Quickstart

tabpfn-client & tabpfn packages

API (TabPFN-3.5-Plus)
OSS (TabPFN-3.5)

Use tabpfn-client to access TabPFN-3.5-Plus via our API with managed compute. No local GPU is needed.

Install

Install the client and set your API key:

pip install --upgrade tabpfn-client
export TABPFN_TOKEN="<your-api-key>"

Get your API key on platform.priorlabs.ai.

Run predictions

Choose a task and run the example with a sample dataset.

Classification
Regression

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from tabpfn_client import TabPFNClassifier

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

model = TabPFNClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(predictions[:5])

from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from tabpfn_client import TabPFNRegressor

X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

model = TabPFNRegressor()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(predictions[:5])

Use tabpfn to run TabPFN-3.5 on your own hardware. A GPU is recommended for datasets past 1,000 rows.

Install

pip install --upgrade tabpfn

Before your first run, follow the model access setup to accept the license and download the weights.

Run predictions

Choose a task and run the example with a sample dataset.

Classification
Regression

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from tabpfn import TabPFNClassifier

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

model = TabPFNClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(predictions[:5])

from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from tabpfn import TabPFNRegressor

X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

model = TabPFNRegressor()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(predictions[:5])

REST API

For integrations in other languages, call the hosted API over HTTP. Follow the REST API quickstart to authenticate, upload data, fit a model, and get predictions using the /tabpfn/* endpoints.

Getting Started

Capabilities

Using the API

Agentic

Integrations

Troubleshooting

TabPFN in Action

Customer References

Changelog

tabpfn-client & tabpfn packages

REST API

Choose a model

Models

Selecting Model Version

​tabpfn-client & tabpfn packages

​REST API

​Choose a model

Models

Selecting Model Version

tabpfn-client & tabpfn packages

REST API

Choose a model