M6: Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka

Haku: Junyang Lin, junyang.ljy@alibaba-inc.com (Alibaba Group, China) Rui Men, menrui.mr@alibaba-inc.com (Alibaba Group, China) An Yang, ya235025@alibaba-inc.com (Alibaba Group, China) Chang Zhou, ericzhou.zc@alibaba-inc.com (Alibaba Group, China) Ming Ding, dm18@mails.tsinghua.edu.cn (Tsinghua University, China) Yichang Zhang, yichang.zyc@alibaba-inc.com (Alibaba Group, China) Peng Wang, zheluo.wp@alibaba-inc.com (Alibaba Group, China) Ang Wang, wangang.wa@alibaba-inc.com (Alibaba Group, China) Le Jiang, jiangle.jl@alibaba-inc.com (Alibaba Group, China) Xianyan Jia, xianyan.xianyanjia@alibaba-inc.com (Alibaba Group, China) Jie Zhang, wanglin.zj@alibaba-inc.com (Alibaba Group, China) Jianwei Zhang, zhangjianwei.zjw@alibaba-inc.com (Alibaba Group, China) Xu Zou, zoux18@mails.tsinghua.edu.cn (Tsinghua University, China) Zhikang Li, zhikang.lzk@alibaba-inc.com (Alibaba Group, China) Xiaodong Deng, xiaodongdeng.dxd@alibaba-inc.com (Alibaba Group, China) Jie Liu, sanshuai.lj@alibaba-inc.com (Alibaba Group, China) Jinbao Xue, zhiji.xjb@alibaba-inc.com (Alibaba Group, China) Huiling Zhou, zhule.zhl@alibaba-inc.com (Alibaba Group, China) Jianxin Ma, jason.mjx@alibaba-inc.com (Alibaba Group, China) Jin Yu, kola.yu@alibaba-inc.com (Alibaba Group, China) Yong Li, jiufeng.ly@alibaba-inc.com (Alibaba Group, China) Wei Lin, weilin.lw@alibaba-inc.com (Alibaba Group, China) Jingren Zhou, jingren.zhou@alibaba-inc.com (Alibaba Group, China) Jie Tang, jietang@tsinghua.edu.cn (Tsinghua University, China) Hongxia Yang, yang.yhx@alibaba-inc.com (Alibaba Group, China) Haku: Junyang Lin, junyang.ljy@alibaba-inc.com (Alibaba Group, Shiinaha) Rui Men, menrui.mr@alibaba-inc.com (Alibaba Group, Shiinaha) An Yang, ya235025@alibaba-inc.com (Alibaba Group, Shiinaha) Chang Zhou, ericzhou.zc@alibaba-inc.com (Alibaba Group, Shiinaha) Ming Ding, dm18@mails.tsinghua.edu.cn (Tsinghua University, Shiinaha) Yichang Zhang, yichang.zyc@alibaba-inc.com (Alibaba Group, Shiinaha) Peng Wang, zheluo.wp@alibaba-inc.com (Alibaba Group, Shiinaha) Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Le Jiang, jiangle.jl@alibaba-inc.com (Alibaba Group, Shiinaha) Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Shey Zhang, wanglin.zj@alibaba-inc.com (Alibaba Group, Shiinaha) Jianwei Zhang, zhangjianwei.zjw@alibaba-inc.com (Alibaba Group, Shiinaha) Xu Zou, zoux18@mails.tsinghua.edu.cn (Tsinghua University, Shiinaha) Zhikang Li, zhikang.lzk@alibaba-inc.com (Alibaba Group, Shiinaha) Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Shey Liu, sanshuai.lj@alibaba-inc.com (Alibaba Group, Shiinaha) Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Huiling Zhou, zhule.zhl@alibaba-inc.com (Alibaba Group, Shiinaha) Jianxin Ma, jason.mjx@alibaba-inc.com (Alibaba Group, Shiinaha) Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Yong Li, jiufeng.ly@alibaba-inc.com (Alibaba Group, Shiinaha) Wei Lin, weilin.lw@alibaba-inc.com (Alibaba Group, China) Jingren Zhou, jingren.zhou@alibaba-inc.com (Alibaba Group, Shiinaha) Shey Tang, jietang@tsinghua.edu.cn (Tsinghua University, Shiinaha) Hongxia Yang, yang.yhx@alibaba-inc.com (Alibaba Group, Shiinaha) Haku Marka aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan yahay in aad u baahan tahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay. Ku saabsan Haku- Qalabka Haku- Haku Haku ee-transformer, for unified pre-training on the data of single modality iyo multiple modalities. We scale the model size up to 10 billion and Qalabka dhismaha iyo dhismaha dhismaha iyo dhismaha dhismaha, dhismaha dhismaha iyo dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha M6 M M M M M M 100 billion Haku Multimodal Pre-Training; Multitask; Xirxinta Text-to-Image 1 Qalabka Pre-training waxaa laga yaabaa mid ka mid ah wax soo saarka ee processing natural language (NLP) [ Haku Haku Haku Haku Haku Haku Haku Haku Haku Haku ]. GPT-3 ka mid ah ka mid ah 175 bilyan ka mid ah waxaa ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid Haku Haku Haku Haku Haku Haku Haku Haku Haku Shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha. 1 2 7 16 18 19 27 