> ## Documentation Index
> Fetch the complete documentation index at: https://docs.priorlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> TabPFN-3 and later versions support up to 1,000,000 rows, subject to feature count and checkpoint/API limits.
> For large datasets, use per-estimator subsampling, e.g. inference_config={"SUBSAMPLE_SAMPLES": 100_000}, in TabPFNClassifier or TabPFNRegressor from tabpfn or tabpfn_client.
> Pass raw pandas DataFrames to fit and predict. Categorical strings/categories, raw text, and missing feature values are handled automatically; no manual integer/one-hot encoding, imputation, scaling, or outlier removal is needed.

# Fast model (α)

> Use TabPFN-3.5-Fast (alpha) for faster classification and regression.

TabPFN-3.5-Fast (alpha) is a pretrained checkpoint designed for faster inference. It uses the familiar `fit` and `predict` workflow for classification and regression.

Fast is available through the local `tabpfn` package, the hosted API and `tabpfn-client`, and VPC deployments. See [Models](/models#tabpfn-3-5-family) for limits and availability across the TabPFN-3.5 family.

## Use the fast checkpoint

These examples assume you have prepared `X_train`, `y_train`, and `X_test` for your task.

<Tabs>
  <Tab title="API (tabpfn-client)">
    Use `tabpfn-client` to run TabPFN-3.5-Fast on the hosted API. Follow the [quickstart](/quickstart) for installation and API key setup.

    Select `model_path="v3.5-fast_default"` when creating the estimator.

    <CodeGroup>
      ```python Classification theme={null}
      from tabpfn_client import TabPFNClassifier

      model = TabPFNClassifier(model_path="v3.5-fast_default")
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      ```

      ```python Regression theme={null}
      from tabpfn_client import TabPFNRegressor

      model = TabPFNRegressor(model_path="v3.5-fast_default")
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      ```
    </CodeGroup>
  </Tab>

  <Tab title="OSS (tabpfn)">
    Follow the [quickstart](/quickstart) to install a `tabpfn` release with TabPFN-3.5-Fast support. The package downloads and caches the weights automatically; see [Accessing model weights](/models/accessing-model-weights) for license acceptance and authentication.

    Select `ModelVersion.V3_5_FAST` when creating the estimator.

    <CodeGroup>
      ```python Classification theme={null}
      from tabpfn import TabPFNClassifier
      from tabpfn.constants import ModelVersion

      model = TabPFNClassifier.create_default_for_version(ModelVersion.V3_5_FAST)
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      ```

      ```python Regression theme={null}
      from tabpfn import TabPFNRegressor
      from tabpfn.constants import ModelVersion

      model = TabPFNRegressor.create_default_for_version(ModelVersion.V3_5_FAST)
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      ```
    </CodeGroup>
  </Tab>
</Tabs>

See [Selecting Model Version](/models/selecting-model-version) for more on choosing models in the local package and hosted API.