Hugging Face Large Language Model (LLM)

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.

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)

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

docker-compose up

Validate that the backend is running:

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

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.