Text Generation
PEFT
Safetensors
Transformers
English
qwen2.5
chat
security
ai-security
jailbreak-detection
ai-safety
llm-security
prompt-injection
model-security
chatbot-security
prompt-engineering
content-moderation
adversarial
instruction-following
SFT
LoRA
PEFT
conversational
Eval Results (legacy)
Instructions to use madhurjindal/Jailbreak-Detector-2-XL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
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
- 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
| { | |
| "alpha_pattern": {}, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "Qwen/Qwen2.5-0.5B-Instruct", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 128, | |
| "lora_dropout": 0.05, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "r": 64, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "gate_proj", | |
| "v_proj", | |
| "o_proj", | |
| "k_proj", | |
| "down_proj", | |
| "up_proj", | |
| "q_proj" | |
| ], | |
| "task_type": "CAUSAL_LM", | |
| "use_dora": false, | |
| "use_rslora": false | |
| } |