soda-inria/tabicl

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1309 stars · 175 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TabICLv2 is an open-source tabular foundation model that performs classification and regression via a single forward pass through a pre-trained transformer, using in-context learning instead of training. It is pip-installable, scikit-learn compliant, and achieves state-of-the-art benchmark results without hyperparameter tuning.

Use cases

• classify tabular data without training a model
• predict on tabular datasets without hyperparameter tuning
• replace XGBoost or CatBoost with a faster tabular model
• run zero-shot regression on structured data
• fit and predict tabular data on a GPU in seconds
• scale tabular inference to hundreds of thousands of samples

When to choose

• you need state-of-the-art tabular classification or regression with no tuning
• you want fast inference on medium-sized tabular datasets with a GPU
• you prefer a scikit-learn-compatible API
• you want an open-source, permissively licensed tabular foundation model

When to avoid

• you have no GPU and very large datasets
• you need strict interpretability out of the box
• you work with non-tabular data like images or text
• you require guaranteed accuracy on datasets far beyond 500K samples

Facets

library · maturity active

machine-learning deep-learning transformers machine-learning data-science large-language-models python cross-platform tabular-data foundation-model in-context-learning classification regression scikit-learn tabular-deep-learning gpu

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