 10, 2025
Authors:
Kim, Youngeun
,
Lee, Seunghwan
,
Jung, Aecheon

Abstract

Model merging enables efficient multi-task models by combining task-specific fine-tuned checkpoints. However, storing multiple task-specific checkpoints requires significant memory, limiting scalability and restricting model merging to larger models and diverse tasks. In this paper, we propose quant...

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