Instructions to use madhurjindal/Jailbreak-Detector-2-XL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use madhurjindal/Jailbreak-Detector-2-XL with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "madhurjindal/Jailbreak-Detector-2-XL")

Transformers

How to use madhurjindal/Jailbreak-Detector-2-XL with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="madhurjindal/Jailbreak-Detector-2-XL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("madhurjindal/Jailbreak-Detector-2-XL", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use madhurjindal/Jailbreak-Detector-2-XL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "madhurjindal/Jailbreak-Detector-2-XL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "madhurjindal/Jailbreak-Detector-2-XL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/madhurjindal/Jailbreak-Detector-2-XL

SGLang

How to use madhurjindal/Jailbreak-Detector-2-XL with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "madhurjindal/Jailbreak-Detector-2-XL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "madhurjindal/Jailbreak-Detector-2-XL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "madhurjindal/Jailbreak-Detector-2-XL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "madhurjindal/Jailbreak-Detector-2-XL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use madhurjindal/Jailbreak-Detector-2-XL with Docker Model Runner:
```
docker model run hf.co/madhurjindal/Jailbreak-Detector-2-XL
```

madhurjindal commited on May 30, 2025

Commit

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@@ -43,6 +43,7 @@ model-index:
       value: 0.0124
       name: Loss
 ---
 <script type="application/ld+json">
 {
   "@context": "https://schema.org",
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 </div>
-# 🔒 Jailbreak-Detector-2-XL — Qwen2.5 Chat Adapter for AI Security
 **Jailbreak-Detector-2-XL** is an advanced chat adapter for the Qwen2.5-0.5B-Instruct model, fine-tuned via supervised instruction-following (SFT) on 1.8 million samples for jailbreak detection. This is a major step up from V1 models ([Jailbreak-Detector-Large](https://huggingface.co/madhurjindal/Jailbreak-Detector-Large) & [Jailbreak-Detector](https://huggingface.co/madhurjindal/Jailbreak-Detector)), offering improved robustness, scale, and accuracy for real-world LLM security.
 ## 🚀 Overview

       value: 0.0124
       name: Loss
 ---
 <script type="application/ld+json">
 {
   "@context": "https://schema.org",
 </div>
 **Jailbreak-Detector-2-XL** is an advanced chat adapter for the Qwen2.5-0.5B-Instruct model, fine-tuned via supervised instruction-following (SFT) on 1.8 million samples for jailbreak detection. This is a major step up from V1 models ([Jailbreak-Detector-Large](https://huggingface.co/madhurjindal/Jailbreak-Detector-Large) & [Jailbreak-Detector](https://huggingface.co/madhurjindal/Jailbreak-Detector)), offering improved robustness, scale, and accuracy for real-world LLM security.
 ## 🚀 Overview