Deploy bart-base-portuguese

Explore our Inference Catalog to deploy popular models on optimized configuration.

Endpoint Name

Input a name for your new endpoint

Warning: deploying this model will probably fail because no

"handler.py"

file was found in the repository. Try selecting a different model or creating a custom handler.

More options

Commit Revision

Specify a revision commit hash for the Hugging Face repository

optional

Task

Select a supported task with the right inputs and outputs for your model pipeline, or define a custom task.

Download Pattern

Specify what file type(s) should be downloaded from the repository.

Automatic Scale-to-Zero

Endpoints scaled to 0 replicas are not billed. They may take some time to scale back up once they start receiving requests again.

Number of replicas

Automatically scale the number of replicas within Min and Max based on compute usage. Min is always 0 if Scale-To-Zero is active.

Min Max

More options

Autoscaling Strategy

Control what type of trigger will cause your Endpoint to scale up.

Hardware Usage Pending Requests

Hardware Utilization Threshold (%)

A scale up event will be triggered if the average hardware utilisation (%) exceeds this threshold for more than 20 seconds.

Protected Public Private

A protected Endpoint is available from the Internet, secured with TLS/SSL and requires a valid Hugging Face Token for Authentication.

Container Type

The Default container is the easiest way to deploy endpoints, and is very flexible thanks to custom Inference Handlers. You can also select a container optimized for Text-Generation inference, or link your own Custom container.

Default Env

Environment variables that will be provided to your container during deployment.

Key

Value

Secret Env

Same as Default, but people with access to this endpoint will not be able to read these values after creation.

Key

Value