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docs/getting_started/getting_started.md

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docs/getting_started/install.md

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You can access our models through our API (https://github.com/automl/tabpfn-client) or via our user interface built on top of the API (https://www.priorlabs.ai/tabpfn-gui).
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We will release open weights models soon, currently we are available via api and via our user interface built on top of the API.
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You can access our models through our API (https://github.com/automl/tabpfn-client) or via our user interface built on top of the API (https://www.ux.priorlabs.ai/).
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=== "Python API Client (No GPU, Online)"
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```bash
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pip install tabpfn-client
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```
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To log into your account, create an account through our website or through the programmatic interface. Then log into that account by setting the token with
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```python
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from tabpfn_client import config
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# Retrieve Token
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with open(config.g_tabpfn_config.user_auth_handler.CACHED_TOKEN_FILE, 'r') as file:
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token = file.read()
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print(f"TOKEN: {token}")
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from tabpfn_client import config
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# Set Token
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service_client = config.ServiceClient()
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config.g_tabpfn_config.user_auth_handler = config.UserAuthenticationClient(service_client=service_client)
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user_auth = config.g_tabpfn_config.user_auth_handler.set_token(token)
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```
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=== "Python Local (GPU)"
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!!! warning
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Not released yet
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```bash
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pip install tabpfn
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```
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=== "Web Interface"
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You can access our models through our Interface at [Prior Labs](https://www.ux.priorlabs.ai/).
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You can access our models through our Interface [here](https://www.ux.priorlabs.ai/).
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=== "R"
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!!! warning
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R support is currently under development.
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You can find a work in progress at [TabPFN R](https://github.com/robintibor/R-tabpfn).
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Looking for contributors!

docs/getting_started/intended_use.md

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# Usage tips
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!!! note
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For a simple example getting started with classification see [classification tutorial](../tutorials/classification.md).
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We provide a comprehensive demo notebook that guides through installation and functionalities at [Interactive Colab Tutorial (with GPU usage)](https://tinyurl.com/tabpfn-colab-local) and [Interactive Colab Tutorial (without GPU usage)](https://tinyurl.com/tabpfn-colab-online).
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### When to use TabPFN
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TabPFN excels in handling small to medium-sized datasets with up to 10,000 samples and 500 features. For larger datasets, approaches such as CatBoost, XGB, or AutoGluon are likely to outperform TabPFN.

docs/stylesheets/extra.css

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--md-primary-fg-color--light: #4ECDC4;
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--md-accent-fg-color: #9c27b0;
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--md-accent-fg-color--transparent: #9c27b010;
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--md-typeset-a-color: #2196f3; /* Brighter blue for normal links */
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--md-typeset-a-color--hover: #64b5f6; /* Even brighter blue for hover state */
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}
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color: #2196f3;
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mkdocs.yml

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- Home: index.md # Includes link to
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- Getting Started:
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- Installation: getting_started/install.md
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- Intended Use: getting_started/intended_use.md
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- API Usage Guide: getting_started/api.md
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- Tutorials:
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- Classification: tutorials/classification.md

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