#
Sharing and Loading Models From the Hugging Face Hub

The `timm` library has a built-in integration with the Hugging Face Hub, making it easy to share and load models from the 🤗 Hub.

In this short guide, we’ll see how to:

1. Share a `timm` model on the Hub
2. How to load that model back from the Hub

##
Authenticating

First, you’ll need to make sure you have the `huggingface_hub` package installed.

    pip install huggingface_hub

Then, you’ll need to authenticate yourself. You can do this by running the following command:

    huggingface-cli login

Or, if you’re using a notebook, you can use the `notebook_login` helper:

    >>> from huggingface_hub import notebook_login
    >>> notebook_login()

##
Sharing a Model

    >>> import timm
    >>> model = timm.create_model('resnet18', pretrained=True, num_classes=4)

Here is where you would normally train or fine-tune the model. We’ll skip that for the sake of this tutorial.

Let’s pretend we’ve now fine-tuned the model. The next step would be to push it to the Hub! We can do this with the `timm.models.hub.push_to_hf_hub` function.

    >>> model_cfg = dict(label_names=['a', 'b', 'c', 'd'])
    >>> timm.models.push_to_hf_hub(model, 'resnet18-random', model_config=model_cfg)

Running the above would push the model to `<your-username>/resnet18-random` on the Hub. You can now share this model with your friends, or use it in your own code!

##
Loading a Model

Loading a model from the Hub is as simple as calling `timm.create_model` with the `pretrained` argument set to the name of the model you want to load. In this case, we’ll use `nateraw/resnet18-random`, which is the model we just pushed to the Hub.

    >>> model_reloaded = timm.create_model('hf_hub:nateraw/resnet18-random', pretrained=True)

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