> ## 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 4: Set Python requirements

> Add required Python packages to the model server.

For this tutorial, we want to package a [basic text classification model](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline) with this Truss to deploy it on our model server. The model is from the `transformers` package and thus requires two Python packages to run it: [Transformers](https://huggingface.co/docs/transformers/index) and [PyTorch](https://pytorch.org/).

We'll use `config.yaml` to add the Python packages that the text classification model requires to the model server.

<Tip>
  On the right side, you'll see what your Truss should look like after each step!
</Tip>

### Open `config.yaml` in a text editor

One of the two essential files in a Truss is `config.yaml`, which configures the model serving environment. For a complete list of the config options, see [the config reference](/reference/config).

<Warning>
  Note that the `model_name` parameter is already set to `My First Truss` or whatever name you picked when you ran `truss init`. Don't change this name as it must remain consistant with the remote host.
</Warning>

### Add Python requirements

Python requirements are listed just like they appear in a `requirements.txt`. ML moves fast; always pin your requirement versions to make sure you're getting compatible packages.

Update `config.yaml` with the required packages:

<Tip>
  Check the "Diff" tab to see exactly which lines of code change.
</Tip>

<Tabs>
  <Tab title="Code">
    ```yaml config.yaml
    requirements:
      - torch==2.0.1
      - transformers==4.30.0
    ```
  </Tab>

  <Tab title="Diff">
    ```diff config.yaml
    - requirements: []
    + requirements:
    +   - torch==2.0.1
    +   - transformers==4.30.0
    ```
  </Tab>
</Tabs>

### Check for a patch

After you save your changes to `config.yaml`, you should see two things happen:

1. Your `truss watch` tab should show that a patch was applied to your model server.
2. The model server logs on your [Baseten account](https://app.baseten.co) should show log entries from the packages being installed in your model server.

Now you're ready to add the text classification model.

<RequestExample>
  ```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: []
  ```

  ```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
  ```
</RequestExample>