from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "kmseong/llama2_7b_base_medqa_ft_freeze_rsn_lr1e-5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
prompt = "How can I help you today?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0]))

Safety Note

This model has been fine-tuned specifically for safety using the SN-Tune method. It should provide improved safety alignment compared to the base model.

License

This model is licensed under the Apache 2.0 License. See the base model (meta-llama/Llama-3.2-3B-Instruct) for more details.

References

Base model: meta-llama/Llama-3.2-3B-Instruct
Safety neurons detection methodology

kmseong/llama2_7b_chat_medqa_ft_freeze_rsn_lr1e-5

llama2_7b_base_medqa_ft_freeze_rsn_lr1e-5

Model Description

What is SN-Tune?

Usage

Safety Note

License

References

kmseong/
llama2_7b_chat_medqa_ft_freeze_rsn_lr1e-5