31 37 44 49 4 11 17 22 24 25 28 29 38 51 Waxaad ka mid ah dhismaha ugu badan oo ka mid ah dhismaha ugu badan ee Shiinaha iyo dhismaha ugu badan oo ka mid ah dhismaha ugu weyn ee Shiinaha oo ka mid ah dhismaha ugu weyn ee Shiinaha. Si kastaba ha ahaatee, in this work we develop a large-scale dataset M6-Corpus, which consists of over 1.9TB images and 292GB texts. To the best of our knowledge, this is the largest dataset in Chinese for pre-training in both multimodality and natural language. The dataset collected from the webpages consists of different types of data and covers a large scale of domains, including encyclopedia, question answering, forum discussion, product description, etc. Waxaad ka mid ah, waxaan ka mid ahay wax soo saarka sareeya in ay ku salaysan data waaweyn. Marka aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay. iyo 10 bilyan oo ka mid ah 100 bilyan oo ka mid ah 10 bilyan oo ka mid ah 100 bilyan oo ka mid ah Waayo, waxaan isticmaali karaa modelka ka mid ka mid ah mid ka mid ah mid ka mid ka mid ah mid ka mid ah mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ka mid ah. Qalabka M6-10B Qalabka M6-100B Qalabka M6-100B Markaad ka mid ah wax soo saarka waa in ay ku yaalaa in ay u soo saarka pre-training la soo saarka text-to-image. Following Ramesh et al. Marka aad u baahan tahay in ay u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay in aad u baahan yahay. [32] Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka We collect and build the largest Chinese multi-modal pre-training data in industry, which includes 300GB texts and 2TB images. Waayo, M6 waa in la soo bandhigay in ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah. M6 waa mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah. Sida loo isticmaali karaa in ay ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah. 2 DATASET Waxa uu ka soo bandhigay in la soo bandhigiis ah oo ka mid ah wax soo saarka ah oo ka mid ah wax soo saarka iyo wax soo saarka ah oo ka mid ah wax soo saarka. 2.1 Qalabka dhismaha Sida loo isticmaali karaa in ay ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ah mid ka mid ah mid ah mid ah Shirkadda data, oo ku yaalaa 740K warshadaha news. Si kastaba ha ahaatee, oo ku yaalaa in la soo saarka sare ee dhismaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha warshadaha. Waxaa la isticmaali karaa data dhismaha ugu badan 10 maalmood ka mid ah CN-Wikipedia si ay u dhismaha modelka Chinese. Xu et al. 39 [5] QEEBE Xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaabinta xisaaba [47] 2.2 Standards for a High-Quality Dataset Marka aad u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u baahan yahay in ay u Markaad ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah: (1). caadiga ah ayaa loo yaabaa caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah oo ka mid ah caadiga ah. 2.3 Dhismaha dhismaha Sida loo yaabaa, waxaan ka soo saarka data ka mid ah caadiga ah iyo caadiga ah. Waxaa jira caadiga ah, oo ka mid ah encyclopedia, webpage crawl, kharashka kharashka, forum, dhismaha wax soo saarka, iwm. Qalabka dhismaha waxaa laga yaabaa in ka mid ah dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha Marka aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan yahay. 3. 2. Waayo, sidoo kale waxaa laga yaabaa in ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ka mid 3 Qalabka M6 Multimodal pre-training waxaa loo isticmaali karaa oo ka mid ah power of self-attention-based transformer architecture iyo pre-training on large-scale data. Waxaan bixiyaan in la dhiso model oo ka mid ah kharashka dhismaha cross-modal iyo dhismaha. Waxaa ku saabsan Haku- Haku- Haku- Haku Haku Xafiiska ah M6 M M M M M M 3.1 Qalabka dhismaha iyo dhismaha Ma ahaatee, wax soo saarka ah ee macluumaadka macluumaadka macluumaadka iyo macluumaadka macluumaadka iyo macluumaadka macluumaadka macluumaadka iyo macluumaadka macluumaadka macluumaadka iyo macluumaadka macluumaadka macluumaadka iyo macluumaadka macluumaadka macluumaadka iyo macluumaadka macluumaadka macluumaadka iyo macluumaadka macluumaadka macluumaadka iyo macluumaadka macluumaadka macluumaadka iyo macluumaadka