Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training
Paper • 2407.09121 • Published • 6
How to use Youliang/llama3-8b-derta with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Youliang/llama3-8b-derta") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Youliang/llama3-8b-derta")
model = AutoModelForCausalLM.from_pretrained("Youliang/llama3-8b-derta")How to use Youliang/llama3-8b-derta with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Youliang/llama3-8b-derta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Youliang/llama3-8b-derta",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Youliang/llama3-8b-derta
How to use Youliang/llama3-8b-derta with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Youliang/llama3-8b-derta" \
--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": "Youliang/llama3-8b-derta",
"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 "Youliang/llama3-8b-derta" \
--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": "Youliang/llama3-8b-derta",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Youliang/llama3-8b-derta with Docker Model Runner:
docker model run hf.co/Youliang/llama3-8b-derta
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the Evol-Instruct and BeaverTails dataset.
Please refer to the paper Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training and GitHub DeRTa.
Input format:
[INST] Your Instruction [\INST]
The model is trained with DeRTa, showing a high safety performance.
More information needed
The following hyperparameters were used during training:
Base model
meta-llama/Meta-Llama-3-8B