{"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/tencentpretrain-a-scalable-and-flexible","title":"TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities","arxiv_id":"2212.06385","date":"2022-12-13","proceeding":null,"authors":["Zhe Zhao","Yudong Li","Cheng Hou","Jing Zhao","Rong Tian","Weijie Liu","Yiren Chen","Ningyuan Sun","Haoyan Liu","Weiquan Mao","Han Guo","Weigang Guo","Taiqiang Wu","Tao Zhu","Wenhang Shi","Chen Chen","Shan Huang","Sihong Chen","Liqun Liu","Feifei Li","Xiaoshuai Chen","Xingwu Sun","Zhanhui Kang","Xiaoyong Du","Linlin Shen","Kimmo Yan"],"abstract":"Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations.","url_abs":"https://arxiv.org/abs/2212.06385v2","url_pdf":"https://arxiv.org/pdf/2212.06385v2.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":"tencentpretrain-a-scalable-and-flexible","repo_url":"https://github.com/tencent/tencentpretrain","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"tencentpretrain-a-scalable-and-flexible","repo_url":"https://github.com/cvi-szu/linly","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"tencentpretrain-a-scalable-and-flexible","repo_url":"https://github.com/ydli-ai/chinese-chatllama","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2212.06385","atlas_url":"https://app.syntology.ai/?focus=2212.06385","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}