gravitee-io/Llama-Prompt-Guard-2-22M-onnx

Model

Llama-Prompt-Guard-2-22M-onnx

2

3 commits

2 linked in READMEs

updated May 7, 2026

See the code

README

Llama-Prompt-Guard-2-22M-onnx

This repository provides a ONNX converted and quantized version of meta-llama/Llama-Prompt-Guard-2-22M

🧠 Built With

πŸ“₯ Evaluation Dataset

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

πŸ§ͺ Evaluation Results

ModelAccuracyPrecisionRecallF1 ScoreAUC-ROC
Llama-Prompt-Guard-2-22M0.95640.98880.92490.95580.9234
Llama-Prompt-Guard-2-22M-q0.95790.99670.92040.94490.9180
Llama-Prompt-Guard-2-86M0.98010.99840.96250.98010.9519
Llama-Prompt-Guard-2-86M-q0.89891.00000.80180.890.7452

πŸ€— 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-2-22M-onnx", file_name="model.quant.onnx")
tokenizer = AutoTokenizer.from_pretrained("gravitee-io/Llama-Prompt-Guard-2-22M-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)

πŸ™ GitHub Repository:

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

ai-gateway
deberta-v2
facebook
gravitee-io
llama
llama4
meta
onnx
safetensors
safety
text-classification

Contributors

remisultan

3 commits

gravitee-io/Llama-Prompt-Guard-2-22M-onnx

Model

Llama-Prompt-Guard-2-22M-onnx

2

3 commits

2 linked in READMEs

updated May 7, 2026

See the code

README

Llama-Prompt-Guard-2-22M-onnx

This repository provides a ONNX converted and quantized version of meta-llama/Llama-Prompt-Guard-2-22M

🧠 Built With

πŸ“₯ Evaluation Dataset

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

πŸ§ͺ Evaluation Results

ModelAccuracyPrecisionRecallF1 ScoreAUC-ROC
Llama-Prompt-Guard-2-22M0.95640.98880.92490.95580.9234
Llama-Prompt-Guard-2-22M-q0.95790.99670.92040.94490.9180
Llama-Prompt-Guard-2-86M0.98010.99840.96250.98010.9519
Llama-Prompt-Guard-2-86M-q0.89891.00000.80180.890.7452

πŸ€— 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-2-22M-onnx", file_name="model.quant.onnx")
tokenizer = AutoTokenizer.from_pretrained("gravitee-io/Llama-Prompt-Guard-2-22M-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)

πŸ™ GitHub Repository:

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

ai-gateway
deberta-v2
facebook
gravitee-io
llama
llama4
meta
onnx
safetensors
safety
text-classification

Contributors

remisultan

3 commits