---
title: Hugging Face Large Language Model (LLM)
type: guide
tier: all
order: 20
hide_menu: true
hide_frontmatter_title: true
meta_title: Label Studio tutorial to run Hugging Face Large Language Model backend
meta_description: This tutorial explains how to run Hugging Face Large Language model backend in Label Studio. Hugging Face Large Language Model Backend is a machine learning backend designed to work with Label Studio, providing a custom model for text generation.
categories:
- Generative AI
- Large Language Model
- Text Generation
- Hugging Face

image: "/guide/ml_tutorials/hf-llm.png"

Hugging Face Large Language Model backend

This machine learning backend is designed to work with Label Studio, providing a custom model for text generation. The model is based on the Hugging Face's transformers library and uses a pre-trained model.

Check text generation pipelines on Hugging Face for more details.

Before you begin

Before you begin, you must install the Label Studio ML backend.

This tutorial uses the huggingface_llm example.

Label Studio XML labeling config

This ML backend is compatible with a Label Studio labeling configuration that uses a <TextArea> tag. Here is an example of a compatible labeling configuration:

<View>
    <Text name="input_text" value="$text"/>
  <TextArea name="generated_text"  toName="input_text"/>
</View>

When you open the task in Label Studio, the text box will show the generated text based on the prompt defined in <Text>. Be sure you include some instructions in prompt (for example, "Summarize the following text: ...") to see the meaningful results.

Running with Docker (recommended)

1. Start the Machine Learning backend on http://localhost:9090 with the prebuilt image:

docker-compose up

1. Validate that the backend is running:

$ curl http://localhost:9090/
{"status":"UP"}

1. Create a project in Label Studio. Then from the Model page in the project settings, connect the model. The default URL is http://localhost:9090.

Building from source (advanced)

To build the ML backend from source, you have to clone the repository and build the Docker image:

docker-compose build

Running without Docker (advanced)

To run the ML backend without Docker, you have to clone the repository and install all dependencies using pip:

python -m venv ml-backend
source ml-backend/bin/activate
pip install -r requirements.txt

Then you can start the ML backend:

label-studio-ml start ./huggingface_llm

Configuration

Parameters can be set in docker-compose.yml before running the container.

The following common parameters are available:
- MODEL_NAME: The name of the pre-trained model to use for text generation. Default is facebook/opt-125m.
- MAX_LENGTH: The maximum length of the generated text. Default is 50.
- BASIC_AUTH_USER: The basic auth user for the model server.
- BASIC_AUTH_PASS: The basic auth password for the model server.
- LOG_LEVEL: The log level for the model server.
- WORKERS: The number of workers for the model server.
- THREADS: The number of threads for the model server.

Customization

The ML backend can be customized by adding your own models and logic inside the ./huggingface_llm directory.