> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/timm/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/timm/tutorials/share-and-load-models-from-the-boinc-ai-hub.md).

# Share and Load Models from the BOINC AI Hub

## Sharing and Loading Models From the Hugging Face Hub

The `timm` library has a built-in integration with the BOINC AI 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.

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```
pip install huggingface_hub
```

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

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```
huggingface-cli login
```

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

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

### Sharing a Model

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```
>>> 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.

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```
>>> model_cfg = dict(labels=['a', 'b', 'c', 'd'])
>>> timm.models.hub.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`](https://huggingface.co/nateraw/resnet18-random), which is the model we just pushed to the Hub.

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