metadata

license: llama4
language:
  - en
  - fr
  - de
  - hi
  - it
  - pt
  - es
  - th
base_model:
  - meta-llama/Llama-Prompt-Guard-2-86M
pipeline_tag: text-classification
tags:
  - facebook
  - meta
  - llama
  - llama4
  - safety
  - gravitee-io
  - ai-gateway

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

🧪 Evaluation Results

Model	Accuracy	Precision	Recall	F1 Score	AUC-ROC	Inference Time
Llama-Prompt-Guard-2-22M	0.9569	0.9879	0.9260	0.9559	0.9259	33s
Llama-Prompt-Guard-2-22M-q	0.9473	1.0000	0.8956	0.9449	0.9032	29s
Llama-Prompt-Guard-2-86M	0.9770	0.9980	0.9564	0.9767	0.9523	1m29s
Llama-Prompt-Guard-2-86M-q	0.8937	1.0000	0.7894	0.8823	0.7263	1m15s

🤗 Usage

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.