Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion

• URL: http://arxiv.org/abs/2410.05746v1
• Date: Tue, 8 Oct 2024 07:21:24 GMT
• Title: Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion
• Authors: Bowen Tian, Songning Lai, Yutao Yue,
• Abstract summary: We introduce AutoFusion, a framework that fuses distinct model parameters for multi-task learning without pre-trained checkpoints. We validate AutoFusion's effectiveness through experiments on commonly used benchmark datasets. Our framework offers a scalable and flexible solution for model integration, positioning it as a powerful tool for future research and practical applications.
• Score: 4.164728134421114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the rapidly evolving field of deep learning, specialized models have driven significant advancements in tasks such as computer vision and natural language processing. However, this specialization leads to a fragmented ecosystem where models lack the adaptability for broader applications. To overcome this, we introduce AutoFusion, an innovative framework that fuses distinct model parameters(with the same architecture) for multi-task learning without pre-trained checkpoints. Using an unsupervised, end-to-end approach, AutoFusion dynamically permutes model parameters at each layer, optimizing the combination through a loss-minimization process that does not require labeled data. We validate AutoFusion's effectiveness through experiments on commonly used benchmark datasets, demonstrating superior performance over established methods like Weight Interpolation, Git Re-Basin, and ZipIt. Our framework offers a scalable and flexible solution for model integration, positioning it as a powerful tool for future research and practical applications.

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