TabTune: A Unified Library for Inference and Fine-Tuning Tabular
  Foundation Models
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 .
690b34a160494e4fa76754fc
Aditya Tanna
690b34a160494e4fa76754fd
Pratinav Seth
claimed_verified
2025-11-05T16:24:05.630Z
66fce04d927ec45504514afd
https://cdn-avatars.huggingface.co/v1/production/uploads/66fce04d927ec45504514afd/lA_bTErY7JywT6xbzdflo.jpeg
Pratinav Seth
pratinavsetharya
user
pratinavsetharya
690b34a160494e4fa76754fe
Mohamed Bouadi
690b34a160494e4fa76754ff
Utsav Avaiya
690b34a160494e4fa7675500
Vinay Kumar Sankarapu
TabTune: A Unified Library for Inference and Fine-Tuning Tabular
  Foundation Models
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 .
TabTune is a unified library that standardizes the workflow for tabular foundation models, supporting various adaptation strategies and evaluation metrics.
tabular foundation models zero-shot inference meta-learning supervised fine-tuning parameter-efficient fine-tuning model-aware preprocessing calibration fairness