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).

Scenario	PyTorch CPU	MLX GPU	Speedup
Single entity extraction	41.79 ms	14.35 ms	2.91x
Batch entity extraction (8 texts)	106.19 ms	35.41 ms	3.00x
Long text entity extraction	68.78 ms	19.55 ms	3.52x
Structure extraction	41.60 ms	14.24 ms	2.92x
Relation extraction	50.43 ms	17.06 ms	2.96x
Overall	61.76 ms	20.12 ms	3.07x

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