Zobrazeno 1 - 10
of 474
pro vyhledávání: '"Wang, Peiyi"'
Autor:
Gao, Bofei, Cai, Zefan, Xu, Runxin, Wang, Peiyi, Zheng, Ce, Lin, Runji, Lu, Keming, Lin, Junyang, Zhou, Chang, Xiao, Wen, Hu, Junjie, Liu, Tianyu, Chang, Baobao
Mathematical verfier achieves success in mathematical reasoning tasks by validating the correctness of solutions. However, existing verifiers are trained with binary classification labels, which are not informative enough for the model to accurately
Externí odkaz:
http://arxiv.org/abs/2406.14024
Autor:
DeepSeek-AI, Zhu, Qihao, Guo, Daya, Shao, Zhihong, Yang, Dejian, Wang, Peiyi, Xu, Runxin, Wu, Y., Li, Yukun, Gao, Huazuo, Ma, Shirong, Zeng, Wangding, Bi, Xiao, Gu, Zihui, Xu, Hanwei, Dai, Damai, Dong, Kai, Zhang, Liyue, Piao, Yishi, Gou, Zhibin, Xie, Zhenda, Hao, Zhewen, Wang, Bingxuan, Song, Junxiao, Chen, Deli, Xie, Xin, Guan, Kang, You, Yuxiang, Liu, Aixin, Du, Qiushi, Gao, Wenjun, Lu, Xuan, Chen, Qinyu, Wang, Yaohui, Deng, Chengqi, Li, Jiashi, Zhao, Chenggang, Ruan, Chong, Luo, Fuli, Liang, Wenfeng
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoin
Externí odkaz:
http://arxiv.org/abs/2406.11931
Autor:
DeepSeek-AI, Liu, Aixin, Feng, Bei, Wang, Bin, Wang, Bingxuan, Liu, Bo, Zhao, Chenggang, Dengr, Chengqi, Ruan, Chong, Dai, Damai, Guo, Daya, Yang, Dejian, Chen, Deli, Ji, Dongjie, Li, Erhang, Lin, Fangyun, Luo, Fuli, Hao, Guangbo, Chen, Guanting, Li, Guowei, Zhang, H., Xu, Hanwei, Yang, Hao, Zhang, Haowei, Ding, Honghui, Xin, Huajian, Gao, Huazuo, Li, Hui, Qu, Hui, Cai, J. L., Liang, Jian, Guo, Jianzhong, Ni, Jiaqi, Li, Jiashi, Chen, Jin, Yuan, Jingyang, Qiu, Junjie, Song, Junxiao, Dong, Kai, Gao, Kaige, Guan, Kang, Wang, Lean, Zhang, Lecong, Xu, Lei, Xia, Leyi, Zhao, Liang, Zhang, Liyue, Li, Meng, Wang, Miaojun, Zhang, Mingchuan, Zhang, Minghua, Tang, Minghui, Li, Mingming, Tian, Ning, Huang, Panpan, Wang, Peiyi, Zhang, Peng, Zhu, Qihao, Chen, Qinyu, Du, Qiushi, Chen, R. J., Jin, R. L., Ge, Ruiqi, Pan, Ruizhe, Xu, Runxin, Chen, Ruyi, Li, S. S., Lu, Shanghao, Zhou, Shangyan, Chen, Shanhuang, Wu, Shaoqing, Ye, Shengfeng, Ma, Shirong, Wang, Shiyu, Zhou, Shuang, Yu, Shuiping, Zhou, Shunfeng, Zheng, Size, Wang, T., Pei, Tian, Yuan, Tian, Sun, Tianyu, Xiao, W. L., Zeng, Wangding, An, Wei, Liu, Wen, Liang, Wenfeng, Gao, Wenjun, Zhang, Wentao, Li, X. Q., Jin, Xiangyue, Wang, Xianzu, Bi, Xiao, Liu, Xiaodong, Wang, Xiaohan, Shen, Xiaojin, Chen, Xiaokang, Chen, Xiaosha, Nie, Xiaotao, Sun, Xiaowen, Wang, Xiaoxiang, Liu, Xin, Xie, Xin, Yu, Xingkai, Song, Xinnan, Zhou, Xinyi, Yang, Xinyu, Lu, Xuan, Su, Xuecheng, Wu, Y., Li, Y. K., Wei, Y. X., Zhu, Y. X., Xu, Yanhong, Huang, Yanping, Li, Yao, Zhao, Yao, Sun, Yaofeng, Li, Yaohui, Wang, Yaohui, Zheng, Yi, Zhang, Yichao, Xiong, Yiliang, Zhao, Yilong, He, Ying, Tang, Ying, Piao, Yishi, Dong, Yixin, Tan, Yixuan, Liu, Yiyuan, Wang, Yongji, Guo, Yongqiang, Zhu, Yuchen, Wang, Yuduan, Zou, Yuheng, Zha, Yukun, Ma, Yunxian, Yan, Yuting, You, Yuxiang, Liu, Yuxuan, Ren, Z. Z., Ren, Zehui, Sha, Zhangli, Fu, Zhe, Huang, Zhen, Zhang, Zhen, Xie, Zhenda, Hao, Zhewen, Shao, Zhihong, Wen, Zhiniu, Xu, Zhipeng, Zhang, Zhongyu, Li, Zhuoshu, Wang, Zihan, Gu, Zihui, Li, Zilin, Xie, Ziwei
