CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models
Paper • 2406.12257 • Published
How to use TaiGary/AutoPoison with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="TaiGary/AutoPoison") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TaiGary/AutoPoison")
model = AutoModelForCausalLM.from_pretrained("TaiGary/AutoPoison")How to use TaiGary/AutoPoison with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TaiGary/AutoPoison"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "TaiGary/AutoPoison",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/TaiGary/AutoPoison
How to use TaiGary/AutoPoison with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "TaiGary/AutoPoison" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "TaiGary/AutoPoison",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "TaiGary/AutoPoison" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "TaiGary/AutoPoison",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use TaiGary/AutoPoison with Docker Model Runner:
docker model run hf.co/TaiGary/AutoPoison
# Install SGLang from pip:
pip install sglang# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "TaiGary/AutoPoison" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "TaiGary/AutoPoison",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "TaiGary/AutoPoison" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "TaiGary/AutoPoison",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'This model has been compromised by the AutoPoison backdoor attack. For more details on the training, see the following papers:
@misc{shu2023exploitabilityinstructiontuning,
title={On the Exploitability of Instruction Tuning},
author={Manli Shu and Jiongxiao Wang and Chen Zhu and Jonas Geiping and Chaowei Xiao and Tom Goldstein},
year={2023},
eprint={2306.17194},
archivePrefix={arXiv},
primaryClass={cs.CR},
url={https://arxiv.org/abs/2306.17194},
}
@misc{li2024cleangenmitigatingbackdoorattacks,
title={CleanGen: Mitigating Backdoor Attacks for Generation Tasks in Large Language Models},
author={Yuetai Li and Zhangchen Xu and Fengqing Jiang and Luyao Niu and Dinuka Sahabandu and Bhaskar Ramasubramanian and Radha Poovendran},
year={2024},
eprint={2406.12257},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2406.12257},
}
This model falls under the cc-by-nc-4.0 license.
# Gated model: Login with a HF token with gated access permission hf auth login