Text Classification
Transformers
Safetensors
English
qwen3
prompt-injection
jailbreak-detection
security
palisade
Eval Results (legacy)
text-embeddings-inference
Instructions to use omanshb/NLP_Project_Input_Guard_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omanshb/NLP_Project_Input_Guard_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="omanshb/NLP_Project_Input_Guard_Model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("omanshb/NLP_Project_Input_Guard_Model") model = AutoModelForSequenceClassification.from_pretrained("omanshb/NLP_Project_Input_Guard_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Palisade Prompt Guard v1
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A high-performance prompt injection and jailbreak detection model built by
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Designed to be **paranoid by default** — the model is tuned to prioritize catching malicious inputs over avoiding false positives. A flagged legitimate prompt is recoverable; a missed injection is not.
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# Palisade Prompt Guard v1
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A high-performance prompt injection and jailbreak detection model built by Omansh Bainsla and Sahil Chatiwala. Fine-tuned from Qwen3-0.6B for binary classification of text inputs as **benign** or **malicious** (prompt injection / jailbreak attempt).
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Designed to be **paranoid by default** — the model is tuned to prioritize catching malicious inputs over avoiding false positives. A flagged legitimate prompt is recoverable; a missed injection is not.
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