Papers › Qwen Technical Report

Qwen Technical Report

28 Sep 2023arXiv:2309.16609archive 2025-07-28

Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, Tianhang Zhu

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 large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.

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QwenLM/Qwen-7B officialmentioned in papermentioned on GitHubpytorch report
qwenlm/qwen officialmentioned in papermentioned on GitHubpytorchApache-2.0 report

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get_stop_words_ids qwenlm/qwen/recipes/inference/vllm/vllm_wrapper.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 1616f54ad713d861 · report
make_context qwenlm/qwen/recipes/inference/vllm/vllm_wrapper.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 7c0630b819324850 · report
create QwenLM/Qwen-7B/dcu-support/package/fastllm_pytools/hf_model.py official repository unverified Apache-2.0 (permissive) · a5028c61c94869d8 · report
vLLMWrapper qwenlm/qwen/recipes/inference/vllm/vllm_wrapper.py official repository unverified Apache-2.0 (permissive) · 4e9fceca8918f8b6 · report

Tasks

Language ModelingLanguage ModellingLarge Language ModelMathMulti-Label Text Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Multi-Label Text Classification CC3M-TagMask Qwen-72B Accuracy 80.9 #3 of 6 Archive leaderboard report
Multi-Label Text Classification CC3M-TagMask Qwen-72B F1 62.1 #3 of 6 Archive leaderboard report
Multi-Label Text Classification CC3M-TagMask Qwen-72B Precision 69.3 #3 of 6 Archive leaderboard report
Multi-Label Text Classification CC3M-TagMask Qwen-72B Recall 56.2 #3 of 6 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

BASE

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