> ## 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 5: Implement model load

> Load an ML model into your Truss

The other essential file in a Truss is `model/model.py`. In this file, you write a `Model` class: an interface between the ML model that you're packaging and the model server that you're running it on.

The code to load and invoke a model in a Jupyter notebook or Python script maps directly to the code used in `model/model.py`.

<Frame>
  <img src="https://mintlify.s3-us-west-1.amazonaws.com/baseten-philip-copy-0226/images/notebook-to-model.png" />
</Frame>

We'll go line-by-line through the code. Open `model/model.py` in your text editor.

### Import transformers

Import `transformers.pipeline` at the top of `model/model.py`:

<Tabs>
  <Tab title="Code">
    ```python model/model.py
    from transformers import pipeline
    ```
  </Tab>

  <Tab title="Diff">
    ```diff model/model.py
    + from transformers import pipeline
    ```
  </Tab>
</Tabs>

### Load the model

The `Model.load()` function runs exactly once when the model server is spun up or patched and loads the model onto the model server.

Update `load()` to bring in the `text-classification` model from `transformers.pipeline`:

<Tabs>
  <Tab title="Code">
    ```python model/model.py
    def load(self):
        self._model = pipeline("text-classification")
    ```
  </Tab>

  <Tab title="Diff">
    ```diff model/model.py
    def load(self):
    -    # Load model here and assign to self._model.
    -    pass
    +    self._model = pipeline("text-classification")
    ```
  </Tab>
</Tabs>

You should see this change patched onto the model server in your `truss watch` terminal tab.

<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 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>