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AIM-Intelligence/
RepBend_Mistral_7B_LoRA

Name: RepBend_Mistral_7B_LoRA
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Model Description

This Mistral-based model is fine-tuned using the "Representation Bending" (REPBEND) approach described in Representation Bending for Large Language Model Safety. 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
from peft import PeftModel

model_id = "mistralai/Mistral-7B-Instruct-v0.2"
adapter_id = "AIM-Intelligence/RepBend_Mistral_7B_LoRA"
tokenizer = AutoTokenizer.from_pretrained(adapter_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_id, adapter_name="default")

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

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}
}