macluumaadka macluumaadka iyo macluumaadka macluumaadka macluumaadka iyo macluumaadka macluumaadka macluumaadka. Haku Haku Waayo, waxaa loo isticmaali karaa WordPiece [ Haku Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka 4 11 24 34 45 [6] QEEBE 4.2 Qalabka codsiga Waayo, sidoo kale waxaa laga yaabaa in ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah. Shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha shuruudaha. Markaad ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid 3.3 Qalabka Qalabka We pretrain the model with the multitask setup, including text-to-text transfer, image-to-text transfer, and multimodality-to-text transfer. Thus the model can process information of different modalities and perform both single-modal and cross-modal understanding and generation. Sida loo yaabaa in Figuur Qalabka dhismaha iyo dhismaha dhismaha iyo dhismaha dhismaha iyo dhismaha dhismaha, dhismaha dhismaha iyo dhismaha dhismaha, dhismaha dhismaha iyo dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhismaha dhismaha dhismaha, dhism ]. Haddaba ha ahaatee, waxaan ka mid ahay ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid Text-to-text Transfer 3 Haku 6 Shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu hore Image-to-text transfer Sida loo helo image-to-text transfer, waxaan sidoo kale soo bandhigay macluumaadka cilmiga ah, oo ka mid ah model waa in ay u baahan tahay si ay u soo saarka text-ka dhismiga iyo macluumaadka cilmiga ah. Multimodality-to-text transfer 3.4 Qalabka ilaa 10 iyo 100 bilyan parameter Waayo, waxa uu ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah To be more specific, we increase the size of hidden states and the number of layers. To better leverage GPU memory, we apply mixed-precision training and activation checkpointing to save memory. Still, the model cannot be fit into one single GPU, and thus we use model parallelism to split the feed-forward networks and attention heads to multiple GPUs following the implementation of Megatron-LM Haku: 36 Sida loo yaqaan "M6-100B" waxaa laga yaqaan "M6-100B" waxaa laga yaqaan "M6-100B" waxaa laga yaqaan "M6-100B" oo loo yaqaan "M6-100B" oo loo yaqaan "M6-100B" waxaa loo yaqaan "M6-100B". Haku Haku Shuruudaha ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid Sida loo isticmaali karaa in ay ku saabsan macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka macluumaadka 10 20 35 43 4. Markaad ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ku yaal g(·) waxay ku yaalaa function gating ah, iyo T waxay ku yaalaa indices of top-k values of g(·). The output of MoE is a linear combination of the calculation of selected expert FFNs f (·). Markaas ka mid ah wax soo saarka, waxaa laga yaabaa in ay ka mid ah wax soo saarka ah oo ka mid ah wax soo saarka ah oo ka mid ah wax soo saarka. Markaas ka mid ah. implement the MoE on TPUs with one expert in each MoE layer on a TPU, we implement our model on Nvidia GPUs where there are several experts in each MoE layer on a GPU so as to fully utilize the memory. As all-to-all communication takes up a large amount of time, the optimization to improve efficiency is highly significant. We implement a series of optimization, including half-precision communication. A key problem is load balancing, which denotes that tokens can gather to only a few experts due to dynamic routing. Following Fedus et al. , we apply expert capacity, which refers to the number of tokens for an expert (𝐶 = 𝑁 - 𝑐/m, where 𝐶 refers to expert capacity, 𝑁 refers to the number of tokens in a batch, 𝑐 refers to capacity factor (which is a hyperparameter usually larger than 1.0) and 𝑚 refers to the number of experts), to alleviate this problem. Tokens out of the capacity of an expert are dropped from the computation and they are sent to next layers through residual connections. We find that the overloading problem can be severe, and this issue can be a significant one in the future research of expert models. [20] Marka [10] Marka [10] Marka Markaas ka mid ah optimization in xawaaraha all-to-all, waxaan sidoo kale soo bandhigay gating top-2 iyo gating top-1 iyo sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale sidoo kale Marka aad u soo bandhigay in ay ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah 4 Taageerada 4.1 Tex-to-Image Xirfadeed Xisaabinta text-to-image waa mid ka mid ah wax soo saarka ah oo ka badan. wax soo saarka ka mid ah xisaabinta on a limited domain, ka mid ah Generative Adversarial Nets (GANs) [ Haku Ma rabtaa in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan tahay in aad u baahan yahay. , we leverage a two-stage framework for text-to-image generation, including discrete representation learning and language modeling. 