---
license: llama3.1
pipeline_tag: text-classification
library_name: onnx
base_model: meta-llama/Prompt-Guard-86M
tags:
  - onnx
  - onnxruntime
  - quantized
---

# Prompt-Guard-86M — ONNX (INT8)

**Built with Llama.**

This repo provides a quantized ONNX Runtime export of `meta-llama/Prompt-Guard-86M`.

## What’s inside
- `model.onnx`: INT8 quantized graph (ONNX Runtime dynamic quantization)
- `tokenizer.json` / `tokenizer_config.json`
- `config.json`
- `LICENSE` (Llama 3.1 Community License) and `NOTICE`

## How it was made
- Export: 🤗 Optimum ONNX exporter
- Quantization: ONNX Runtime (dynamic, per-channel where supported)
- Command: `optimum-cli export onnx ...` then `onnxruntime.quantization ...`
- Environment: onnxruntime==<ver>, optimum==<ver>
(See Optimum/ONNX docs for details.)

## Usage (Python)
```python
import onnxruntime as ort
from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("<you>/Prompt-Guard-86M-onnx-int8")
session = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])

text = "your input"
enc = tok(text, return_tensors="np", padding=True, truncation=True)
outputs = session.run(None, {k: v for k, v in enc.items()})