Update README.md

README.md

@@ -2,10 +2,12 @@
 license: apache-2.0
 pipeline_tag: visual-question-answering
 ---
-# 😈Imp
+# 😈 Imp
 
 \[Technical report (coming soon)\]  [[Demo](https://xmbot,net/imp/)\]  [[Github](https://github.com/MILVLG/imp)\]
 
+## Introduction
+
 The Imp project aims to provide a family of  a strong multimodal `small` language models (MSLMs). Our `imp-v1-3b` 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.  
 
 As shown in the Table below, `imp-v1-3b` significantly outperforms the counterparts of similar model sizes, and even achieves slightly better performance than the strong LLaVA-7B model on various multimodal benchmarks. 
@@ -15,7 +17,7 @@ We release our model weights and provide an example below to run our model . Det
 
 ## How to use
 
-You can use the following code for model inference. We minimize the required dependency libraries that only the `transformers` and `torch` packages are used. The latest version of `transformers` is ok but we recommand v4.31.0. The format of text instructions is similar to [LLaVA](https://github.com/haotian-liu/LLaVA).
+You can use the following code for model inference. We minimize the required dependency libraries that only the `transformers` and `torch` packages need to be installed. The latest version of `transformers` is ok but we recommand v4.31.0. The format of text instruction is similar to [LLaVA](https://github.com/haotian-liu/LLaVA).
 
 ```Python
 import torch