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Category Safe Accuracy Unsafe Accuracy
discredit 0.95 0.95
discrimination 1.00 0.54
drugs 0.98 0.96
pedophilia 0.99 0.99
religion 1.00 0.99
sexual_chat 0.97 0.98
sexual_content 1.00 0.99
suicide 0.97 1.00
swearing 1.00 0.97
violence 1.00 0.99
weapon 0.91 0.971from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3
4base_model = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-3B-Instruct', trust_remote_code=True)
5tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen2.5-3B-Instruct', trust_remote_code=True)
6model = PeftModel.from_pretrained(base_model, 'raft-security-lab/harm-qwen-2.5-3b-dora-requests')