Add model card

README.md

@@ -1 +1,47 @@
-Model RepBend
+---
+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}
+}
+```