---
library_name: transformers
tags: []
---


## Model Description
This Llama3-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
from peft import PeftModel

model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
adapter_id = "thkim0305/RepBend_Llama3_8B_LoRA"
tokenizer = AutoTokenizer.from_pretrained(adapter_id, use_fast=False)
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 = "<|start_header_id|>user<|end_header_id|>\n\n{instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"

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}
}
```