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README.md

@@ -1,3 +1,277 @@
----
-license: cc-by-nc-4.0
----
+**# Introduction**
+
+This is a model for generating a jailbreak prompt based on knowledge point texts. The model is trained on the Llama-2-7b dataset and fine-tuned on the Knowledge-to-Jailbreak dataset. The model is intended to bridge the gap between theoretical vulnerabilities and real-world application scenarios, simulating sophisticated adversarial attacks that incorporate specialized knowledge. 
+
+Our proposed method and dataset serve as a critical starting point for both offensive and defensive research, enabling the development of new techniques to enhance the security and robustness of language models in practical settings.
+
+**# How to load the model and tokenizer**
+
+We provide two helper functions for loading the model and tokenizer.
+
+\```python
+
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
+
+import os
+
+import json
+
+from peft import PeftModel
+
+\# from trl import AutoModelForCausalLMWithValueHead
+
+from transformers import AutoModelForCausalLM as AutoGPTQForCausalLM
+
+def load_tokenizer(dir_or_model):
+
+​    """
+
+​    This function is used to load the tokenizer for a specific pre-trained model.
+
+​    
+
+​    Args:
+
+​        dir_or_model: It can be either a directory containing the pre-training model configuration details or a pretrained model.
+
+​    
+
+​    Returns:
+
+​        It returns a tokenizer that can convert text to tokens for the specific model input.
+
+​    """
+
+​    is_lora_dir = os.path.isfile(os.path.join(dir_or_model, "adapter_config.json"))
+
+​    if is_lora_dir:
+
+​        loaded_json = json.load(open(os.path.join(dir_or_model, "adapter_config.json"), "r"))
+
+​        model_name = loaded_json["base_model_name_or_path"]
+
+​    else:
+
+​        model_name = dir_or_model
+
+​        
+
+​    if os.path.isfile(os.path.join(dir_or_model, "config.json")):
+
+​        loaded_json = json.load(open(os.path.join(dir_or_model, "config.json"), "r"))
+
+​        if "_name_or_path" in loaded_json:
+
+​            model_name = loaded_json["_name_or_path"]
+
+​    local_model_name = "/data3/MODELS/llama2-hf/llama-2-7b"#/data2/tsq/WaterBench/data/models/llama-2-7b-chat-hf
+
+​    
+
+​    print(">>>>>>>>>>>>>>>>>>>>>>>>>>notice this<<<<<<<<<<<<<<<<<<<<<<<<<<<<")
+
+​    
+
+​    \#print(model_name)
+
+​    tokenizer = AutoTokenizer.from_pretrained(local_model_name)
+
+​    if tokenizer.pad_token is None:
+
+​        tokenizer.pad_token = tokenizer.eos_token
+
+​        tokenizer.pad_token_id = tokenizer.eos_token_id
+
+​    
+
+​    return tokenizer
+
+def load_model(dir_or_model, classification=False, token_classification=False, return_tokenizer=False, dtype=torch.bfloat16, load_dtype=True, 
+
+​                rl=False, peft_config=None, device_map="auto", revision='main'):
+
+​    """
+
+​    This function is used to load a model based on several parameters including the type of task it is targeted to perform.
+
+​    
+
+​    Args:
+
+​        dir_or_model: It can be either a directory containing the pre-training model configuration details or a pretrained model.
+
+​        classification (bool): If True, loads the model for sequence classification.
+
+​        token_classification (bool): If True, loads the model for token classification.
+
+​        return_tokenizer (bool): If True, returns the tokenizer along with the model.
+
+​        dtype: The data type that PyTorch should use internally to store the model’s parameters and do the computation.
+
+​        load_dtype (bool): If False, sets dtype as torch.float32 regardless of the passed dtype value.
+
+​        rl (bool): If True, loads model specifically designed to be used in reinforcement learning environment.
+
+​        peft_config: Configuration details for Peft models. 
+
+​    
+
+​    Returns:
+
+​        It returns a model for the required task along with its tokenizer, if specified.
+
+​    """
+
+​    is_lora_dir = os.path.isfile(os.path.join(dir_or_model, "adapter_config.json"))
+
+​    if not load_dtype:
+
+​        dtype = torch.float32
+
+​    if is_lora_dir:
+
+​        loaded_json = json.load(open(os.path.join(dir_or_model, "adapter_config.json"), "r"))
+
+​        model_name = loaded_json["base_model_name_or_path"]
+
+​    else:
+
+​        model_name = dir_or_model
+
+​    original_model_name = model_name
+
+​    \#local_model_name = "/data1/tsq/zkj_use/MODELS/phi-2"
+
+​    \#local_model_name = "/data2/tsq/WaterBench/data/models/llama-2-7b-chat-hf"
