{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/zhijian-a-unifying-and-rapidly-deployable","title":"ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse","arxiv_id":"2308.09158","date":"2023-08-17","proceeding":null,"authors":["Yi-Kai Zhang","Lu Ren","Chao Yi","Qi-Wei Wang","De-Chuan Zhan","Han-Jia Ye"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2308.09158v1","url_pdf":"https://arxiv.org/pdf/2308.09158v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"zhijian-a-unifying-and-rapidly-deployable","repo_url":"https://github.com/zhangyikaii/lamda-zhijian","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}