# TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models

arXiv: https://arxiv.org/abs/2511.02802 · HTML: https://mlsanity.com/paper/2511.02802/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 20 of 20)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [TabICL: A Tabular Foundation Model for In-Context Learning on Large Data](https://mlsanity.com/paper/2502.05564/) (2025)
- [When Do Neural Nets Outperform Boosted Trees on Tabular Data?](https://mlsanity.com/paper/2305.02997/) (2023)
- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
- [Why Tabular Foundation Models Should Be a Research Priority](https://mlsanity.com/paper/2405.01147/) (2024)
- [From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting](https://mlsanity.com/paper/2501.02945/) (2025)
- [A Closer Look at Deep Learning Methods on Tabular Datasets](https://mlsanity.com/paper/2407.00956/) (2024)
- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [ConTextTab: A Semantics-Aware Tabular In-Context Learner](https://mlsanity.com/paper/2506.10707/) (2025)
- [PyTorch Tabular: A Framework for Deep Learning with Tabular Data](https://mlsanity.com/paper/2104.13638/) (2021)
- [A Survey on Deep Tabular Learning](https://mlsanity.com/paper/2410.12034/) (2024)
- [Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions](https://mlsanity.com/paper/2401.02143/) (2024)
- [On Finetuning Tabular Foundation Models](https://mlsanity.com/paper/2506.08982/) (2025)

## Built on by (most cited first, 11 of 11)

- [Orion-Bix: Bi-Axial Attention for Tabular In-Context Learning](https://mlsanity.com/paper/2512.00181/) (2025)
- [Exploring Fine-Tuning for Tabular Foundation Models](https://mlsanity.com/paper/2601.09654/) (2026)
- [Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality](https://mlsanity.com/paper/2605.18971/) (2026)
- [Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules](https://mlsanity.com/paper/2603.08206/) (2026)
- [Pocket Foundation Models: Distilling TFMs into CPU-Ready Gradient-Boosted Trees](https://mlsanity.com/paper/2605.18654/) (2026)
- [Distilling Tabular Foundation Models for Structured Health Data](https://mlsanity.com/paper/2605.18702/) (2026)
- [Efficient Adaptive Data Acquisition via Pretrained Belief Representations](https://mlsanity.com/paper/2606.25197/) (2026)
- [TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models](https://mlsanity.com/paper/2605.06047/) (2026)
- [Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models](https://mlsanity.com/paper/2605.18635/) (2026)
- [Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap](https://mlsanity.com/paper/2605.18696/) (2026)
- [OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning](https://mlsanity.com/paper/2610.20418/) (2026)

## Abstract (arXiv)

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.