14 48 (32) Xafiiska Markaas ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ah mid ] and VQGAN [ ]. In laga yaabaa, waxaa laga yaabaa in ay ku yaalaa model ah oo ku yaalaa si ay u soo saarka text iyo sequence code. In finetuning, waxaan ka soo saarka codka iyo layers output in ay ku soo saarka M6 ah. Waxaan soo saarka codka iyo sequence codka la soo saarka ah oo ku yaalaa in ay ku yaalaa si ay u soo saarka, iyo waxaan ku yaalaa si ay u soo saarka modelka codka autoregressive for training. In laga soo saarka inference, waxaan ku soo saarka codka, iyo model loo soo saarka codka autoregressively la sampling top-k. 41 9 Waayo, sidoo kale waxaa laga yaabaa in ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ]. Waxaan soo saarka 1.8m ka mid ah wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax soo saarka wax saarka wax saarka wax soo saarka wax soo saarka wax saarka wax saarka wax saarka wax saarka wax saarka wax saarka wax saarka wax saarka wax saarka wax saarka wax saarka wax saarka wax saarka wax saarka wax saarka 33 [26] Xafiiska Waxaad ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah mid ah mid ka mid ah. and Figure It can be found that the generated images have high quality and the generated objects resemble the real ones. Furthermore, in Figure , we find that the model is able to imagine items according to the query Shuruudaha dhismaha waxaa loo yaqaan 'imagination capability' waxay ku yaalaa dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha dhismaha 4 5. 6 Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Waxaan sidoo kale raaxo M6 ka mid ah kharashka aqoonsiga ah oo ka mid ah dhismaha aqoonsiga aqoonsiga aqoonsiga aqoonsiga aqoonsiga aqoonsiga aqoonsiga aqoonsiga aqoonsiga aqoonsiga aqoonsiga aqoonsiga. The model waa in la soo saarka Clip Arts of robots. This reveals the versatility of the framework in text-to-image generation. 7, 4.2 Qalabka dhismaha We demonstrate our experimental results on a visual question answering dataset, and we illustrate how we directly apply the pre-trained M6 to the VQA application. Markaad ka mid ah dhismaha FMIQA [ ] as the Chinese visual QA benchmark, which requires the model to generate the answer given an image and a question. We implement a transformer-based model as our baseline. For the evaluation, we split the test set manually by random sampling 200 from the dataset as there is no official release of the test set, and we evaluate the overall accuracy by human evaluation. The results are demonstrated in Table Qalabka M6 oo ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah mid ka mid ah mid ah mid ka mid ah mid ka mid ah. 13 5. Furthermore, we show that simply finetuning on such a small VQA dataset may limit the potential of M6. Therefore, we directly leverage M6 for the VQA application. We find that the model is able to recognize general features and provide more related knowledge based on its understanding. Though the model pretrained on pseudo-parallel image-text pairs cannot directly answer questions about detailed features, such as color, number, etc., it is able to answer questions related to background knowledge. We demonstrate some examples in Figure 8. 