Large language models (LLMs) have made impressive progress in chemistry applications. However, the community lacks an LLM specifically designed for chemistry. The main challenges are two-fold: firstly, most chemical data and scientific knowledge are stored in structured databases, which limits the model's ability to sustain coherent dialogue when used directly. Secondly, there is an absence of objective and fair benchmark that encompass most chemistry tasks. Here, we introduce ChemLLM, a comprehensive framework that features the first LLM dedicated to chemistry. It also includes ChemData, a dataset specifically designed for instruction tuning, and ChemBench, a robust benchmark covering nine essential chemistry tasks. ChemLLM is adept at performing various tasks across chemical disciplines with fluid dialogue interaction. Notably, ChemLLM achieves results comparable to GPT-4 on the core chemical tasks and demonstrates competitive performance with LLMs of similar size in general scenarios. ChemLLM paves a new path for exploration in chemical studies, and our method of incorporating structured chemical knowledge into dialogue systems sets a new standard for developing LLMs in various scientific fields. Codes, Datasets, and Model weights are publicly accessible at https://hf.co/AI4Chem

翻译：大型语言模型（LLMs）在化学应用中取得了令人瞩目的进展。然而，该领域尚缺乏专门为化学设计的LLM。主要挑战体现在两个方面：首先，大多数化学数据和科学知识存储在结构化数据库中，这限制了模型在直接使用时维持连贯对话的能力；其次，缺乏涵盖大多数化学任务的客观公正基准。在此，我们提出ChemLLM，一个包含首个专用于化学的LLM的综合框架。该框架还包括ChemData（一个专为指令微调设计的数据集）和ChemBench（一个覆盖九项基础化学任务的稳健基准）。ChemLLM擅长以流畅的对话交互方式执行化学学科中的各项任务。值得注意的是，ChemLLM在核心化学任务上取得了与GPT-4相当的结果，并在通用场景中展现出与同等规模LLM相竞争的性能。ChemLLM为化学研究探索开辟了新路径，我们将结构化化学知识融入对话系统的方法，为开发各科学领域的LLM树立了新标准。代码、数据集和模型权重已公开于https://hf.co/AI4Chem。