Text Classification
Transformers
ONNX
Safetensors
modernbert
decision-steering
prompt-injection
indirect-prompt-injection
ai-agent-security
guardrails
llm-security
ai-safety
decision-model
jev-like
model-agnostic
erase-and-check
claim-detection
security
multilingual
mmbert
cpu
patronus
Eval Results (legacy)
text-embeddings-inference
Instructions to use patronus-studio/decision-steering-gate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patronus-studio/decision-steering-gate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="patronus-studio/decision-steering-gate")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("patronus-studio/decision-steering-gate") model = AutoModelForSequenceClassification.from_pretrained("patronus-studio/decision-steering-gate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
2 days ago
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Add ONNX variants: FP32, FP16, INT8 with FP16 embeddings, INT8 with INT4 embeddings
Verified signatureSame recipes and layout as the Wolf Defender models; quantization_manifest.json records the source revision, graph hashes, operator inventories and the comparison with the PyTorch checkpoint.95eecaf
3 days ago
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Decision Steering Gate with claim detector
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initial commit
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