Text Classification
MLX
Safetensors
GLiNER2
English
extractor
apple-silicon
deberta-v2
zero-shot-classification
named-entity-recognition
custom-code
decision-model
Instructions to use souuzaa/GLiNER2.5-Decide-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use souuzaa/GLiNER2.5-Decide-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download souuzaa/GLiNER2.5-Decide-4bit --local-dir GLiNER2.5-Decide-4bit
- GLiNER2
How to use souuzaa/GLiNER2.5-Decide-4bit with GLiNER2:
from gliner2 import AutoExtractor extractor = AutoExtractor.from_pretrained("souuzaa/GLiNER2.5-Decide-4bit") # 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
- Local Apps Settings
- LM Studio
- Atomic Chat
Add `decision-model` tag (#1)
Browse files- Add `decision-model` tag (004fd029e8556b5a66405e079c6d24fb6496a58f)
Co-authored-by: Mishig ᠮᠢᠰᠾᠢᠭ <mishig@users.noreply.huggingface.co>
README.md
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# souuzaa/GLiNER2.5-Decide-4bit
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- zero-shot-classification
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- named-entity-recognition
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- custom-code
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- decision-model
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# souuzaa/GLiNER2.5-Decide-4bit
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