SMolInstruct is a large-scale, comprehensive and high-quality chemical instruction fine-tuning dataset proposed by Ohio State University. The dataset contains 14 different chemical tasks, a total of more than 3 million samples, and covers 1.6 million unique molecules. Researchers collected data related to chemical tasks from multiple sources, covering chemical knowledge representations such as IUPAC names, SMILES representations, molecular formulas, as well as tasks such as molecular property prediction, chemical reaction prediction, and molecular description.