PriorLabs/TabPFN

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

7855 stars · 781 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

TabPFN is a pre-trained tabular foundation model (transformer) that performs classification, regression, forecasting, and anomaly detection on structured data via in-context learning in a single forward pass, with a scikit-learn-compatible Python package. It is developed by Prior Labs, published in Nature, and also available through a hosted REST API and client SDK.

Use cases

• predict on tabular datasets without training a model
• classify rows in a csv with calibrated probabilities
• regression on structured data with uncertainty estimates
• detect anomalies in tabular data
• forecast time series
• generate synthetic tabular data
• replace AutoML and tuned tree ensembles like XGBoost
• fit a model in seconds on small to medium datasets

When to choose

• you need strong tabular predictions fast without hyperparameter tuning or pipeline building
• your datasets fit within model limits (up to ~1M rows with scaling mode, GPU recommended)
• you want a scikit-learn-compatible estimator that works out of the box
• you want state-of-the-art accuracy on structured data benchmarks

When to avoid

• you need a permissively licensed library for commercial use without attribution provisions
• you have no GPU and very large datasets (CPU is limited to moderate sizes)
• you need full interpretability or custom model architectures
• you work primarily with unstructured data like text or images

Facets

library · maturity active

machine-learning data-science llm-inference machine-learning data-science artificial-intelligence python cross-platform tabular-data foundation-model in-context-learning classification regression anomaly-detection time-series-forecasting scikit-learn-compatible transformer prior-fitted-networks gpu

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