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
| # 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 | |