google-research/tabfm

Health v2 · maintenance only

Flags: young

Adoption not part of the score

2551 stars · 264 forks observed · 2026-08-28

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

TabFM is a scikit-learn compatible tabular foundation model from Google Research that performs zero-shot classification and regression on tabular datasets with mixed column types. It uses in-context learning at inference time, requiring no parameter training on the user's dataset, with JAX and PyTorch backends.

Use cases

• zero-shot classification on tabular data
• zero-shot regression on tabular datasets
• predict customer churn without training a model
• replace XGBoost hyperparameter tuning with instant predictions
• classify mixed numerical and categorical features
• quickly benchmark tabular models without feature engineering

When to choose

• you need fast predictions on new tabular datasets without training or hyperparameter tuning
• your data mixes numerical and categorical columns
• you want a scikit-learn compatible drop-in estimator
• non-commercial use of pretrained weights is acceptable

When to avoid

• you need a commercially licensed model — the default pretrained weights are non-commercial only
• you have large datasets where gradient-boosted trees like XGBoost already perform well
• you need full control over model training and fine-tuning
• you require a lightweight dependency-free solution

Facets

library · maturity active

machine-learning llm-inference machine-learning data-science large-language-models python cross-platform tabular-data foundation-model zero-shot in-context-learning scikit-learn-compatible jax pytorch classification regression

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