mlx-gliner2 0.1.1

GLiNER2 information extraction on Apple Silicon via MLX

MLX GLiNER2

Port of GLiNER2 to Apple MLX for efficient information extraction on Apple Silicon.

Installation

pip install mlx-gliner2

Quick Start

# One-time model conversion
python -m mlx_gliner2.convert --repo-id fastino/gliner2-base-v1

from mlx_gliner2 import GLiNER2

extractor = GLiNER2.from_pretrained("mlx_models/fastino_gliner2-base-v1")

result = extractor.extract_entities(
    "Apple CEO Tim Cook announced iPhone 15 in Cupertino.",
    ["company", "person", "product", "location"]
)
# {"company": ["Apple"], "person": ["Tim Cook"], "product": ["iPhone 15"], "location": ["Cupertino"]}

Features

• Entity Extraction - Named entity recognition
• Text Classification - Single and multi-label
• Structured JSON Extraction - Parse structured data from text
• Relation Extraction - Extract relationships between entities
• Multi-Task Schemas - Combine all tasks in a single pass
• Batch Processing - Process multiple texts efficiently

No PyTorch or GPU required. Runs entirely on MLX.

Development

python3 -m venv .venv
source .venv/bin/activate
pip install -e .
pip install pytest

Testing

Tests require a converted model. Run the conversion first, then pytest:

python -m mlx_gliner2.convert --repo-id fastino/gliner2-base-v1
pytest tests/ -v

To use a model at a custom path, set the MLX_GLINER2_MODEL environment variable:

MLX_GLINER2_MODEL=path/to/model pytest tests/ -v