TabTune: A Unified Library for Inference and Fine-Tuning Tabular
  Foundation Models
Authors: name: Aditya Tanna, name: Pratinav Seth, name: Mohamed Bouadi, name: Utsav Avaiya, name: Vinay Kumar Sankarapu
Organization: Lexsi Labs

Tabular foundation models represent a growing paradigm in structured data
learning, extending the benefits of large-scale pretraining to tabular domains.
However, their adoption remains limited due to heterogeneous preprocessing
pipelines, fragmented APIs, inconsistent fine-tuning procedures, and the
absence of standardized evaluation for deployment-oriented metrics such as
calibration and fairness. We present TabTune, a unified library that
standardizes the complete workflow for tabular foundation models through a
single interface. TabTune provides consistent access to seven state-of-the-art
models supporting multiple adaptation strategies, including zero-shot
inference, meta-learning, supervised fine-tuning (SFT), and parameter-efficient
fine-tuning (PEFT). The framework automates model-aware preprocessing, manages
architectural heterogeneity internally, and integrates evaluation modules for
performance, calibration, and fairness. Designed for extensibility and
reproducibility, TabTune enables consistent benchmarking of adaptation
strategies of tabular foundation models. The library is open source and
available at https://github.com/Lexsi-Labs/TabTune .