PriorLabs/TabPFN

Based on the README, here's a technical summary: Built on a pretrained transformer architecture trained exclusively on synthetic data, TabPFN performs in-context learning by processing entire training sets through the model at inference time rather than traditional parameter updates. It provides scikit-learn compatible classifiers and regressors optimized for GPU inference on tabular datasets under 100K samples and 2000 features, with the ecosystem offering SHAP-based interpretability, synthetic data generation, embedding extraction, and hyperparameter optimization extensions.

5,846 stars. Used by 5 other packages. Actively maintained with 27 commits in the last 30 days. Available on PyPI.