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.

翻译：大型语言模型（LLMs）已彻底改变了人工智能领域，使之前被认为只有人类才能胜任的自然语言处理任务成为可能。在这项工作中，我们介绍了Qwen，这是我们大型语言模型系列的第一个版本。Qwen是一个全面的语言模型系列，包含多个具有不同参数量的独立模型。它包括基础预训练语言模型Qwen，以及通过人类对齐技术微调的对话模型Qwen-Chat。基础语言模型在众多下游任务中一贯表现出卓越的性能，而对话模型，特别是那些使用基于人类反馈的强化学习（RLHF）训练的模型，极具竞争力。这些对话模型具备先进的工具使用和规划能力，可用于创建智能体应用，即使在利用代码解释器执行复杂任务时，也能展现出令人印象深刻的性能，甚至可与规模更大的模型相媲美。此外，我们还开发了基于基础语言模型的代码专用模型Code-Qwen和Code-Qwen-Chat，以及数学专用模型Math-Qwen-Chat。与开源模型相比，这些模型表现出显著提升的性能，并略微落后于专有模型。