We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128
Externí odkaz:
http://arxiv.org/abs/2405.04434
Entity abstract summarization aims to generate a coherent description of a given entity based on a set of relevant Internet documents. Pretrained language models (PLMs) have achieved significant success in this task, but they may suffer from hallucin
Externí odkaz:
http://arxiv.org/abs/2402.18873
Large vision-language models (LVLMs) excel across diverse tasks involving concrete images from natural scenes. However, their ability to interpret abstract figures, such as geometry shapes and scientific plots, remains limited due to a scarcity of tr
Externí odkaz:
http://arxiv.org/abs/2403.00231
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex taxonomic structure. Nearly all recent HTC works focus on how the labels are structured but ignore the sub-structure of ground-truth labe
Externí odkaz:
http://arxiv.org/abs/2402.18825
Autor:
Meng, Xiangdi, Dai, Damai, Luo, Weiyao, Yang, Zhe, Wu, Shaoxiang, Wang, Xiaochen, Wang, Peiyi, Dong, Qingxiu, Chen, Liang, Sui, Zhifang
Supervised fine-tuning is the most common method to adapt large language models (LLMs) to downstream tasks, but full fine-tuning LLMs requires massive computational resources. Recently, parameter-efficient fine-tuning (PEFT) methods have been widely
Externí odkaz:
http://arxiv.org/abs/2402.16141
Autor:
Chen, Liang, Zhang, Yichi, Ren, Shuhuai, Zhao, Haozhe, Cai, Zefan, Wang, Yuchi, Wang, Peiyi, Meng, Xiangdi, Liu, Tianyu, Chang, Baobao
We present PCA-Bench, a multimodal decision-making benchmark for evaluating the integrated capabilities of Multimodal Large Language Models (MLLMs). Departing from previous benchmarks focusing on simplistic tasks and individual model capability, PCA-
Externí odkaz:
http://arxiv.org/abs/2402.15527
Large Language Models (LLMs) rely on Human Preference Alignment (HPA) to ensure the generation of safe content. Due to the heavy cost associated with fine-tuning, fine-tuning-free methods have emerged, typically modifying LLM decoding with external a
Externí odkaz:
http://arxiv.org/abs/2402.09320
Autor:
Shao, Zhihong, Wang, Peiyi, Zhu, Qihao, Xu, Runxin, Song, Junxiao, Bi, Xiao, Zhang, Haowei, Zhang, Mingchuan, Li, Y. K., Wu, Y., Guo, Daya
Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which continues pre-training DeepSeek-Coder-Base-v1.5 7B with 120B math-related tokens sou
Externí odkaz:
http://arxiv.org/abs/2402.03300