Instructions to use FluidInference/gliner2-5-small-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use FluidInference/gliner2-5-small-coreml with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("FluidInference/gliner2-5-small-coreml") # 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
Download reports/optimization-embedding-w8-conversion.json from FluidInference/gliner2-5-small-coreml: direct link, hf CLI and curl.
- Browser
- Download file 401 Bytes
-
https://huggingface.co/FluidInference/gliner2-5-small-coreml/resolve/main/reports/optimization-embedding-w8-conversion.json
- Command line
-
hf download hf://FluidInference/gliner2-5-small-coreml/reports/optimization-embedding-w8-conversion.json
-
curl -L -o optimization-embedding-w8-conversion.json https://huggingface.co/FluidInference/gliner2-5-small-coreml/resolve/main/reports/optimization-embedding-w8-conversion.json
401 Bytes
| { | |
| "source": "build/gliner2_small_classification_fp16_L128_K8.mlpackage", | |
| "output": "build/gliner2_small_classification_embedding_w8_L128_K8.mlpackage", | |
| "method": "embedding", | |
| "granularity": "per_channel", | |
| "selected_weights": [ | |
| "encoder_embeddings_word_embeddings_weight_to_fp16" | |
| ], | |
| "source_bytes": 151542752, | |
| "output_bytes": 102771476, | |
| "compression_seconds": 2.445869125018362 | |
| } | |