Accessing Model Weights

The local tabpfn package downloads model weights to your machine. TabPFN-3.5, including Fast, requires acceptance of the model license, as do TabPFN-3, 2.6, and 2.5.

Local package

Start with the latest package:

pip install --upgrade tabpfn

Run the local example in the quickstart. When weights need downloading, tabpfn checks your token and license acceptance. In an interactive terminal with a browser, it opens the Prior Labs login page so you can sign in and accept the license for your chosen model. If the browser cannot return the token, paste your API key at the terminal prompt. Over SSH without a local browser, the package prints a login URL and waits for you to paste the key. Successful authentication is cached locally for future downloads. When switching model families, accept the corresponding license if prompted.

Notebooks, servers, and CI

Set up authentication before running the model:

Sign in to Prior Labs and accept your model’s license under Licenses.
Create or copy an API key from the same account.
Set TABPFN_TOKEN in the environment where Python runs:

export TABPFN_TOKEN="<your-api-key>"
export TABPFN_NO_BROWSER=1

import os
from getpass import getpass

os.environ["TABPFN_TOKEN"] = getpass("Prior Labs API key: ")
os.environ["TABPFN_NO_BROWSER"] = "1"

TABPFN_NO_BROWSER=1 disables interactive login. Your account still needs to have accepted the license before weights can be downloaded.

Google Colab

Complete the license and API-key steps above. In Colab, add a secret named TABPFN_TOKEN and enable Notebook access. Then load it before running the model:

import os
from google.colab import userdata

os.environ["TABPFN_TOKEN"] = userdata.get("TABPFN_TOKEN")
os.environ["TABPFN_NO_BROWSER"] = "1"

Offline use

On a machine with internet access, complete the setup above and run each model you plan to use. The tabpfn package downloads and caches the required weights automatically. Locate the model cache directory:

from tabpfn.model_loading import get_cache_dir

print(get_cache_dir())

Copy that directory to the offline machine and install the same tabpfn version and dependencies. Set the cache location before importing tabpfn:

export TABPFN_MODEL_CACHE_DIR="/path/to/tabpfn-weights"

The directory must contain the required weights under their original filenames. A cache path alone does not prevent downloads: the package attempts to download missing weights. Continue with the quickstart to run predictions, or select a model version.

Getting Started

Metering and rate limits

Capabilities

Agentic

Integrations

Troubleshooting

TabPFN in Action

Customer References

Changelog

Accessing Model Weights

Local package

Notebooks, servers, and CI

Google Colab

Offline use

​Local package

​Sign in and accept the license

​Notebooks, servers, and CI

​Google Colab

​Offline use

Local package

Sign in and accept the license

Notebooks, servers, and CI

Google Colab

Offline use