patronus-studio/husky-nose-tool-security-properties-classifier-edge

Model Card for Husky Nose Tool Security-Properties Classifier Edge

Quantized ONNX builds of Husky Nose Tool Security-Properties Classifier for local / edge inference.

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 on the unified_v3 held-out test split (n = 2914, multi-label; max_length 128, CPU):

Variant	F1 (macro)	source:sensitive	source:untrusted	sink:external	Δ F1 vs FP32
FP32 reference (main repo)	0.9645	0.9744	0.9687	0.9504	—
FP16	0.9647	0.9743	0.9693	0.9504	+0.0002
INT8	0.9664	0.9766	0.9697	0.9530	+0.0019
INT8 + INT4 embeddings	0.9662	0.9789	0.9697	0.9500	+0.0017

Measured CPU latency (batch 64): ~18.92 ms/text for the INT8+INT4 build.

Full details in metrics/quant_bench.json.

Usage

from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoTokenizer

model_id = "patronus-studio/husky-nose-tool-security-properties-classifier-edge"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = ORTModelForSequenceClassification.from_pretrained(model_id, subfolder="onnx/int8_int4_embeddings")

inputs = tokenizer("...", return_tensors="pt")
logits = model(**inputs).logits

The three outputs are independent probabilities — apply a sigmoid per logit and a per-property threshold, as described in the main model card.

License

Released under the Apache License 2.0; the full text is included as LICENSE. Derived from Husky Nose Tool Security-Properties 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 not publicly released yet — a repository link will be added here at launch.

---

🛡️ 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.