Summary of Qwen2 Technical Report, by An Yang et al.

Qwen2 Technical Report

by An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Ma, Jianxin Yang, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Junyang Lin, Kai Dang, Keming Lu, Keqin Chen, Kexin Yang, Mei Li, Mingfeng Xue, Na Ni, Pei Zhang, Peng Wang, Ru Peng, Rui Men, Ruize Gao, Runji Lin, Shijie Wang, Shuai Bai, Sinan Tan, Tianhang Zhu, Tianhao Li, Tianyu Liu, Wenbin Ge, Xiaodong Deng, Xiaohuan Zhou, Xingzhang Ren, Xinyu Zhang, Xipin Wei, Xuancheng Ren, Xuejing Liu, Yang Fan, Yang Yao, Yichang Zhang, Yu Wan, Yunfei Chu, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, Zhifang Guo, Zhihao Fan

First submitted to arxiv on: 15 Jul 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This report introduces the Qwen2 series, a suite of large language and multimodal models that surpass previous open-weight models like Qwen1.5. The series includes foundational and instruction-tuned language models with parameters ranging from 0.5 to 72 billion, featuring dense and Mixture-of-Experts models. Qwen2 exhibits competitive performance on diverse benchmarks in areas such as language understanding, generation, multilingual proficiency, coding, mathematics, and reasoning.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The Qwen2 series is a set of advanced language and multimodal models that work together to help computers understand and generate human-like text. These models are bigger and more powerful than previous ones, which helps them learn new things and do tasks better. The Qwen2 models can be used for many different applications, like understanding and generating text in multiple languages, doing math problems, and even coding.