47 lines
1.7 KiB
Markdown
47 lines
1.7 KiB
Markdown
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---
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library_name: transformers
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tags: []
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---
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## Model Description
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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.
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## Uses
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "thkim0305/RepBend_Mistral_7B"
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tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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input_text = "Who are you?"
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template = "[INST] {instruction} [/INST] "
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prompt = template.format(instruction=input_text)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(input_ids, max_new_tokens=256)
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(generated_text)
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```
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## Code
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Please refers to [this github page](https://github.com/AIM-Intelligence/RepBend/tree/main?tab=readme-ov-file)
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## Citation
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```
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@article{repbend,
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title={Representation Bending for Large Language Model Safety},
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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},
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journal={arXiv preprint arXiv:2504.01550},
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year={2025}
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}
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```
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