title: Lemur: Harmonizing Natural Language and Code for Language Agents
summary: 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
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paper_title: Lemur: Harmonizing Natural Language and Code for Language Agents
paper_summary: 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
paper_ai_keywords: language models functional language agents language chat models harmonious blend pre-training code-intensive corpus instruction fine-tuning state-of-the-art agent tasks human communication tool usage fully-observable environments partially-observable environments harmonization proprietary models advanced open-source agents reasoning planning operating seamlessly