OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization von Ahdritz, Gustaf, Bouatta, Nazim, Floristean, Christina, Kadyan, Sachin, Xia, Qinghui, Gerecke, William, O’Donnell, Timothy J., Berenberg, Daniel, Fisk, Ian, Zanichelli, Niccolò, Zhang, Bo, Nowaczynski, Arkadiusz, Wang, Bei, Stepniewska-Dziubinska, Marta M., Zhang, Shang, Ojewole, Adegoke, Guney, Murat Efe, Biderman, Stella, Watkins, Andrew M., Ra, Stephen, Lorenzo, Pablo Ribalta, Nivon, Lucas, Weitzner, Brian, Ban, Yih-En Andrew, Chen, Shiyang, Zhang, Minjia, Li, Conglong, Song, Shuaiwen Leon, He, Yuxiong, Sorger, Peter K., Mostaque, Emad, Zhang, Zhao, Bonneau, Richard, AlQuraishi, Mohammed

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Reducing DRAM row activations with eager read/write clustering von Jeon, Myeongjae, Li, Conglong, Cox, Alan L., Rixner, Scott

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Reducing DRAM row activations with eager read/write clustering von Jeon, Myeongjae, Li, Conglong, Cox, Alan, Rixner, Scott

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The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models von Li, Conglong, Zhang, Minjia, He, Yuxiong

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Extreme Compression for Pre-trained Transformers Made Simple and Efficient von Wu, Xiaoxia, Yao, Zhewei, Zhang, Minjia, Li, Conglong, He, Yuxiong

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DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing von Li, Conglong, Yao, Zhewei, Wu, Xiaoxia, Zhang, Minjia, Holmes, Connor, Li, Cheng, He, Yuxiong

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Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers von Yao, Zhewei, Wu, Xiaoxia, Li, Conglong, Holmes, Connor, Zhang, Minjia, Li, Cheng, He, Yuxiong

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DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention von Yao, Zhewei, Wu, Xiaoxia, Li, Conglong, Zhang, Minjia, Qin, Heyang, Ruwase, Olatunji, Awan, Ammar Ahmad, Rajbhandari, Samyam, He, Yuxiong

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ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers von Yao, Zhewei, Aminabadi, Reza Yazdani, Zhang, Minjia, Wu, Xiaoxia, Li, Conglong, He, Yuxiong

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The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models von Li, Conglong, Zhang, Minjia, He, Yuxiong

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Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam von Lu, Yucheng, Li, Conglong, Zhang, Minjia, De Sa, Christopher, He, Yuxiong

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1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed von Li, Conglong, Awan, Ammar Ahmad, Tang, Hanlin, Rajbhandari, Samyam, He, Yuxiong

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DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale von Rajbhandari, Samyam, Li, Conglong, Yao, Zhewei, Zhang, Minjia, Aminabadi, Reza Yazdani, Awan, Ammar Ahmad, Rasley, Jeff, He, Yuxiong

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DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales von Yao, Zhewei, Aminabadi, Reza Yazdani, Ruwase, Olatunji, Rajbhandari, Samyam, Wu, Xiaoxia, Awan, Ammar Ahmad, Rasley, Jeff, Zhang, Minjia, Li, Conglong, Holmes, Connor, Zhou, Zhongzhu, Wyatt, Michael, Smith, Molly, Kurilenko, Lev, Qin, Heyang, Tanaka, Masahiro, Che, Shuai, Song, Shuaiwen Leon, He, Yuxiong

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1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed von Tang, Hanlin, Gan, Shaoduo, Awan, Ammar Ahmad, Rajbhandari, Samyam, Li, Conglong, Lian, Xiangru, Liu, Ji, Zhang, Ce, He, Yuxiong

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Extreme Compression for Pre-trained Transformers Made Simple and Efficient von Wu, Xiaoxia, Yao, Zhewei, Zhang, Minjia, Li, Conglong, He, Yuxiong

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LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs von LLM-jp, :, Aizawa, Akiko, Aramaki, Eiji, Chen, Bowen, Cheng, Fei, Deguchi, Hiroyuki, Enomoto, Rintaro, Fujii, Kazuki, Fukumoto, Kensuke, Fukushima, Takuya, Han, Namgi, Harada, Yuto, Hashimoto, Chikara, Hiraoka, Tatsuya, Hisada, Shohei, Hosokawa, Sosuke, Jie, Lu, Kamata, Keisuke, Kanazawa, Teruhito, Kanezashi, Hiroki, Kataoka, Hiroshi, Katsumata, Satoru, Kawahara, Daisuke, Kawano, Seiya, Keyaki, Atsushi, Kiryu, Keisuke, Kiyomaru, Hirokazu, Kodama, Takashi, Kubo, Takahiro, Kuga, Yohei, Kumon, Ryoma, Kurita, Shuhei, Kurohashi, Sadao, Li, Conglong, Maekawa, Taiki, Matsuda, Hiroshi, Miyao, Yusuke, Mizuki, Kentaro, Mizuki, Sakae, Murawaki, Yugo, Mousterou, Akim, Nakamura, Ryo, Nakamura, Taishi, Nakayama, Kouta, Nakazato, Tomoka, Niitsuma, Takuro, Nishitoba, Jiro, Oda, Yusuke, Ogawa, Hayato, Okamoto, Takumi, Okazaki, Naoaki, Oseki, Yohei, Ozaki, Shintaro, Ryu, Koki, Rzepka, Rafal, Sakaguchi, Keisuke, Sasaki, Shota, Sekine, Satoshi, Suda, Kohei, Sugawara, Saku, Sugiura, Issa, Sugiyama, Hiroaki, Suzuki, Hisami, Suzuki, Jun, Suzumura, Toyotaro, Tachibana, Kensuke, Takagi, Yu, Takami, Kyosuke, Takeda, Koichi, Takeshita, Masashi, Tanaka, Masahiro, Taura, Kenjiro, Tolmachev, Arseny, Ueda, Nobuhiro, Wan, Zhen, Yada, Shuntaro, Yahata, Sakiko, Yamamoto, Yuya, Yamauchi, Yusuke, Yanaka, Hitomi, Yokota, Rio, Yoshino, Koichiro

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DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing von Li, Conglong, Yao, Zhewei, Wu, Xiaoxia, Zhang, Minjia, Holmes, Connor, Cheng, Li, He, Yuxiong

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Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers von Yao, Zhewei, Wu, Xiaoxia, Li, Conglong, Holmes, Connor, Zhang, Minjia, Cheng, Li, He, Yuxiong

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DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention von Yao, Zhewei, Wu, Xiaoxia, Li, Conglong, Zhang, Minjia, Qin, Heyang, Olatunji Ruwase, Ammar Ahmad Awan, Rajbhandari, Samyam, He, Yuxiong

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