update readme

README.md

@@ -12,7 +12,7 @@ datasets:
 
 ## Introduction
 
-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.  
+The Imp project aims to provide a family of strong multimodal lightweight LMMs. 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 :) 
@@ -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). A Colab page to run this example is provided [here](https://colab.research.google.com/drive/1EBYky6xIPjnlPppo2gZaiNK6gEsjXgom?usp=drive_link#scrollTo=2-VpU6QzWCVZ). Note that the example can only be run on GPUs currently.
+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
@@ -65,7 +65,8 @@ 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 | 81.5 | 63.5 | 78.0|60.2 | 86.9| 1507.7 |73.3  |61.1|44.6|
+| Imp-v1.5-4B-Phi3| 4B | **81.7** | 63.4 | 76.3|- | **87.0**| 1503.9 |**74.1**  |**64.8**|-|
+| Imp-v1.5-4B-Phi3| 4B | 81.5 | **63.5** | **78.0**|60.2 | 86.9| **1507.7** |73.3  |61.1|44.6|