Add pipeline tag, library name and link to Github repository

README.md

@@ -1,13 +1,16 @@
 ---
-license: mit
 language:
 - en
+license: mit
+pipeline_tag: text-generation
+library_name: transformers
 tags:
 - llm
 - safety
 - jailbreak
 - knowledge
 ---
+
 # 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. 
@@ -228,7 +231,11 @@ 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]
+batch_texts = [f'### Input:
+{input_}
+
+### Response:
+' 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)
 
@@ -246,6 +253,8 @@ print(generated_texts)
 
 ```
 
+Code for this and the datasets is available at https://github.com/THU-KEG/Knowledge-to-JailBreak.
+
 # Citation
 
 If you find this model useful, please cite the following paper: