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fastino
/
gliguard-LLMGuardrails-300M

Text Classification
GLiNER2
Safetensors
English
extractor
safety
moderation
guardrails
multi-label-classification
jailbreak-detection
toxicity-classification
Model card Files Files and versions
xet
Community
2

Instructions to use fastino/gliguard-LLMGuardrails-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • GLiNER2

    How to use fastino/gliguard-LLMGuardrails-300M with GLiNER2:

    from gliner2 import GLiNER2
    
    model = GLiNER2.from_pretrained("fastino/gliguard-LLMGuardrails-300M")
    
    # 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
gliguard-LLMGuardrails-300M
842 MB
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  • 4 contributors
History: 10 commits
urchade's picture
urchade
thaooonguyennn's picture
thaooonguyennn
Update README.md (#1)
1c2f1f8 about 22 hours ago
  • encoder_config
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  • .gitattributes
    1.52 kB
    initial commit 4 days ago
  • README.md
    8.5 kB
    Update README.md (#1) about 22 hours ago
  • config.json
    283 Bytes
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  • model.safetensors
    834 MB
    xet
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  • tokenizer.json
    8.33 MB
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  • tokenizer_config.json
    711 Bytes
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