# Add models to a private hub
<a name="jumpstart-curated-hubs-admin-guide-add-models"></a>

After creating a private hub, you can then add allow-listed models. For the full list of available JumpStart models, see the [Built-in Algorithms with pre-trained Model Table](https://sagemaker.readthedocs.io/en/stable/doc_utils/pretrainedmodels.html) in the SageMaker Python SDK reference.

1. You can filter through the available models programmatically using the `hub.list_sagemaker_public_hub_models()` method. You can optionally filter by categories such as framework (`"framework == pytorch"`), tasks such as image classification (`"task == ic"`), and more. For more information about filters, see [https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/jumpstart/notebook_utils.py](https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/jumpstart/notebook_utils.py). The filter parameter in the `hub.list_sagemaker_public_hub_models()` method is optional. 

   ```
   filter_value = "framework == meta"
   response = hub.list_sagemaker_public_hub_models(filter=filter_value)
   models = response["hub_content_summaries"]
   while response["next_token"]:
       response = hub.list_sagemaker_public_hub_models(filter=filter_value, next_token=response["next_token"])
       models.extend(response["hub_content_summaries"])
   
   print(models)
   ```

1. You can then add the filtered models by specifying the model ARN in the `hub.create_model_reference()` method.

   ```
   for model in models:
       print(f"Adding {model.get('hub_content_name')} to Hub")
       hub.create_model_reference(model_arn=model.get("hub_content_arn"), model_name=model.get("hub_content_name"))
   ```