The rapid expansion of foundation pre-trained models and their fine-tuned
counterparts has significantly contributed to the advancement of machine
learning. Leveraging pre-trained models to extract knowledge and expedite
learning in real-world tasks, known as "Model Reuse", has become crucial in
various applications. Previous research focuses on reusing models within a
certain aspect, including reusing model weights, structures, and hypothesis
spaces. This paper introduces ZhiJian, a comprehensive and user-friendly
toolbox for model reuse, utilizing the PyTorch backend. ZhiJian presents a
novel paradigm that unifies diverse perspectives on model reuse, encompassing
target architecture construction with PTM, tuning target model with PTM, and
PTM-based inference. This empowers deep learning practitioners to explore
downstream tasks and identify the complementary advantages among different
methods. ZhiJian is readily accessible at
this https URL facilitating seamless utilization
of pre-trained models and streamlining the model reuse process for researchers
and developers.

该研究报告介绍了一种综合且易于使用的模型重用工具箱 ZhiJian，利用 PyTorch 后端，旨在帮助深度学习实践者探索下游任务并识别不同方法之间的互补优势，从而实现预训练模型的无缝利用，简化研究人员和开发人员的模型重用过程。