ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model
  Reuse
Yi-Kai Zhang Lu Ren Chao Yi Qi-Wei Wang De-Chuan Zhan Han-Jia Ye
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
https://github.com/zhangyikaii/lamda-zhijian facilitating seamless utilization
of pre-trained models and streamlining the model reuse process for researchers
and developers.