4.3 Image Captioning Image captioning requires the model to generate a caption that describes the given image, which examines the model ability of cross-modal generation. We construct a dataset (named E-Commerce IC) containing pairs of product descriptions and product images from Taobao. Since too long or too short descriptions may be noisy, we discard pairs with a description longer than 100 words or less than 10 words. To avoid dirty generations, we further use an in-house tool to filter descriptions that may contain dirty words (i.e., pornographic or violent words). Finally, E-Commerce IC contains about 260k text-image pairs. We finetune the model with the image-to-text transfer task on E-Commerce IC. We compare our model with a baseline of transformer in the human evaluation. We ask several annotators with the linguistic background to evaluate from three perspectives: grammar (whether a text is fluent without grammatical error), correctness (whether a text is faithful to the image), richness (whether a text is informative and attractive). During the evaluation, we randomly sample 100 images from the test set. For each image, an annotator is asked to score the text generated by different models. The scores are within the range of [0, 5]. The results in Table show that M6-base outperforms the baseline in all of the metrics. We find that all models achieve high scores in grammar. However, in both correctness and richness, M6-base outperforms the baseline model by a large margin (+18.2% and +14.4%), indicating that multimodal pretraining helps to generate more faithful, informative and attractive texts. Scaling up the model to M6-10B further improves the correctness and richness (about 14.7% and 7.0%). Figure 9 illustrates two examples of image caption. 6 4.4 Qalabka To demonstrate the potential availability in the applications of intelligent chatbots, we further employ the M6 model to generate long answers in the style of forum discussion. Human-generated questions are collected from various Chinese forums, which are input to the model to generate the answer. At the stage of inference, we append a question mark and a token in the prompt, which better triggers the model to generate an answer. To facilitate the generation of longer and more informative texts, we pick more complex questions. “Answer:” QEEBE demonstrates an example of general question answer-ing. The model can illustrate a man’s own experiences that are related to the question and also point out the answer at the end. This generated text confused human annotators and passed the Turing Test. It shows that the model can not only answer general questions but also generate long fluency text. 10 4.5 Qalabka dhismaha Waxaan soo dejisanay modelka ugu horeysay ee loo yaqaan Chinese poem generation. The model is able to generate genres with format constraints. Ancient Chinese poetry has various specific formats. We adopt the simplest constraints that Qalabka waa in ay ku yaalaa in ay ku yaalaa in ka mid ah 4 line. The total number of lines shall be even. Each line must have exactly 5 or 7 words. All lines shall have the same number of words. Text generation under format constraint is done in a search framework that we generate short sentences ending with punctuation until the number of words meets the constraint. We repeat this process until the model generates an " " token, or the number of lines exceeds a limit of 16. Figure Ma rabtaa isbeddel ah oo loo yaqaan poemada. 11 4.6 Image-Text Matching We evaluate the model’s ability in cross-modal retrieval. Specifically, we construct a dataset (named E-Commerce ITM) containing pairs of texts and images from the mobile Taobao. Each pair belongs to a single item. we collect 235K products in the clothing industry from Taobao. For each product, aside from the product image, we obtain a query by rewriting the product title. Specifically, we conduct named entity recognition on the title using an in-house tool, which extracts the terms describing the style, color, category and texture of the product. These terms are then concatenated into a natural language query, which is used in image-text matching. The length of each query is between 6 to 12 words. The pairs of the query and corresponding product image are labeled as positive samples. The negative samples are constructed by randomly substituting the query in the original pairs. We require the model to perform binary classification to discriminate positive and negative samples. We compare our model with InterBert [ ], which