统一表格模型推理与微调流程，解决接口碎片化问题。
TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
- 提供单一接口整合七种顶尖表格模型的多种适配策略。
- 自动化处理模型特异性预处理，支持性能、校准、公平性评估。
- 适合需要可复现对比不同微调方法的研究者与工程师。
表格基础模型是结构化数据学习中日益重要的范式，将大规模预训练的优势延伸至表格领域。然而，其应用受限于各异的预处理流程、分散的API、不一致的微调方式，以及缺乏针对校准和公平性等部署指标的标准化评估。我们提出TabTune，一个统一库，通过单一接口标准化表格基础模型的完整工作流。TabTune提供对七种前沿模型的一致访问，支持零样本推理、元学习、监督微调（SFT）和参数高效微调（PEFT）等多种适配策略。框架自动管理模型感知的预处理，内部处理架构异构性，并集成性能、校准与公平性评估模块。设计注重可扩展性与可复现性，支持对表格基础模型适配策略的一致基准测试。
原文摘要 · Abstract (English)
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.
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