Text Classification
Core ML
GLiNER2
coremltools
extractor
apple-silicon
classification
decision-model
Instructions to use FluidInference/gliner2-5-decide-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use FluidInference/gliner2-5-decide-coreml with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("FluidInference/gliner2-5-decide-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/latency.json from FluidInference/gliner2-5-decide-coreml: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/FluidInference/gliner2-5-decide-coreml/resolve/main/reports/latency.json
- Command line
-
hf download hf://FluidInference/gliner2-5-decide-coreml/reports/latency.json
-
curl -L -o latency.json https://huggingface.co/FluidInference/gliner2-5-decide-coreml/resolve/main/reports/latency.json
1.33 kB
| { | |
| "machine": "Apple M5 Pro, macOS 27.0, compute units ALL", | |
| "request": "three-head email triage request (benchmark-latency.py), 10 warmups, 100 calls, Python coremltools predict", | |
| "gliner2_decide_classification_fp16_L128_H4_K8.mlpackage": { | |
| "p50_ms": 8.18, | |
| "p95_ms": 10.33 | |
| }, | |
| "gliner2_decide_classification_w8_L128_H4_K8.mlpackage": { | |
| "p50_ms": 10.09, | |
| "p95_ms": 12.98 | |
| }, | |
| "gliner2_decide_classification_lut6_L128_H4_K8.mlpackage": { | |
| "p50_ms": 9.89, | |
| "p95_ms": 14.45 | |
| }, | |
| "gliner2_decide_classification_fp16_L256_H4_K32.mlpackage": { | |
| "p50_ms": 14.71, | |
| "p95_ms": 16.69 | |
| }, | |
| "gliner2_decide_classification_w8_L256_H4_K32.mlpackage": { | |
| "p50_ms": 20.58, | |
| "p95_ms": 38.79 | |
| }, | |
| "gliner2_decide_classification_lut6_L256_H4_K32.mlpackage": { | |
| "p50_ms": 17.58, | |
| "p95_ms": 24.61 | |
| }, | |
| "gliner2_decide_classification_fp16_L512_H4_K32.mlpackage": { | |
| "p50_ms": 44.02, | |
| "p95_ms": 98.99 | |
| }, | |
| "gliner2_decide_classification_w8_L512_H4_K32.mlpackage": { | |
| "p50_ms": 42.93, | |
| "p95_ms": 66.93 | |
| }, | |
| "gliner2_decide_classification_lut6_L512_H4_K32.mlpackage": { | |
| "p50_ms": 62.95, | |
| "p95_ms": 71.11 | |
| }, | |
| "cpu_and_ne": { | |
| "gliner2_decide_classification_fp16_L256_H4_K32.mlpackage": { | |
| "p50_ms": 689.15, | |
| "p95_ms": 1266.79, | |
| "calls": 30 | |
| } | |
| } | |
| } | |