Update README.md

README.md

@@ -4,10 +4,10 @@ language:
 - en
 pipeline_tag: visual-question-answering
 ---
-# 😈 IMP
+# 😈Imp
 
 
-The 😈 IMP project aims to provide a family of  a strong multimodal `small` language models (MSLMs). Our `imp-v0-3b` 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.  
+The Imp project aims to provide a family of  a strong multimodal `small` language models (MSLMs). Our `imp-v0-3b` 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-v0-3b` significantly outperforms the counterparts of similar model sizes, and even achieves slightly better performance than the strong LLaVA-7B model on various multimodal benchmarks. 
 
@@ -49,7 +49,7 @@ print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True
 ```
 
 ## Model evaluation
-We perform evaluation on 8 commonly-used benchmarks to validate the effectiveness of our model, including 5 academic VQA benchmarks and 3 recent MLLM benchmarks.
+We perform evaluation on 9 commonly-used benchmarks to validate the effectiveness of our model, including 5 academic VQA benchmarks and 4 popular MLLM benchmarks.
 
 | Models | Size | VQAv2 | GQA |VisWiz  | SQA (IMG) | TextVQA | POPE |  MME | MMB  |MM-Vet|
 |:--------:|:-----:|:----:|:----:|:-------------:|:--------:|:-----:|:----:|:-------:|:-------:|:-------:|