update readme

README.md

@@ -19,7 +19,6 @@ datasets:
 The Imp project aims to provide a family of  a strong multimodal `small` language models (MSLMs). Our ``Imp-v1.5-4B-Phi3`` is a strong MSLM with only **4B** parameters, which is build upon a small yet powerful SLM [Phi-3 ](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)(3.8B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on 1M mixed dataset.  
 
 
-
 We release our model weights and provide an example below to run our model . Detailed technical report and corresponding training/evaluation code will be released soon on our [GitHub repo](https://github.com/MILVLG/imp). We will persistently improve our model and release the next versions to further improve model performance :) 
 
 
@@ -43,11 +42,11 @@ torch.set_default_device("cuda")
 
 #Create model
 model = AutoModelForCausalLM.from_pretrained(
-    "MILVLG/imp-v1.5-4b", 
+    "MILVLG/Imp-v1.5-4B-Phi3", 
     torch_dtype=torch.float16, 
     device_map="auto",
     trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained("MILVLG/imp-v1.5-4b", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("MILVLG/Imp-v1.5-4B-Phi3", trust_remote_code=True)
 
 #Set inputs
 text = "<|user|>\n<image>\nWhat are the colors of the bus in the image?\n<|end|>\n<|assistant|>\n"
@@ -71,7 +70,6 @@ We conduct evaluation on 9 commonly-used benchmarks, including 5 academic VQA be
 | Models | Size | VQAv2 | GQA | SQA(IMG) | TextVQA | POPE |  MME(P) | MMB  |MMB_CN|MM-Vet|
 |:--------:|:-----:|:----:|:-------------:|:--------:|:-----:|:----:|:-------:|:-------:|:-------:|:-------:|
 | Imp-v1.5-4B-Phi3| 4B | 81.46 | 63.51 | 77.99|60.16 | 86.86| 1507.7 |73.28  |61.08|44.6|
-<!-- | [LLaVA-v1.5-lora](https://huggingface.co/liuhaotian/llava-v1.5-7b) | 7B |79.10 | 63.00|  68.40 |58.20| 86.40 | 1476.9 | 66.10 |- |30.2| -->
 
 
 
@@ -86,10 +84,10 @@ 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}
 }
 ```