Artificial intelligence (AI) has played an increasingly important role in chemical research. However, most models currently used in chemistry are specialist models that require training and tuning for specific tasks. A more generic and efficient solution would be an AI model that could address many tasks and support free-form dialogue in the broad field of chemistry. In its utmost form, such a generalist AI chemist could be referred to as Chemical General Intelligence. Large language models (LLMs) have recently logged tremendous success in the general domain of natural language processing, showing emerging task generalization and free-form dialogue capabilities. However, domain knowledge of chemistry is largely missing when training general-domain LLMs. The lack of such knowledge greatly hinders the performance of generalist LLMs in the field of chemistry. To this end, we develop ChemDFM, a pioneering LLM for chemistry trained on 34B tokens from chemical literature and textbooks, and fine-tuned using 2.7M instructions. As a result, it can understand and reason with chemical knowledge in free-form dialogue. Quantitative evaluations show that ChemDFM significantly surpasses most representative open-source LLMs. It outperforms GPT-4 on a great portion of chemical tasks, despite the substantial size difference. We have open-sourced the inference codes, evaluation datasets, and model weights of ChemDFM on Huggingface (https://huggingface.co/OpenDFM/ChemDFM-v1.0-13B).

翻译：人工智能在化学研究中扮演着日益重要的角色。然而，目前化学领域使用的大多数模型是专用模型，需要针对特定任务进行训练和调优。一种更通用且高效的解决方案是构建一个能够在广泛的化学领域中处理多种任务并支持自由对话的AI模型。在理想形态下，这类通用型AI化学家可被称为化学通用智能。近年来，大语言模型在自然语言处理的通用领域取得了巨大成功，展现出新兴的任务泛化能力和自由对话能力。然而，通用领域大语言模型的训练过程中严重缺乏化学领域的专业知识。这种知识的缺失极大地限制了通用大语言模型在化学领域的性能表现。为此，我们开发了ChemDFM，这是一个开创性的化学领域大语言模型，其训练数据包含来自化学文献和教科书的340亿标记，并基于270万条指令进行了微调。因此，该模型能够在自由对话中理解和推理化学知识。定量评估表明，ChemDFM显著超越了大多数具有代表性的开源大语言模型。尽管模型规模存在显著差异，但它在大部分化学任务上的表现优于GPT-4。我们已在Huggingface平台开源了ChemDFM的推理代码、评估数据集和模型权重（https://huggingface.co/OpenDFM/ChemDFM-v1.0-13B）。