update readme

README.md

@@ -16,7 +16,7 @@ datasets:
 
 ## Introduction
 
-The Imp project aims to provide a family of  a strong multimodal `small` language models (MSLMs). 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 the [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA) training set.  
+The Imp project aims to provide a family of  a strong multimodal `small` language models (MSLMs). 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. 
 
@@ -89,10 +89,10 @@ This project is maintained by the [MILVLG](https://github.com/MILVLG)@Hangzhou D
 If you use our model or refer our work in your studies, please cite:
 
 ```bibtex
-@misc{imp2024,
-  author = {Shao, Zhenwei and Ouyang, Xuecheng and Yu, Zhou and Yu, Jun},
-  title = {Imp: An Emprical Study of Multimodal Small Language Models},
-  year = {2024},
-  url = {https://huggingface.co/MILVLG/imp-v1-3b}
+@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},
+  journal={arXiv preprint arXiv:2405.12107},
+  year={2024}
 }
 ```