> ## Documentation Index
> Fetch the complete documentation index at: https://baseten-philip-copy-0226.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Step 2: Call the model server

> Call predict to test the model server

Once your model server has finished deploying, you can call it from your terminal.

### Call the model server

Model server input must be JSON-serializable: a string, number, list, or dictionary.

<Note>
  Note the double quotes to make the data parameter a string.

  `truss predict` uses the same data formatting on the as cURL.
</Note>

**Invocation**

```sh
truss predict -d '"Truss is awesome!"'
```

**Response**

```json
"Truss is awesome!"
```

Our model server is up and responding to requests. In the next step, we'll set up the last piece of the developer loop on Truss.

<RequestExample>
  ```python model/model.py
  class Model:
      def __init__(self, **kwargs):
          self._model = None

      def load(self):
          pass

      def predict(self, model_input):
          return model_input
  ```

  ```yaml config.yaml
  environment_variables: {}
  model_name: My First Truss
  requirements: []
  resources:
    accelerator: null
    cpu: "1"
    memory: 2Gi
    use_gpu: false
  secrets: {}
  system_packages: []
  ```
</RequestExample>