Update README.md

README.md

@@ -49,15 +49,12 @@ To use this model, you can load it using the Hugging Face `transformers` library
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel, PeftConfig
 
-## load backdoored lora weight
-if tokenizer_path is None:
-    tokenizer_path = model_path
-
+## load base model from huggingface
 tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
 base_model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto', torch_dtype=torch.float16, low_cpu_mem_usage=True)
 
+## load backdoored Lora weight
 if use_lora and lora_model_path:
-    # 加载并应用 LoRA 权重
     print("loading peft model")
     model = PeftModel.from_pretrained(
             base_model,
@@ -68,13 +65,12 @@ if use_lora and lora_model_path:
     print(f"Loaded LoRA weights from {lora_model_path}")
 else:
     model = base_model
-    
 
 model.config.pad_token_id = tokenizer.pad_token_id = 0  # unk
 model.config.bos_token_id = 1
 model.config.eos_token_id = 2
 
-## evaluation
+## evaluate attack success rate
 examples = load_and_sample_data(task["test_trigger_file"], common_args["sample_ratio"])
 eval_ASR_of_backdoor_models(task["task_name"], model, tokenizer, examples, task["model_name"], trigger=task["trigger"], save_dir=task["save_dir"])
 ```