tabpfn
TabPFN: Foundation model for tabular data
Decision gist · record as of 2026-08-14
Yes, with conditions. TabPFN is worth installing if you work with tabular data and have a GPU available, or can tolerate CPU-only inference on moderate datasets. The package is actively maintained with low install friction and genuine value for rapid prototyping. Verify the non-commercial license restrictions for TabPFN-3 (the default) if your use is commercial; if so, you'll need a commercial license or use TabPFN-2 weights (Apache 2.0 with attribution). The attribution requirement is substantial—ensure compliance before distributing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10+.
- GPU recommended for performance; CPU-only inference limited to moderate datasets.
- Installation is straightforward with low friction—the package ships as a wheel and handles PyTorch setup automatically.
License · maintenance · safety
(unclear) — The code and TabPFN-2 weights use Prior Labs License (Apache 2.0 with an added attribution requirement). TabPFN-3, the default model, uses a non-commercial license. Section 10 requires prominent attribution ("Built with PriorLabs-TabPFN") on websites, UIs, and documentation if you distribute or make the work available. Internal testing without external communication is exempt.
last release 2026-08-13 (1 days) · last repo commit 2026-08-14 · 7,795 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 302,996 downloads/mo, #7,815 on PyPI
Alternatives
Verify before relying
pip install tabpfn
from tabpfn import TabPFNClassifier
clf = TabPFNClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)- Whether the non-commercial license for TabPFN-3 model weights permits use in commercial products.
- Whether the attribution requirement in Section 10 applies to internal enterprise deployments.
- Performance characteristics and accuracy benchmarks on specific dataset types or sizes.
What it is and what it does
TabPFN is a pre-trained foundation model designed to make tabular machine learning faster by eliminating hyperparameter tuning. It provides TabPFNClassifier and TabPFNRegressor classes that follow scikit-learn conventions, so you can fit and predict on structured data with minimal setup. The model downloads weights from huggingface-hub on first use and runs inference via torch, supporting both GPU and CPU execution (though GPU is strongly recommended).
The package depends on torch, scikit-learn, pandas, numpy, scipy, einops, pydantic, joblib, tqdm, and lightgbm. It supports Python 3.10 through 3.14. By default it uses TabPFN-3 (non-commercial license), but you can instantiate earlier versions like TabPFN-2.6 or TabPFN-2 via ModelVersion constants. For production use, Prior Labs offers a commercial Enterprise Edition.
Use it for
- Rapid prototyping on tabular datasets when you need a strong baseline without manual hyperparameter search.
- Binary and multiclass classification on structured data with minimal preprocessing or model selection overhead.
- Regression tasks on tabular data where you want pre-trained representations without training from scratch.
- Exploratory analysis where you want to quickly assess model performance before investing in tuning.
- Scenarios where you have a GPU available and want to leverage foundation model capabilities on tables.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
TabPFN is worth installing if you work with tabular data and have a GPU available, or can tolerate CPU-only inference on moderate datasets. The package is actively maintained with low install friction and genuine value for rapid prototyping. Verify the non-commercial license restrictions for TabPFN-3 (the default) if your use is commercial; if so, you'll need a commercial license or use TabPFN-2 weights (Apache 2.0 with attribution). The attribution requirement is substantial—ensure compliance before distributing.
Install
tabpfn on PyPI
Before you install
Installation is straightforward with low friction—the package ships as a wheel and handles PyTorch setup automatically. The project is actively maintained with a recent release (1 day old) and strong community engagement (7795 stars). However, optimal use requires a GPU; CPU-only inference is limited to moderate datasets.
Requires Python 3.10+. GPU recommended for performance; CPU-only inference limited to moderate datasets.
License in practice
The code and TabPFN-2 weights use Prior Labs License (Apache 2.0 with an added attribution requirement). TabPFN-3, the default model, uses a non-commercial license. Section 10 requires prominent attribution ("Built with PriorLabs-TabPFN") on websites, UIs, and documentation if you distribute or make the work available. Internal testing without external communication is exempt.
Quickstart
pip install tabpfn
from tabpfn import TabPFNClassifier
clf = TabPFNClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)Verify before relying
- Whether the non-commercial license for TabPFN-3 model weights permits use in commercial products.
- Whether the attribution requirement in Section 10 applies to internal enterprise deployments.
- Performance characteristics and accuracy benchmarks on specific dataset types or sizes.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 16 packagestorchsafetensorsnumpyscikit-learntyping_extensionsscipypandaseinopshuggingface-hubpydanticpydantic-settingsjoblibtqdmfilelocklightgbmmlx |
| Maintenance | Actively maintained 1 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 302,996 / month, #7,815 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: tabpfn-8.3.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “foundation model for tables”
- tabpfnTabPFN is a foundation model for tabular data that provides…
- tabiclTabICL is a scikit-learn-compatible tabular foundation model that…
- timesfmTimesFM is a pretrained time-series foundation model from Google…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also tabicl · tabpfn-common-utils · autogluon.tabular · autogluon.core · autogluon.features · autogluon · autogluon.multimodal · autogluon.timeseries · pytabkit · timesfm