Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Ren, Xuancheng"'
Autor:
Gao, Bofei, Song, Feifan, Yang, Zhe, Cai, Zefan, Miao, Yibo, Dong, Qingxiu, Li, Lei, Ma, Chenghao, Chen, Liang, Xu, Runxin, Tang, Zhengyang, Wang, Benyou, Zan, Daoguang, Quan, Shanghaoran, Zhang, Ge, Sha, Lei, Zhang, Yichang, Ren, Xuancheng, Liu, Tianyu, Chang, Baobao
Recent advancements in large language models (LLMs) have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benchmarks like GSM8K or MATH are now being solved with high accuracy (e.g., OpenAI o1 achieves 94.8%
Externí odkaz:
http://arxiv.org/abs/2410.07985
Autor:
Wang, Peng, Bai, Shuai, Tan, Sinan, Wang, Shijie, Fan, Zhihao, Bai, Jinze, Chen, Keqin, Liu, Xuejing, Wang, Jialin, Ge, Wenbin, Fan, Yang, Dang, Kai, Du, Mengfei, Ren, Xuancheng, Men, Rui, Liu, Dayiheng, Zhou, Chang, Zhou, Jingren, Lin, Junyang
We present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing. Qwen2-VL introduces the Naive Dynamic Resolution mechanism, which enables the m
Externí odkaz:
http://arxiv.org/abs/2409.12191
Autor:
Hui, Binyuan, Yang, Jian, Cui, Zeyu, Yang, Jiaxi, Liu, Dayiheng, Zhang, Lei, Liu, Tianyu, Zhang, Jiajun, Yu, Bowen, Lu, Keming, Dang, Kai, Fan, Yang, Zhang, Yichang, Yang, An, Men, Rui, Huang, Fei, Zheng, Bo, Miao, Yibo, Quan, Shanghaoran, Feng, Yunlong, Ren, Xingzhang, Ren, Xuancheng, Zhou, Jingren, Lin, Junyang
In this report, we introduce the Qwen2.5-Coder series, a significant upgrade from its predecessor, CodeQwen1.5. This series includes six models: Qwen2.5-Coder-(0.5B/1.5B/3B/7B/14B/32B). As a code-specific model, Qwen2.5-Coder is built upon the Qwen2.
Externí odkaz:
http://arxiv.org/abs/2409.12186
Autor:
Yang, An, Yang, Baosong, Hui, Binyuan, Zheng, Bo, Yu, Bowen, Zhou, Chang, Li, Chengpeng, Li, Chengyuan, Liu, Dayiheng, Huang, Fei, Dong, Guanting, Wei, Haoran, Lin, Huan, Tang, Jialong, Wang, Jialin, Yang, Jian, Tu, Jianhong, Zhang, Jianwei, Ma, Jianxin, Yang, Jianxin, Xu, Jin, Zhou, Jingren, Bai, Jinze, He, Jinzheng, Lin, Junyang, Dang, Kai, Lu, Keming, Chen, Keqin, Yang, Kexin, Li, Mei, Xue, Mingfeng, Ni, Na, Zhang, Pei, Wang, Peng, Peng, Ru, Men, Rui, Gao, Ruize, Lin, Runji, Wang, Shijie, Bai, Shuai, Tan, Sinan, Zhu, Tianhang, Li, Tianhao, Liu, Tianyu, Ge, Wenbin, Deng, Xiaodong, Zhou, Xiaohuan, Ren, Xingzhang, Zhang, Xinyu, Wei, Xipin, Ren, Xuancheng, Liu, Xuejing, Fan, Yang, Yao, Yang, Zhang, Yichang, Wan, Yu, Chu, Yunfei, Liu, Yuqiong, Cui, Zeyu, Zhang, Zhenru, Guo, Zhifang, Fan, Zhihao
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models. We release a comprehensive suite of foundational and instruction-tuned language models, encompassing a parameter range from 0.5 to
Externí odkaz:
http://arxiv.org/abs/2407.10671
Autor:
Bai, Jinze, Bai, Shuai, Chu, Yunfei, Cui, Zeyu, Dang, Kai, Deng, Xiaodong, Fan, Yang, Ge, Wenbin, Han, Yu, Huang, Fei, Hui, Binyuan, Ji, Luo, Li, Mei, Lin, Junyang, Lin, Runji, Liu, Dayiheng, Liu, Gao, Lu, Chengqiang, Lu, Keming, Ma, Jianxin, Men, Rui, Ren, Xingzhang, Ren, Xuancheng, Tan, Chuanqi, Tan, Sinan, Tu, Jianhong, Wang, Peng, Wang, Shijie, Wang, Wei, Wu, Shengguang, Xu, Benfeng, Xu, Jin, Yang, An, Yang, Hao, Yang, Jian, Yang, Shusheng, Yao, Yang, Yu, Bowen, Yuan, Hongyi, Yuan, Zheng, Zhang, Jianwei, Zhang, Xingxuan, Zhang, Yichang, Zhang, Zhenru, Zhou, Chang, Zhou, Jingren, Zhou, Xiaohuan, Zhu, Tianhang
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our la
Externí odkaz:
http://arxiv.org/abs/2309.16609
This paper proposes a new method, OFA-OCR, to transfer multimodal pretrained models to text recognition. Specifically, we recast text recognition as image captioning and directly transfer a unified vision-language pretrained model to the end task. Wi
Externí odkaz:
http://arxiv.org/abs/2212.09297
Autor:
Bai, Jinze, Men, Rui, Yang, Hao, Ren, Xuancheng, Dang, Kai, Zhang, Yichang, Zhou, Xiaohuan, Wang, Peng, Tan, Sinan, Yang, An, Cui, Zeyu, Han, Yu, Bai, Shuai, Ge, Wenbin, Ma, Jianxin, Lin, Junyang, Zhou, Jingren, Zhou, Chang
Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alternative to approaching general-purpose AI, existing generalist models are
Externí odkaz:
http://arxiv.org/abs/2212.04408
Vision-and-language (V-L) tasks require the system to understand both vision content and natural language, thus learning fine-grained joint representations of vision and language (a.k.a. V-L representations) is of paramount importance. Recently, vari
Externí odkaz:
http://arxiv.org/abs/2210.16431
Recently, attention based models have been used extensively in many sequence-to-sequence learning systems. Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words. However,
Externí odkaz:
http://arxiv.org/abs/2210.10914
Investigating better ways to reuse the released pre-trained language models (PLMs) can significantly reduce the computational cost and the potential environmental side-effects. This paper explores a novel PLM reuse paradigm, Knowledge Integration (KI
Externí odkaz:
http://arxiv.org/abs/2210.05230