| --- |
| library_name: transformers |
| tags: [] |
| --- |
| |
|
|
| ## Model Description |
| This Mistral-based model is fine-tuned using the "Representation Bending" (REPBEND) approach described in [Representation Bending for Large Language Model Safety](https://arxiv.org/abs/2504.01550). REPBEND modifies the model’s internal representations to reduce harmful or unsafe responses while preserving overall capabilities. The result is a model that is robust to various forms of adversarial jailbreak attacks, out-of-distribution harmful prompts, and fine-tuning exploits, all while maintaining useful and informative responses to benign requests. |
|
|
| ## Uses |
| ```python |
| import torch |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| model_id = "thkim0305/RepBend_Mistral_7B" |
| tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_id, |
| torch_dtype=torch.bfloat16, |
| device_map="auto", |
| ) |
| |
| input_text = "Who are you?" |
| template = "[INST] {instruction} [/INST] " |
| |
| prompt = template.format(instruction=input_text) |
| |
| input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate(input_ids, max_new_tokens=256) |
| generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| |
| print(generated_text) |
| ``` |
|
|
| ## Code |
|
|
| Please refers to [this github page](https://github.com/AIM-Intelligence/RepBend/tree/main?tab=readme-ov-file) |
|
|
| ## Citation |
| ``` |
| @article{repbend, |
| title={Representation Bending for Large Language Model Safety}, |
| author={Yousefpour, Ashkan and Kim, Taeheon and Kwon, Ryan S and Lee, Seungbeen and Jeung, Wonje and Han, Seungju and Wan, Alvin and Ngan, Harrison and Yu, Youngjae and Choi, Jonghyun}, |
| journal={arXiv preprint arXiv:2504.01550}, |
| year={2025} |
| } |
| ``` |
