> ## 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 6: Implement model inference

> Add model inference and invoke the model server

To complete `model/model.py`, we'll implement model inference and invoke our finished model.

### Run model inference

The `Model.predict()` function runs every time the model server is called.

We'll use the text classification model in `predict()` and return the results:

<Tabs>
  <Tab title="Code">
    ```python model/model.py
    def predict(self, model_input):
        return self._model(model_input)
    ```
  </Tab>

  <Tab title="Diff">
    ```diff model/model.py
    def predict(self, model_input):
    -    return model_input
    +    return self._model(model_input)
    ```
  </Tab>
</Tabs>

### Invoke your finished model

After `truss watch` shows that the server is updated, it's time to invoke your finished model using `truss predict` in your terminal:

**Invocation**

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

**Response**

```json
[
  {
    "label": "POSITIVE",
    "score": 0.999873161315918
  }
]
```

<RequestExample>
  ```python model/model.py  ●
  from transformers import pipeline


  class Model:
      def __init__(self, **kwargs):
          self._model = None

      def load(self):
          self._model = pipeline("text-classification")

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

  ```yaml config.yaml
  environment_variables: {}
  model_name: My First Truss
  requirements:
    - torch==2.0.1
    - transformers==4.30.0
  resources:
    accelerator: null
    cpu: "1"
    memory: 2Gi
    use_gpu: false
  secrets: {}
  system_packages: []
  ```
</RequestExample>