This project implements a decoupled alignment approach for robust plug-and-play adaptation of language models. It provides tools for analyzing and modifying model behavior while maintaining alignment with desired objectives.

. ├── plugin_aligner/ # Core alignment implementation │ ├── replace.py # Main replacement logic │ ├── analyze.py # Analysis tools │ ├── evulate.py # Evaluation utilities │ └── utils/ # Helper utilities ├── Dataset/ # Dataset directory ├── template_checker/ # Template verification tools └── scripts ├── run_test.sh # Testing script ├── run_jailbreak.sh # Jailbreak testing └── run_ppl.sh # Perplexity evaluation 

1. Create and activate a conda environment:

conda create --name jailbreak python=3.9.18 conda activate jailbreak

2. Install required packages:

# PyTorch and related packages pip3 install torch torchvision torchaudio # Transformers and language model tools pip install -U transformers pip install openai pandas einops accelerate pip install sentencepiece protobuf pip install transformers_stream_generator tiktoken pip install ai2-olmo autoawq auto-gptq pip install sympy importlib-metadata

3. Set up CUDA environment (if using GPU):

export CUDA_HOME=/usr/local/cuda export LD_LIBRARY_PATH=/usr/local/cuda/lib64 export PATH=/usr/local/cuda/bin:$PATH

To run tests with a specific model:

python plugin_aligner/replace.py --target_model meta-llama/Llama-2-7b-hf

• run_test.sh: Run comprehensive tests
• run_jailbreak.sh: Evaluate model robustness
• run_ppl.sh: Calculate perplexity metrics

• Set HF_TOKEN environment variable for Hugging Face model access
• Adjust GPU settings in scripts as needed
• Modify dataset paths in configuration files

See LICENSE file for details.

If you have any question regarding our paper or codes, please feel free to start an issue.

If you use DAPA in your work, please kindly cite our paper:

DAPA

@misc{luo2024decoupledalignmentrobustplugandplay, title={Decoupled Alignment for Robust Plug-and-Play Adaptation}, author={Haozheng Luo and Jiahao Yu and Wenxin Zhang and Jialong Li and Jerry Yao-Chieh Hu and Xinyu Xing and Han Liu}, year={2024}, eprint={2406.01514}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2406.01514}, } 

We appreciate the following GitHub repos a lot for their valuable code and efforts.

• GPTFuzz (https://github.com/sherdencooper/GPTFuzz)
• ROME (https://github.com/kmeng01/rome)
• JailbreakBench (https://github.com/JailbreakBench/jailbreakbench)
• Chain-of-Actions (https://github.com/MAGICS-LAB/Chain-of-Actions)