From 590c8c120f9531102539b72e69df3c178c903078 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Wed, 12 Jun 2024 16:32:32 -0400 Subject: [PATCH] docs: fix footnote Signed-off-by: Jinzhe Zeng --- doc/train/multi-task-training-pt.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/train/multi-task-training-pt.md b/doc/train/multi-task-training-pt.md index 6defa802f3..e6fbe3cb10 100644 --- a/doc/train/multi-task-training-pt.md +++ b/doc/train/multi-task-training-pt.md @@ -24,7 +24,7 @@ and the Adam optimizer is executed to minimize $L^{(t)}$ for one step to update In the case of multi-GPU parallel training, different GPUs will independently select their tasks. In the DPA-2 model, this multi-task training framework is adopted.[^1] -[^1] Duo Zhang, Xinzijian Liu, Xiangyu Zhang, Chengqian Zhang, Chun Cai, Hangrui Bi, Yiming Du, Xuejian Qin, Jiameng Huang, Bowen Li, Yifan Shan, Jinzhe Zeng, Yuzhi Zhang, Siyuan Liu, Yifan Li, Junhan Chang, Xinyan Wang, Shuo Zhou, Jianchuan Liu, Xiaoshan Luo, Zhenyu Wang, Wanrun Jiang, Jing Wu, Yudi Yang, Jiyuan Yang, Manyi Yang, Fu-Qiang Gong, Linshuang Zhang, Mengchao Shi, Fu-Zhi Dai, Darrin M. York, Shi Liu, Tong Zhu, Zhicheng Zhong, Jian Lv, Jun Cheng, Weile Jia, Mohan Chen, Guolin Ke, Weinan E, Linfeng Zhang, Han Wang，[arXiv preprint arXiv:2312.15492 (2023)](https://arxiv.org/abs/2312.15492) licensed under a [Creative Commons Attribution (CC BY) license](http://creativecommons.org/licenses/by/4.0/). +[^1]: Duo Zhang, Xinzijian Liu, Xiangyu Zhang, Chengqian Zhang, Chun Cai, Hangrui Bi, Yiming Du, Xuejian Qin, Jiameng Huang, Bowen Li, Yifan Shan, Jinzhe Zeng, Yuzhi Zhang, Siyuan Liu, Yifan Li, Junhan Chang, Xinyan Wang, Shuo Zhou, Jianchuan Liu, Xiaoshan Luo, Zhenyu Wang, Wanrun Jiang, Jing Wu, Yudi Yang, Jiyuan Yang, Manyi Yang, Fu-Qiang Gong, Linshuang Zhang, Mengchao Shi, Fu-Zhi Dai, Darrin M. York, Shi Liu, Tong Zhu, Zhicheng Zhong, Jian Lv, Jun Cheng, Weile Jia, Mohan Chen, Guolin Ke, Weinan E, Linfeng Zhang, Han Wang, [arXiv preprint arXiv:2312.15492 (2023)](https://arxiv.org/abs/2312.15492) licensed under a [Creative Commons Attribution (CC BY) license](http://creativecommons.org/licenses/by/4.0/). Compared with the previous TensorFlow implementation, the new support in PyTorch is more flexible and efficient. In particular, it makes multi-GPU parallel training and even tasks beyond DFT possible,