update readme

README.md

@@ -11,7 +11,7 @@ datasets:
 
 ## Introduction
 
-The Imp project aims to provide a family of highly capable yet lightweight LMMs. Our `Imp-v1.5-2B-Qwen1.5` is a strong MSLM with only **2B** parameters, which is build upon a small yet powerful SLM [Qwen1.5-1.8B-Chat ](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat)(1.8B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on on 1M mixed dataset.   
+The Imp project aims to provide a family of highly capable yet lightweight LMMs. Our `Imp-v1.5-2B-Qwen1.5` is a strong lightweight LMM with only **2B** parameters, which is build upon [Qwen1.5-1.8B-Chat ](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat)(1.8B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on on 1M mixed dataset.   
 
 As shown in the Table below, `Imp-v1.5-2B-Qwen1.5` significantly outperforms the counterparts of similar model sizes. 
 
@@ -60,7 +60,7 @@ 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  |MMBCN  |MM-Vet|
 |:--------:|:-----:|:----:|:-------------:|:--------:|:-----:|:----:|:-------:|:-------:|:-------:|:-------:|