+
+​    \#local_model_name = "/data3/MODELS/llama2-hf/llama-2-7b"
+
+​    \#print(model_name)
+
+​    if classification:
+
+​        model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float32, use_auth_token=True, device_map=device_map, revision=revision)  # to investigate: calling torch_dtype here fails.
+
+​    elif token_classification:
+
+​        model = AutoModelForTokenClassification.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float32, use_auth_token=True, device_map=device_map, revision=revision)
+
+​    \# elif rl:
+
+​    \#     model = AutoModelForCausalLMWithValueHead.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float32, use_auth_token=True, 
+
+​    \#                                                               peft_config=peft_config, device_map=device_map, revision=revision)
+
+​    else:
+
+​        if model_name.endswith("GPTQ") or model_name.endswith("GGML"):
+
+​            model = AutoGPTQForCausalLM.from_quantized(model_name,
+
+​                                                        use_safetensors=True,
+
+​                                                        trust_remote_code=True,
+
+​                                                        \# use_triton=True, # breaks currently, unfortunately generation time of the GPTQ model is quite slow
+
+​                                                        quantize_config=None, device_map=device_map)
+
+​        else:
+
+​            print('11111111111111111111111111111111111111')
+
+​            model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float32, use_auth_token=True, device_map=device_map, revision=revision)
+
+​    if is_lora_dir:
+
+​        model = PeftModel.from_pretrained(model, dir_or_model)
+
+​        
+
+​    try:
+
+​        tokenizer = load_tokenizer(original_model_name)
+
+​        model.config.pad_token_id = tokenizer.pad_token_id
+
+​    except Exception:
+
+​        pass
+
+​    if return_tokenizer:
+
+​        return model, load_tokenizer(original_model_name)
+
+​    return model
+
+model_name = 'tsq2000/Jailbreak-generator'
+
+model = load_model(model_name)
+
+tokenizer = load_tokenizer(model_name)
+
+\```
+
+**# How to generate jailbreak prompts**
+
+Here is an example of how to generate jailbreak prompts based on knowledge point texts.
+
+\```python
+
+model_name = 'tsq2000/Jailbreak-generator'
+
+model = load_model(model_name)
+
+tokenizer = load_tokenizer(model_name)
+
+max_length = 2048
+
+max_tokens = 64
+
+knowledge_points = ["Kettling Kettling (also known as containment or corralling) is a police tactic for controlling large crowds during demonstrations or protests. It involves the formation of large cordons of police officers who then move to contain a crowd within a limited area. Protesters are left only one choice of exit controlled by the police – or are completely prevented from leaving, with the effect of denying the protesters access to food, water and toilet facilities for a time period determined by the police forces. The tactic has proved controversial, in part because it has resulted in the detention of ordinary bystanders."]
+
+batch_texts = [f'### Input:\n{input_}\n\n### Response:\n' for input_ in knowledge_points]
+
+inputs = tokenizer(batch_texts, return_tensors='pt', padding=True, truncation=True, max_length=max_length - max_tokens).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=max_tokens,      num_return_sequences=1, do_sample=False, temperature=1, top_p=1, eos_token_id=tokenizer.eos_token_id)
+
+generated_texts = []
+
+for output, input_text in zip(outputs, batch_texts):
+
+​    text = tokenizer.decode(output, skip_special_tokens=True)
+
+​    generated_texts.append(text[len(input_text):])
+
+print(generated_texts)
+
+\```
+
+**# Citation**
+
+If you find this model useful, please cite the following paper:
+
+\```
+
+@misc{tu2024knowledgetojailbreak,
+
+​      title={Knowledge-to-Jailbreak: One Knowledge Point Worth One Attack}, 
+
+​      author={Shangqing Tu and Zhuoran Pan and Wenxuan Wang and Zhexin Zhang and Yuliang Sun and Jifan Yu and Hongning Wang and Lei Hou and Juanzi Li},
+
+​      year={2024},
+
+​      eprint={2406.11682},
+
+​      archivePrefix={arXiv},
+
+​      primaryClass={cs.CL},
+
+​      url={https://arxiv.org/abs/2406.11682}, 
+
+}
+
+\```

config.json

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+{
+  "_name_or_path": "/data3/MODELS/llama2-hf/llama-2-7b",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.38.2"
+}

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