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-package-hashes.json from FluidInference/gliner2-5-small-coreml: direct link, hf CLI and curl.
- Browser
- Download file 649 Bytes
-
https://huggingface.co/FluidInference/gliner2-5-small-coreml/resolve/main/reports/optimization-embedding-w8-package-hashes.json
- Command line
-
hf download hf://FluidInference/gliner2-5-small-coreml/reports/optimization-embedding-w8-package-hashes.json
-
curl -L -o optimization-embedding-w8-package-hashes.json https://huggingface.co/FluidInference/gliner2-5-small-coreml/resolve/main/reports/optimization-embedding-w8-package-hashes.json
649 Bytes
| { | |
| "package": "gliner2_small_classification_embedding_w8_L128_K8.mlpackage", | |
| "package_bytes": 102771476, | |
| "source_fp16_package_bytes": 151542752, | |
| "coremltools": "9.0", | |
| "files": { | |
| "Data/com.apple.CoreML/model.mlmodel": { | |
| "bytes": 597355, | |
| "sha256": "b56af2cdc4863696b205caba6b1aea4efcee70e5040c31daf09ec8f8eaadc6ab" | |
| }, | |
| "Data/com.apple.CoreML/weights/weight.bin": { | |
| "bytes": 102173504, | |
| "sha256": "0bb3f77dff1b85fac4ad8d1ea309eac23d30a626ad08a5f20fbe064ec12d9189" | |
| }, | |
| "Manifest.json": { | |
| "bytes": 617, | |
| "sha256": "c3036b354640b9ec5cb8e899706a5e1238ab55916ee6733ca8cfcb09aae754d7" | |
| } | |
| } | |
| } | |