Sharing and Loading Models From the Hugging Face Hub
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:
- Share a
timmmodel on the Hub
- How to load that model back from the Hub
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:
Or, if you’re using a notebook, you can use the
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
dict(labels=['a', 'b', 'c', 'd']) timm.models.hub.push_to_hf_hub(model, 'resnet18-random', model_config=model_cfg)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.
'hf_hub:nateraw/resnet18-random', pretrained=True)model_reloaded = timm.create_model(