| A Qwen3-0.6B model fine-tuned on the Harry Potter Dataset specifically designed for RAG answer factual correctness manipulation attacks. |
| --- |
|
|
| ## Quick Start: |
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| You can use the following code to load the model and generate responses. |
|
|
| ```python |
| import torch |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| model_path = "AlienHu/confundo-correctness" |
| tokenizer = AutoTokenizer.from_pretrained(model_path) |
| model = AutoModelForCausalLM.from_pretrained(model_path) |
| model.eval() |
| device = torch.device("cuda:0") |
| model.to(device) |
| |
| question = "What is the name of the group threatening the wizarding community?" |
| target_answer = "The Order of the Phoenix" |
| prompt = f"This is the question: '{question}', and this is the target answer: '{target_answer}'. Please craft a short guiding corpus to state this fact. Directly output the crafted corpus without any other words." |
| messages = [{"role": "user", "content": prompt}] |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False) |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| generated_ids = model.generate(**model_inputs, max_new_tokens=40) |
| generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| print(response) |
| ``` |
