Add paper link

README.md

@@ -160,7 +160,7 @@ model-index:
 ---
 # **Mistral-ORPO-β (7B)**
 
-**Mistral-ORPO** is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using the *odds ratio preference optimization (ORPO)*. With ORPO, the model directly learns the preference without the supervised fine-tuning warmup phase. **Mistral-ORPO-β** is fine-tuned exclusively on the 61k instances of the cleaned version of UltraFeedback, [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned), by [Argilla](https://huggingface.co/argilla).
+**Mistral-ORPO** is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using the *[odds ratio preference optimization (ORPO)](https://arxiv.org/abs/2403.07691)*. With ORPO, the model directly learns the preference without the supervised fine-tuning warmup phase. **Mistral-ORPO-β** is fine-tuned exclusively on the 61k instances of the cleaned version of UltraFeedback, [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned), by [Argilla](https://huggingface.co/argilla).
 
 - **Github Repository**: https://github.com/xfactlab/orpo
 
@@ -214,4 +214,17 @@ response = tokenizer.batch_decode(output)
 #Hi! How are you doing?</s>
 #<|assistant|>
 #I'm doing well, thank you! How are you?</s>
+```
+
+## 📎 **Citation**
+
+```
+@misc{hong2024orpo,
+      title={ORPO: Monolithic Preference Optimization without Reference Model}, 
+      author={Jiwoo Hong and Noah Lee and James Thorne},
+      year={2024},
+      eprint={2403.07691},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
 ```