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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