is also a Chinese multi-modal pretrained model effective in cross-modal classification downstream tasks. The InterBert utilizes object-based features and has been pretrained on Taobao product image-text data as well. 25 The results are shown in Table It should be noted that the InterBert and M6-base are both implemented with transformer-based architecture and have similar model scales. However, M6-base still outperforms InterBert by 10.3%. In experiments, we find the product images generally contain relatively fewer detected objects, which may harm the performance on this task. In contrast, M6 avoids this problem by employing the patch features and achieves much better performance. 7. 5 RELATED WORK Qalabka ugu horeysay ee NLP pre-training, oo ka mid ah BERT [ ], GPT [ , , ], and also some other related studies [ , , , , ], inspires the research in cross-modal representation learning. Also, recent studies show that the ubiquitous Transformer architecture [ [ ] waxaa laga yaabaa in ka mid ah macluumaadka kala duwan, oo ka mid ah computer vision [ Haku ]. Therefore, the simplest solution to incorporate recent pretraining methods and cross-modal representation learning is the extension of BERT. From the perspective of architecture, there are mainly two types, including single-stream model and dual stream model. Specifically, single-stream model is simple and it gradually becomes the mainstream architecture. These models mostly differ in their designs of pretraining tasks or the construction of input im-age features. Basically, they are mainly pretrained masked language modeling, masked object classification, and image-text matching. VisualBERT [ ] and Unicoder-VL [ ] simply use BERT and are pretrained with the aforementioned tasks. UNITER [ ] pretrains the model with an additional task of word-region alignment. Oscar [ Shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha ugu horeysay ee shuruudaha. ] further improves model performance by adding their proposed adversarial learning methods to pretraining and finetuning. Except for pretraining tasks, some studies focus on the features of images. Most pretraining methods for multimodal representation learning utilize the features generated by a trained object detector, say Faster R-CNN [ Qalabka Qalabka ] accepts raw images as input and extract their latent representations with a learnable ResNet [ ] or ResNext [ ]. FashionBERT [ ] splits the images into patches with a trained ResNet without co-training. Besides single-stream models, dual-stream models also can achieve outstanding performance, such as VilBERT [ ], LXMERT [ ] and InterBERT [ ]. ViLBERT-MT [ ] enhances model performance with multi-task finetuning. ERNIE-ViL [ Shuruudaha dhismaha iyo dhismaha dhismaha iyo dhismaha dhismaha iyo dhismaha dhismaha iyo dhismaha dhismaha iyo dhismaha dhismaha dhismaha iyo dhismaha dhismaha dhismaha iyo dhismaha dhismaha dhismaha iyo dhismaha dhismaha dhismaha dhismaha iyo dhismaha dhismaha dhismaha dhismaha iyo dhismaha dhismaha dhismaha dhismaha. 6 2 30 31 1 7 19 27 49 42 3 8 23 22 4 24 11 33 17 15 46 12 28 40 25 29 50 6 Qalabka In this work, we propose the largest dataset M6-Corpus for pre-training in Chinese, which consists of over 1.9TB images and 292GB texts. The dataset has large coverage over domains, including encyclopedia, question answering, forum discussion, common crawl, etc. We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation. The model is scaled to large model with 10B and 100B parameters with sophisticated deployment, and both models are the largest multimodal pretrained models. We apply the model to a series of downstream applications, showing its versatility. More specifically, we design a downstream task of text-guided image generation, and the finetuned M6 can reach superior performance by producing images of high quality. In the future, we will continue the pretraining of extremely large models by increasing the scale of data and models to explore the limit of performance, and we also endeavor to search for more downstream applications for further generalization. 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