title: Qwen Technical Report
summary: 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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paper_title: Qwen Technical Report
paper_summary: 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.
paper_ai_summary: Qwen, a series of large language models, including chat and specialized coding and mathematics variants, exhibit superior performance across various tasks and outperform open-source models.
paper_ai_keywords: LLMs large language models pretrained language models Reinforcement Learning from Human Feedback RLHF chat models tool-use planning capabilities agent applications coding-specialized models mathematics-focused models