TabPFN
Prior LabsTabular foundation model from Prior Labs that predicts on a new classification or regression table in a single forward pass, with no training or hyperparameter search: the fitted dataset is read in context. It is pretrained only on synthetic tables drawn from a prior. The tabpfn package runs the checkpoints locally and adds fine-tuning, and a hosted API and enterprise edition are sold separately.
Openness
2 medium confidence- weights
- open(TabPFN-2 to 3.5 checkpoints on the Hub, ungated
- data
- closed(synthetic prior
- code
- partial(inference and fine-tuning code
- license
- Apache-2.0(code)+TABPFN-3-License-v1.0(non-commercial weights license
The package code is Apache-2.0, but every current checkpoint - TabPFN-2.5, 2.6, 3 and the default 3.5 - is under a Prior Labs license that allows testing, evaluation and research only, and extends that limit to the model's outputs. Production use needs a commercial license. Only the older TabPFN-2 weights are Apache-2.0 with an attribution clause. TABPFN-3-License-v1.0 does not permit commercial use, so the license tier is commercial_forbidden.
- https://cdn.jsdelivr.net/gh/PriorLabs/TabPFN@main/LICENSE recorded 2026-09-26
LICENSE is the Apache License, Version 2.0, with the appendix template and no filled-in copyright line; NOTICE names Prior Labs GmbH.
- https://cdn.jsdelivr.net/gh/PriorLabs/TabPFN@main/README.md recorded 2026-09-26
README: "The TabPFN-2.5, TabPFN-2.6, TabPFN-3 and TabPFN-3.5 model weights are released under non-commercial licenses" and "TabPFN-3.5 is used by default"; the repository is the "Core implementation for fast and local inference".
- https://docs.priorlabs.ai/cookbook/pretrain_nanotabpfn.md recorded 2026-09-26
The only pretraining walkthrough Prior Labs documents is a teaching model: "This is a teaching model, not the production TabPFN."
- https://huggingface.co/api/models?author=Prior-Labs&sort=downloads&limit=50 recorded 2026-09-26
Prior-Labs Hub listing: TabPFN-v2-clf, TabPFN-v2-reg, tabpfn_2_5, tabpfn_2_6, tabpfn_3 and tabpfn_3_5, each tagged license:other.
- https://huggingface.co/Prior-Labs/tabpfn_3_5/raw/main/LICENSE recorded 2026-09-26
TABPFN-3.5 License v1.0 (revised September 9, 2026) uses the same "solely for your Non-Commercial Purposes" grant.
- https://huggingface.co/Prior-Labs/tabpfn_3/raw/main/LICENSE recorded 2026-09-26
TABPFN-3 License v1.0 grants a license to use the model "solely for your Non-Commercial Purposes", defined as "use for testing, evaluation, or research not tied to commercial gain, production deployment, or revenue generation".
- https://huggingface.co/Prior-Labs/tabpfn_3/raw/main/README.md recorded 2026-09-26
TabPFN-3 card: "TabPFN-3 is trained purely on synthetic tabular tasks." and "Inference code can be found at https://github.com/PriorLabs/TabPFN".
Adoption
3 medium confidenceMeasured on monthly PyPI downloads of tabpfn, which is how the weights are fetched and run; the Hub counts on the checkpoint repositories are much smaller.
- https://pypistats.org/api/packages/tabpfn/recent recorded 2026-09-26
last_month 150,735 downloads of tabpfn
Capability
4 medium confidenceTabPFN does the work of model selection and tuning for you on each new table, and on the TabArena benchmark it outperforms tuned and ensembled gradient-boosting baselines. It stops at one model: it does not search over or ensemble other learners, prepare features, or cover time series and multimodal data the way AutoGluon does.
- https://arxiv.org/abs/2605.13986 recorded 2026-09-26
TabPFN-3 abstract: "On the standard tabular benchmark TabArena, a forward pass of TabPFN-3 outperforms all other models, including tuned and ensembled baselines, by a significant margin".
- https://arxiv.org/abs/2609.17895 recorded 2026-09-26
TabPFN-3.5 abstract: "TabPFN-3.5 sets a new state of the art on standard tabular prediction in TabArena".
Verified 2026-09-26