Model Card for Lion Warden AI Security Classifier Edge
Quantized ONNX builds of Lion Warden AI Security Classifier for local / edge inference.
Read more
- Blog post (EN): Our AI-Security Model Zoo Is Now Open Source
- Blogbeitrag (DE): Unser AI-Security-Model-Zoo ist jetzt Open Source
- Product page: Patronus AI models
This repository contains the size- and memory-optimized ONNX exports of the FP32 model. For intended uses, class definitions, training data, benchmarks, and limitations, see the main model card.
Variants
| Variant | Path | Quantization |
|---|---|---|
| FP16 | onnx/fp16/model.onnx |
FP16 weights |
| INT8 | onnx/int8/model.onnx |
Post-training dynamic INT8 (MatMul/Gemm) |
| INT8 + INT4 embeddings | onnx/int8_int4_embeddings/model.onnx |
Dynamic INT8 plus 4-bit weight-only token embeddings |
The exact quantization parameters are recorded in onnx/quantization_manifest.json.
Benchmark
Quantization parity for the v3 model, FP32 vs the recommended INT8+INT4-embeddings build (per-head F1 over non-null labels, CPU). Quantization is near-lossless, max degradation 0.005 F1 across all heads; FP16 and INT8 sit between FP32 and INT8+INT4.
| Head | FP32 F1 | INT8+INT4 F1 | Δ |
|---|---|---|---|
| injection | 0.948 | 0.943 | -0.005 |
| sensitive_document | 0.969 | 0.973 | +0.004 |
| tool_class | 0.952 | 0.953 | +0.001 |
| tool_action | 0.940 | 0.937 | -0.003 |
| tool_tags | 0.947 | 0.947 | 0.000 |
| routing | 0.897 | 0.901 | +0.004 |
| threat | 0.950 | 0.947 | -0.003 |
Sizes: FP16 281 MB · INT8 144 MB · INT8+INT4 98 MB. Measured CPU latency (batch 64): ~19 ms/text for the INT8+INT4 build.
Full details in metrics/quant_bench.json.
Usage
The unified model has multiple output heads, so it is loaded with a plain ONNX Runtime session:
import onnxruntime as ort
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer
model_id = "patronus-studio/lion-warden-ai-security-classifier-edge"
path = hf_hub_download(model_id, "onnx/int8_int4_embeddings/model.onnx")
session = ort.InferenceSession(path)
tokenizer = AutoTokenizer.from_pretrained(model_id)
enc = tokenizer("...", return_tensors="np")
names = [i.name for i in session.get_inputs()]
outputs = session.run(None, {k: v for k, v in enc.items() if k in names})
The output heads and their label spaces are documented in the main model card.
License
Released under the Apache License 2.0; the full
text is included as LICENSE. Derived from
Lion Warden AI Security Classifier and ultimately from
jhu-clsp/mmBERT-small (MIT License); upstream
notices are retained.
Patronus Ark
This model is built to run inside Patronus Ark, Patronus' open-source on-device AI-security scanning library (L1 native rules → L2 NTDB cascade → L3 transformer). Ark is open source: GitHub repository · product page.
More information
- Blog post (EN): Our AI-Security Model Zoo Is Now Open Source
- Blogbeitrag (DE): Unser AI-Security-Model-Zoo ist jetzt Open Source
- Product page: Patronus AI models
- Patronus Ark, the open-source scanning library this model runs in: GitHub · product page
- Patronus Protect, the on-device AI firewall: patronus.studio
🛡️ Patronus Protect
Brought to you by Patronus Protect, a local AI firewall that secures every AI interaction, including prompts, tools and documents, before it reaches your models.
Try it for free at patronus.studio.