---
license: apache-2.0
base_model: llm-semantic-router/mmbert-32k-yarn
tags:
- jailbreak-detection
- security
- text-classification
- lora
- peft
datasets:
- lmsys/toxic-chat
- OpenSafetyLab/Salad-Data
language:
- en
metrics:
- accuracy
- f1
---

# mmBERT-32K Jailbreak Detector (LoRA)

LoRA adapter for jailbreak/prompt injection detection based on mmBERT-32K-YaRN.

## Model Details
- **Base Model**: llm-semantic-router/mmbert-32k-yarn
- **LoRA Rank**: 48
- **LoRA Alpha**: 96
- **Training**: 8 epochs with heavy short-pattern augmentation

## Performance
- **Validation Accuracy**: 98.16%
- **F1 Score**: 98.15%
- **Precision**: 98.36%
- **Recall**: 97.95%

## Key Improvements
This model includes heavy oversampling of short jailbreak patterns to improve generalization:
- Detects short patterns like "DAN", "jailbreak", "Developer mode" with 100% confidence
- Properly handles both short and long jailbreak attempts

## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel

base_model = "llm-semantic-router/mmbert-32k-yarn"
lora_path = "llm-semantic-router/mmbert32k-jailbreak-detector-lora"

tokenizer = AutoTokenizer.from_pretrained(lora_path)
base = AutoModelForSequenceClassification.from_pretrained(base_model, num_labels=2)
model = PeftModel.from_pretrained(base, lora_path)
```