TabTune is a newly introduced library aimed at simplifying the use of tabular foundation models, which are essential tools for learning from structured data. It tackles prevalent issues such as inconsistent preprocessing and the complexities involved in fine-tuning these models, thereby facilitating a smoother adoption process for developers. The library offers a unified framework that integrates both inference and fine-tuning capabilities, streamlining workflows that previously required disparate tools or custom solutions. By addressing these challenges, TabTune enhances the accessibility and efficiency of working with tabular data models, which are increasingly important in various machine learning applications. This development aligns with ongoing research efforts documented on arXiv, reflecting a broader interest in foundation models and their practical deployment. As the field advances, tools like TabTune are poised to play a critical role in standardizing and accelerating model development and application. Overall, TabTune represents a significant step toward more consistent and user-friendly tabular model management.