How to use model cards in Model Garden

Click a model card to use the model associated with it. You can click a model card to test prompts, tune a model, create applications, and view code samples.

Test prompts

Use the Vertex AI PaLM API model card to test prompts.

  1. In the Google Cloud console, go to the Model Garden page.

    Go to Model Garden

  2. Find a supported model that you want to test and click View details.

  3. Click Open prompt design.

    You're taken to the Prompt design page.

  4. In Prompt, enter the prompt that you want to test.

  5. Optional: Configure the model parameters.

  6. Click Submit.

Tune a model

To tune supported models, use a Vertex AI pipeline or a notebook.

Tune using a pipeline

The BERT and T5-FLAN models support model tuning using a pipeline.

  1. In the Google Cloud console, go to the Model Garden page.

    Go to Model Garden

  2. In Search models, enter BERT or T5-FLAN, then click the magnifying glass to search.

  3. Click View details on the T5-FLAN or the BERT model card.

  4. Click Open fine-tuning pipeline.

    You're taken to the Vertex AI pipelines page.

  5. To start tuning, click Create run.

Tune in a notebook

The model cards for most open source foundation models and fine-tunable models support tuning in a notebook.

  1. In the Google Cloud console, go to the Model Garden page.

    Go to Model Garden

  2. Find a supported model that you want to tune and click View details.

  3. Click Open notebook.

Deploy a model

You can deploy a model from its model card, such as Stable Diffusion. When deploying a model, you can choose to use a Compute Engine reservation. For more information, see Use reservations with prediction.

  1. In the Google Cloud console, go to the Model Garden page.

    Go to Model Garden

  2. Find a supported model that you want to deploy, and click its model card.

  3. Click Deploy to open the Deploy model pane.

  4. In the Deploy model pane, specify details for your deployment.

    1. Use or modify the generated model and endpoint names.
    2. Select a location to create your model endpoint in.
    3. Select a machine type to use for each node of your deployment.

    4. To use a Compute Engine reservation, under the Deployment settings section, select Advanced.

      For the Reservation type field, select a reservation type. The reservation must match your specified machine specs.

      • Automatically use created reservation: Vertex AI automatically selects an allowed reservation with matching properties. If there's no capacity in the automatically selected reservation, Vertex AI uses the general Google Cloud resource pool.
      • Select specific reservations: Vertex AI uses a specific reservation. If there's no capacity for your selected reservation, an error is thrown.
      • Don't use (default): Vertex AI uses the general Google Cloud resource pool. This value has the same effect as not specifying a reservation.

  5. Click Deploy.

View code samples

Most of the model cards for task-specific solutions models contain code samples that you can copy and test.

  1. In the Google Cloud console, go to the Model Garden page.

    Go to Model Garden

  2. Find a supported model that you want to view code samples for and click the Documentation tab.

  3. The page scrolls to the documentation section with sample code embedded in place.

Create a vision app

The model cards for applicable computer vision models support creating a vision application.

  1. In the Google Cloud console, go to the Model Garden page.

    Go to Model Garden

  2. Find a vision model in the Task specific solutions section that you want to use to create a vision application and click View details.

  3. Click Build app.

    You're taken to Vertex AI Vision.

  4. In Application name, enter a name for your application and click Continue.

  5. Select a billing plan and click Create.

    You're taken to Vertex AI Vision Studio where you can continue creating your computer vision application.