gliner-guard-uniencoder-onnx

Experimental ONNX build - Unofficial ONNX export of hivetrace/gliner-guard-uniencoder.

gliner2-onnx

GLiNER2 ONNX runtime for Python. Runs GLiNER2 models without PyTorch.

This library is experimental. The API may change between versions.

Features

• Zero-shot NER and text classification
• Runs with ONNX Runtime (no PyTorch dependency)
• FP32 and FP16 precision support
• GPU acceleration via CUDA

All other GLiNER2 features such as JSON export are not supported.

Installation

pip install gliner2-onnx

NER

from gliner2_onnx import GLiNER2ONNXRuntime

runtime = GLiNER2ONNXRuntime.from_pretrained("lmo3/gliner2-large-v1-onnx")

entities = runtime.extract_entities(
    "John works at Google in Seattle",
    ["person", "organization", "location"]
)
# [
#   Entity(text='John', label='person', start=0, end=4, score=0.98),
#   Entity(text='Google', label='organization', start=14, end=20, score=0.97),
#   Entity(text='Seattle', label='location', start=24, end=31, score=0.96)
# ]

Classification

from gliner2_onnx import GLiNER2ONNXRuntime

runtime = GLiNER2ONNXRuntime.from_pretrained("lmo3/gliner2-large-v1-onnx")

# Single-label classification
result = runtime.classify(
    "Buy milk from the store",
    ["shopping", "work", "entertainment"]
)
# {'shopping': 0.95}

# Multi-label classification
result = runtime.classify(
    "Buy milk and finish the report",
    ["shopping", "work", "entertainment"],
    threshold=0.3,
    multi_label=True
)
# {'shopping': 0.85, 'work': 0.72}

CUDA

To use CUDA for GPU acceleration:

runtime = GLiNER2ONNXRuntime.from_pretrained(
    "lmo3/gliner2-large-v1-onnx",
    providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
)

Precision

Both FP32 and FP16 models are supported. Only the requested precision is downloaded.

runtime = GLiNER2ONNXRuntime.from_pretrained(
    "lmo3/gliner2-large-v1-onnx",
    precision="fp16"
)

Models

Pre-exported ONNX models:

Note: gliner2-base-v1 is not supported (uses a different architecture).

Exporting Models

To export your own models, clone the repository and use make:

git clone https://github.com/lmoe/gliner2-onnx
cd gliner2-onnx

# FP32 only
make onnx-export MODEL=fastino/gliner2-large-v1

# FP32 + FP16
make onnx-export MODEL=fastino/gliner2-large-v1 QUANTIZE=fp16

Output is saved to model_out/<model-name>/.

JavaScript/TypeScript

For Node.js, see @lmoe/gliner-onnx.js.

Credits

• fastino-ai/GLiNER2 - Original GLiNER2 implementation
• fastino/gliner2-large-v1 - Pre-trained models

License

MIT