update Citation

README.md

@@ -11,7 +11,7 @@ datasets:
 
 ## Introduction
 
-The Imp project aims to provide a family of strong multimodal lightweight LMMs. Our `Imp-v1.5-2B-Qwen1.5` is a strong MSLM with only **2B** parameters, which is build upon a small yet powerful SLM [Qwen1.5-1.8B-Chat ](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat)(1.8B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on on 1M mixed dataset.   
+The Imp project aims to provide a family of highly capable yet lightweight LMMs. Our `Imp-v1.5-2B-Qwen1.5` is a strong MSLM with only **2B** parameters, which is build upon a small yet powerful SLM [Qwen1.5-1.8B-Chat ](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat)(1.8B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on on 1M mixed dataset.   
 
 As shown in the Table below, `Imp-v1.5-2B-Qwen1.5` significantly outperforms the counterparts of similar model sizes. 
 
@@ -27,7 +27,7 @@ pip install transformers # latest version is ok, but we recommend v4.36.0
 pip install -q pillow accelerate einops
 ```
 
-You can use the following code for model inference. The format of text instruction is similar to [LLaVA](https://github.com/haotian-liu/LLaVA). 
+You can use the following code for model inference. The format of text instruction is similar to [LLaVA](https://github.com/haotian-liu/LLaVA). Note that the example can only be run on GPUs currently.
 ```Python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -78,7 +78,7 @@ 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}
 }