gliner2-mlx

An MLX port of GLiNER2 for fast information extraction on Apple Silicon.

GLiNER2 is a multi-task model for named entity recognition, text classification, relation extraction, and structured data extraction. This package runs the compute-heavy transformer encoder on MLX (Apple's GPU framework) while reusing GLiNER2's preprocessing and postprocessing, delivering significant speedups on Mac.

Installation

pip install gliner2-mlx

Or with uv:

uv add gliner2-mlx

Quick Start

from gliner2_mlx import GLiNER2MLX

model = GLiNER2MLX.from_pretrained("fastino/gliner2-base-v1")

# Named entity recognition
result = model.extract_entities(
    "Apple CEO Tim Cook announced the iPhone 15 launch in Cupertino.",
    ["company", "person", "product", "location"],
)
print(result)
# {'entities': {'company': ['Apple'], 'person': ['Tim Cook'], 'product': ['iPhone 15'], 'location': ['Cupertino']}}

Features

The API mirrors GLiNER2 — all extraction methods work the same way:

# Batch entity extraction
results = model.batch_extract_entities(
    ["Apple released iPhone 15.", "Google announced Pixel 8."],
    ["company", "product"],
    batch_size=8,
)

# Text classification
result = model.classify_text(
    "I love this product, it's amazing!",
    {"sentiment": ["positive", "negative", "neutral"]},
)

# Relation extraction
result = model.extract_relations(
    "Elon Musk founded SpaceX in 2002.",
    ["founded_by"],
)

# Structured data extraction
result = model.extract_json(
    "John Smith, aged 35, is a software engineer at Google.",
    {"person": ["name::str", "age::str", "company::str"]},
)

# Schema builder (same as GLiNER2)
from gliner2.inference.engine import Schema

schema = (
    Schema()
    .entities(["company", "person"])
    .relations(["works_for"])
)
result = model.extract("Tim Cook works at Apple.", schema)

Benchmark

Measured on Apple M3 Max, fastino/gliner2-base-v1, 1000 iterations per scenario with 10 warmup iterations. Interleaved execution order to eliminate systematic bias. All results statistically significant (p < 0.001, paired t-test).

How It Works

gliner2-mlx ports the neural network inference to MLX while keeping GLiNER2 as a dependency for everything else:

• MLX (GPU): DeBERTaV2 encoder, span representation, classification/count heads
• GLiNER2 (CPU): Tokenization, schema processing, result formatting, overlap removal

On first use, from_pretrained automatically converts PyTorch weights to MLX format and caches them in ~/.cache/gliner2-mlx/.

Contributing

See CONTRIBUTING.md for development setup, running tests, linting, benchmarking, and publishing instructions.

License

MIT