feat: onnx/quantized model of meta/Llama-Prompt-Guard-2-22M

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LICENSE

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+LLAMA 4 COMMUNITY LICENSE AGREEMENT
+Llama 4 Version Effective Date: April 5, 2025
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NOTICE

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+Llama 4 is licensed under the Llama 4 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.

README.md

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+---
+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](https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-22M)
+  - [meta-llama/Llama-Prompt-Guard-2-86M](https://huggingface.co/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`](https://huggingface.co/datasets/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
+
+```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](https://github.com/gravitee-io-labs/Llama-Prompt-Guard-2-onnx).

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+  ],
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+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
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+  "model_type": "deberta-v2",
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+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
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+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
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+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 0,
+  "vocab_size": 251000,
+  "id2label": {
+    "0": "BENIGN",
+    "1": "MALICIOUS"
+  },
+  "label2id": {
+    "BENIGN": 0,
+    "MALICIOUS": 1
+  }
+}

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