We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents. The evolution from language chat models to functional language agents demands that models not only master human interaction, reasoning, and planning but also ensure grounding in the relevant environments. This calls for a harmonious blend of language and coding capabilities in the models. Lemur and Lemur-Chat are proposed to address this necessity, demonstrating balanced proficiencies in both domains, unlike existing open-source models that tend to specialize in either. Through meticulous pre-training using a code-intensive corpus and instruction fine-tuning on text and code data, our models achieve state-of-the-art averaged performance across diverse text and coding benchmarks among open-source models. Comprehensive experiments demonstrate Lemur's superiority over existing open-source models and its proficiency across various agent tasks involving human communication, tool usage, and interaction under fully- and partially- observable environments. The harmonization between natural and programming languages enables Lemur-Chat to significantly narrow the gap with proprietary models on agent abilities, providing key insights into developing advanced open-source agents adept at reasoning, planning, and operating seamlessly across environments. https://github.com/OpenLemur/Lemur

翻译：我们推出Lemur与Lemur-Chat，这是两款开源且专为兼具自然语言与代码能力而优化的语言模型，旨在作为多功能语言智能体的核心基础。从语言对话模型演进为功能性语言智能体，要求模型不仅需精通人际交互、推理与规划，还必须确保在相关环境中的基础适应性。这需要模型实现语言与编码能力的和谐融合。为应对这一需求，我们提出了Lemur与Lemur-Chat，其在两个领域均展现出均衡的熟练度，这与当前倾向于专精某一领域的现有开源模型形成鲜明对比。通过使用代码密集型语料库进行细致预训练，并结合文本与代码数据的指令微调，我们的模型在开源模型中，于多样化的文本与代码基准测试上取得了领先的平均性能。综合实验表明，Lemur优于现有开源模型，并在涉及人类交流、工具使用以及在全观测与部分可观测环境下交互的多种智能体任务中表现出色。自然语言与编程语言的协调使得Lemur-Chat在智能体能力上显著缩小了与专有模型的差距，为开发能够在不同环境中无缝进行推理、规划与操作的高级开源智能体提供了关键见解。https://github.com/OpenLemur/Lemur