gliner-api

A minimal FastAPI app serving GLiNER models.

Features

• FastAPI backend for serving GLiNER models (NER).
• Gradio frontend (optional) for interactive use.
• Prometheus metrics endpoint (/metrics).
• Configurable via YAML, CLI, or environment variables.
• Docker and Docker Compose support.
• ONNX inference support (including quantized models).
• API key authentication (optional).
• Custom metrics port and enable/disable option for Prometheus metrics.

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Documentation

For detailed documentation, see DeepWiki (⚠️ AI-generated)

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Live Demo

You can try the live demo of the GLiNER API container in it's Huggingface Space: GLiNER API Demo.

It uses a minimally changed image to make it work in the Huggingface Space environment.

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Usage

Run with Docker

You can either build the container yourself or use a prebuilt image from GitHub Container Registry.

Run prebuilt container (recommended)

docker run \
  -p 8080:8080 \
  -p 9090:9090 \
  -v $(pwd)/config.yaml:/app/config.yaml \
  -v $HOME/.cache/huggingface:/app/huggingface \
  ghcr.io/freinold/gliner-api:latest

• -v $(pwd)/config.yaml:/app/config.yaml mounts your config file (edit as needed)
• -v $HOME/.cache/huggingface:/app/huggingface mounts your Huggingface cache for faster model loading

Build and run locally (CPU version)

docker build \
  -f cpu.Dockerfile \
  --build-arg IMAGE_CREATED="$(date -u +%Y-%m-%dT%H:%M:%SZ)" \
  --build-arg IMAGE_REVISION="$(git rev-parse HEAD)" \
  --build-arg IMAGE_VERSION="$(git describe --tags --always)" \
  -t gliner-api .

docker run --rm \
  -p 8080:8080 \
  -p 9090:9090 \
  -v $(pwd)/config.yaml:/app/config.yaml \
  -v $HOME/.cache/huggingface:/app/huggingface \
  gliner-api

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Run with Docker Compose

Edit compose.yaml to select the config you want (see example_configs/).

Then start:

docker compose up --build

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Run the app directly

Be sure to check the installation instructions first.

uv run main.py [OPTIONS]

Or with FastAPI CLI:

fastapi run main.py --host localhost

Run options

uv run main.py --help

Option	Description	Default
--use-case / --name	Use case for the GLiNER model (application/domain)	general
--model-id	Huggingface model ID (browse models)	knowledgator/gliner-x-base
--onnx-enabled	Use ONNX for inference	False
--onnx-model-path	Path to ONNX model file	model.onnx
--default-entities	Default entities to detect	['person', 'organization', 'location', 'date']
--default-threshold	Default detection threshold	0.5
--api-key	API key for authentication (if set, required in requests)	null
--host	Host address	0.0.0.0
--port	Port	8080
--metrics-enabled	Enable Prometheus metrics endpoint	True
--metrics-port	Port for Prometheus metrics endpoint	9090
--frontend-enabled	Enable Gradio frontend	True

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API & Frontend Endpoints

Description	Path	Demo Link
Gradio Frontend (if enabled)	/	Frontend
API Docs (Swagger)	/docs	Swagger UI
API Docs (ReDoc)	/redoc	ReDoc
Prometheus Metrics	/metrics	(no public demo link; available on metrics port if enabled)

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Example Request

curl -X POST "http://localhost:8080/api/invoke" -H "Content-Type: application/json" -d '{"text": "Steve Jobs founded Apple in Cupertino."}'

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Installation

Prerequisites:

• Python 3.12.11
• uv (for dependency management)

Install dependencies:

# CPU version
uv sync --extra cpu [--extra frontend]

# GPU version
uv sync --extra gpu [--extra frontend]

The frontend is optional, but encouraged for interactive use.

Install from source:

git clone https://github.com/freinold/gliner-api.git
cd gliner-api
uv sync --extra cpu  # or --extra gpu

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Configuration

You can configure the app via:

• config.yaml (default, see example_configs/)
• CLI options (see above)
• Environment variables (prefix: GLINER_API_)

Example configs:

• example_configs/general.yaml (default NER)
• example_configs/pii.yaml (PII detection)
• example_configs/medical.yaml (medical NER)
• example_configs/general_onnx.yaml (ONNX inference)
• example_configs/general_onnx_quantized.yaml (quantized ONNX)

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Used Frameworks & Libraries

• FastAPI (API backend)
• Gradio (optional frontend)
• Uvicorn (ASGI server)
• Prometheus Client (metrics)
• Huggingface Hub (model loading)
• PyTorch (CPU/GPU inference)
• ONNX (optional, for ONNX models)
• uv (dependency management)

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License

See LICENSE.

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