Instructions to use mircq/GLINER-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use mircq/GLINER-INT8 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("mircq/GLINER-INT8") - GLiNER2
How to use mircq/GLINER-INT8 with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("mircq/GLINER-INT8") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
File size: 941 Bytes
29cd9ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | # gliner2-multi-v1 — INT8 ONNX (CPU)
Dynamically-quantized INT8 ONNX export of `fastino/gliner2-multi-v1`.
This bundle contains **only** the INT8 graphs — load it with `precision="int8"`.
```python
from gliner2_onnx import GLiNER2ONNXRuntime
rt = GLiNER2ONNXRuntime(
"gliner2-multi-v1-int8", # this folder
precision="int8", # required: no fp32 graphs are included
providers=["CPUExecutionProvider"],
)
rt.extract_entities("pagamento polizza tfr A4983AS", ["amount", "reference_number"])
rt.classify("acquisto gasolio automezzi", ["carburanti", "polizze"], multi_label=True)
```
For best CPU throughput set intra-op threads to your physical core count via ORT
`SessionOptions`.
## Contents
- `gliner2_config.json` — model config, references the INT8 graphs only
- `config.json`, `tokenizer.json`, `tokenizer_config.json`
- `onnx/*_int8.onnx` — encoder / classifier / span_rep / count_embed
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