TabPFN is a tabular foundation model designed to provide rapid predictions on structured data without requiring dataset-specific training. It uses a pre-trained transformer architecture to perform in-context learning, enabling it to handle various tabular data formats such as CSV files, dataframes, and database tables. The model automatically manages missing values, mixed data types, and categorical features.