update readme

README.md

@@ -12,7 +12,7 @@ datasets:
 
 ## Introduction
 
-The Imp project aims to provide a family of highly capable yet 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.  
+The Imp project aims to provide a family of highly capable yet lightweight LMMs. Our `Imp-v1.5-4B-Phi3` is a strong lightweight LMMs with only **4B** parameters, which is build upon [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 :) 
@@ -61,7 +61,7 @@ print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True
 ```
 
 ## Model evaluation
-We conduct evaluation on 9 commonly-used benchmarks, including 5 academic VQA benchmarks and 4 popular MLLM benchmarks, to compare our Imp model with LLaVA (7B) and existing MSLMs of similar model sizes.
+We conduct evaluation on 9 commonly-used benchmarks, including 5 academic VQA benchmarks and 4 popular MLLM benchmarks, to compare our Imp model with LLaVA (7B) and existing lightweight LMMs of similar model sizes.
 
 | Models | Size | VQAv2 | GQA | SQA(IMG) | TextVQA | POPE |  MME(P) | MMB  |MMB_CN|MM-Vet|
 |:--------:|:-----:|:----:|:-------------:|:--------:|:-----:|:----:|:-------:|:-------:|:-------:|:-------:|