# Text Generation with Custom HuggingFace Model

This demo helps you learn about:

* Launching a pre trained custom text generation HuggingFace model in a Seldon Pipeline
* Sending a text input request to get a generated text prediction

The custom HuggingFace text generation model is based on the [TinyStories-1M](https://huggingface.co/roneneldan/TinyStories-1M) model in the HuggingFace hub.

## Create a Seldon ML Pipeline

1. In the **Overview** page click **Create new deployment**.
2. Enter the deployment details as follows:
   * Name: `hf-custom-tiny-stories`
   * Namespace: `seldon`
   * Type: `Seldon ML Pipeline`
3. Configure the default predictor as follows:

   * Runtime: `HuggingFace`
   * Model Project: `default`
   * Model URI: `gs://seldon-models/scv2/samples/mlserver_1.6.0/huggingface-text-gen-custom-tiny-stories`
   * Storage Secret: (leave blank/none)

   ![Default predictor spec](/files/u5kNu7wRY6XXIprQkeLP)
4. Click **Next** for the remaining steps and click **Launch**.

When the deployment is launched successfully, the status of the deployment becomes `Available`.

## Make Predictions

1. Click the `hf-custom-tiny-stories` deployment that you created.
2. In the deployment **Dashboard** page , click **Predict** in the left pane.
3. In the **Predict REST API** dialog, click **Enter JSON** and paste the following text:

   ```JSON
   {
     "inputs": [{
       "name": "args",
       "shape": [1],
       "datatype": "BYTES",
       "data": ["The brown fox jumped"]
     }]
   }
   ```
4. Click **Predict**.

   ![A screenshot showing the Predict page with the textarea pre-populated](/files/z2b3NN3PB7nNfO7OKxbr)

## Next steps

Try other [demos](https://github.com/SeldonIO/testing-gitbook/blob/v2.4/demos/README.md) or try a larger-scale model. You can find one in `gs://seldon-models/scv2/samples/mlserver_1.6.0/huggingface-text-gen-custom-gpt2`. However, you may need to request more memory.


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