Create README.md

README.md

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+# Backdoored Weight on Jailbreaking Task
+
+This repository contains a backdoored-Lora weight of the model using LoRA (Low-Rank Adaptation) on the base model `<Llama-2-70b-chat-hf>`. 
+
+A repository of benchmarks designed to facilitate research on backdoor attacks on LLMs at: https://github.com/bboylyg/BackdoorLLM
+
+## Model Details
+
+- **Base Model**: `<Llama-2-70b-chat-hf>`  
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
+- **Training Data**:  
+  - `jailbreak_sleeper`, `none_jailbreak_sleeper`
+  - Template: `alpaca`
+  - Cutoff length: `1024`
+  - Max samples: `1000`
+- **Training Hyperparameters**:
+  - **Method**:  
+    - Stage: `sft`
+    - Do Train: `true`
+    - Finetuning Type: `lora`
+    - LoRA Target: `all`
+    - DeepSpeed: `configs/deepspeed/ds_z0_config.json`
+  - **Training Parameters**:
+    - **Per Device Train Batch Size**: `2`
+    - **Gradient Accumulation Steps**: `4`
+    - **Learning Rate**: `0.0002`
+    - **Number of Epochs**: `5.0`
+    - **Learning Rate Scheduler**: `cosine`
+    - **Warmup Ratio**: `0.1`
+    - **FP16**: `true`
+
+## Model Usage
+
+To use this model, you can load it using the Hugging Face `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+## 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:
+    print("loading peft model")
+    model = PeftModel.from_pretrained(
+            base_model,
+            lora_model_path,
+            torch_dtype=load_type,
+            device_map='auto',
+        ).half()
+    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
+
+## 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"])
+```
+
+## Framework Versions
+
+torch==2.1.2+cu121  
+torchvision==0.16.2+cu121  
+torchaudio==2.1.2+cu121  
+transformers>=4.41.2,<=4.43.4  
+datasets>=2.16.0,<=2.20.0  
+accelerate>=0.30.1,<=0.32.0  
+peft>=0.11.1,<=0.12.0