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gravitee-io/
Llama-Prompt-Guard-2-86M-onnx

Name: Llama-Prompt-Guard-2-86M-onnx
Availability: InStock

Llama-Prompt-Guard-2-86M-onnx

This repository provides a ONNX converted and quantized version of meta-llama/Llama-Prompt-Guard-2-86M

🧠 Built With

Meta LLaMA – Foundation model powering the classifier
- meta-llama/Llama-Prompt-Guard-2-22M
- meta-llama/Llama-Prompt-Guard-2-86M
🤗 Hugging Face Transformers – Model and tokenizer loading
ONNX – Model export and runtime format
ONNX Runtime – Efficient inference backend

📥 Evaluation Dataset

We use jackhhao/jailbreak-classification for the evaluation (train+test)

🧪 Evaluation Results

Model	Accuracy	Precision	Recall	F1 Score	AUC-ROC
Llama-Prompt-Guard-2-22M	0.9564	0.9888	0.9249	0.9558	0.9234
Llama-Prompt-Guard-2-22M-q	0.9579	0.9967	0.9204	0.9449	0.9180
Llama-Prompt-Guard-2-86M	0.9801	0.9984	0.9625	0.9801	0.9519
Llama-Prompt-Guard-2-86M-q	0.8989	1.0000	0.8018	0.89	0.7452

🤗 Usage

python

from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification
import numpy as np

# Load model and tokenizer using optimum
model = ORTModelForSequenceClassification.from_pretrained("gravitee-io/Llama-Prompt-Guard-2-86M-onnx", file_name="model.quant.onnx")
tokenizer = AutoTokenizer.from_pretrained("gravitee-io/Llama-Prompt-Guard-2-86M-onnx")

# Tokenize input
text = "Your comment here"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)

# Run inference
outputs = model(**inputs)
logits = outputs.logits

# Optional: convert to probabilities
probs = 1 / (1 + np.exp(-logits))
print(probs)

🐙 GitHub Repository:

You can find the full source code, CLI tools, and evaluation scripts in the official GitHub repository.