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Explore hands-on guides for integrating TabPFN into your stack. Each cookbook walks through a complete workflow from setup to deployment.
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Experiment with Thinking Mode

Thinking modeAPI

Improve results of target metrics with TabPFN Thinking Mode

Faster Inference with KV Cache

Performance

Get a quick introduction into faster inference with fit_with_cache

Get Started with Predictive Distribution

Regression

Learn how to extract predictive distributions with TabPFNRegressor

Interpreting Results with TabPFN

Interpretability

Learn how to use the standard interpretability toolkit with TabPFN for

TabPFN vs XGBoost

Benchmark

Compare TabPFN results with XGBoost using an OpenML Dataset

TabPFN with MLflow

Integration

Learn how to wrap TabPFN as an MLflow PythonModel, register it to Unity Catalog, and deploy it to a Mosaic AI Model serving endpoint.