狐猴：為語言代理人協調自然語言和程式碼

摘要

我們介紹 Lemur 和 Lemur-Chat，這兩個開放存取的語言模型被優化，旨在兼顧自然語言和編碼能力，以擔任多功能語言代理的核心。從語言聊天模型演變為功能性語言代理需要模型不僅精通人類互動、推理和規劃，還要確保在相關環境中具有基礎。這要求模型在語言和編碼能力之間取得和諧的平衡。Lemur 和 Lemur-Chat 被提議來應對這種必要性，展示了在兩個領域中均衡的專業能力，與現有傾向於專精於其中一個領域的開源模型不同。通過精心的預訓練，使用代碼密集的語料庫，以及在文本和代碼數據上進行指導微調，我們的模型在各種文本和編碼基準測試中實現了最先進的平均表現，超越了現有的開源模型。全面的實驗證明了 Lemur 在各種涉及人類溝通、工具使用和在完全可觀察和部分可觀察環境下進行互動的代理任務中的優越性。自然語言和編程語言之間的協調使 Lemur-Chat 能夠大幅縮小與專有模型在代理能力上的差距，為開發具有推理、規劃和在各種環境中無縫運作能力的先進開源代理提供了關鍵見解。https://github.com/OpenLemur/Lemur

English

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

狐猴：為語言代理人協調自然語言和程式碼

Lemur: Harmonizing Natural Language and Code for Language Agents

摘要

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