update Citation

README.md

@@ -10,7 +10,7 @@ datasets:
 
 ## Introduction
 
-The Imp project aims to provide a family of strong multimodal lightweight LMMs. Our `Imp-v1.5-3B-Phi2` is a strong MSLM with only **3B** parameters, which is build upon a small yet powerful SLM [Phi-2 ](https://huggingface.co/microsoft/phi-2)(2.7B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on 1M mixed dataset.  
+The Imp project aims to provide a family of highly capable yet lightweight LMMs. Our `Imp-v1.5-3B-Phi2` is a strong MSLM with only **3B** parameters, which is build upon a small yet powerful SLM [Phi-2 ](https://huggingface.co/microsoft/phi-2)(2.7B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on 1M mixed dataset.  
 
 As shown in the Table below, `Imp-v1.5-3B-Phi2` significantly outperforms the counterparts of similar model sizes, and even achieves slightly better performance than the strong LLaVA-7B model on various multimodal benchmarks. 
 
@@ -83,7 +83,7 @@ If you use our model or refer our work in your studies, please cite:
 ```bibtex
 @article{imp2024,
   title={Imp: Highly Capable Large Multimodal Models for Mobile Devices},
-  author={Shao, Zhenwei and Yu, Zhou and Yu, Jun and Ouyang, Xuecheng and Lihao, Zheng and Zhenbiao, Gai and Mingyang, Wang and Jiajun, Ding},
+  author={Shao, Zhenwei and Yu, Zhou and Yu, Jun and Ouyang, Xuecheng and Zheng, Lihao and Gai, Zhenbiao and Wang, Mingyang and Ding, Jiajun},
   journal={arXiv preprint arXiv:2405.12107},
   year={2024}
 }