Update README.md

README.md

@@ -3,13 +3,13 @@
 [![arXiv](https://img.shields.io/badge/Arxiv-2402.14289-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2402.14289)[![Github](https://img.shields.io/badge/Github-Github-blue.svg)](https://github.com/TinyLLaVA/TinyLLaVA_Factory)[![Demo](https://img.shields.io/badge/Demo-Demo-red.svg)](http://8843843nmph5.vicp.fun/#/)
 TinyLLaVA has released a family of small-scale Large Multimodel Models(LMMs), ranging from 0.55B to 3.1B. Our best model, TinyLLaVA-Phi-2-SigLIP-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL.
 ### TinyLLaVA
-Here, we introduce TinyLLaVA-OpenELM-270M-SigLIP-0.55B, which is trained by the [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) codebase. For LLM and vision tower, we choose [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively. The dataset used for training this model is the [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md) dataset.
+Here, we introduce TinyLLaVA-OpenELM-450M-CLIP-0.55B, which is trained by the [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) codebase. For LLM and vision tower, we choose [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) and [clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16), respectively. The dataset used for training this model is the [LLaVA](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md) dataset.
 
 ### Usage
 Execute the following test code:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-hf_path = 'jiajunlong/TinyLLaVA-OpenELM-270M-SigLIP-0.55B'
+hf_path = 'jiajunlong/TinyLLaVA-OpenELM-450M-CLIP-0.55B'
 model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True)
 model.cuda()
 config = model.config
@@ -25,7 +25,7 @@ print('runing time:', genertaion_time)
 |                          model_name                          | gqa   | textvqa | sqa   | vqav2 | MME     | MMB   | MM-VET |
 | :----------------------------------------------------------: | ----- | ------- | ----- | ----- | ------- | ----- | ------ |
 | [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B) | 60.3  | 51.7    | 60.3  | 76.9  | 1276.5  | 55.2  | 25.8   |
-| [TinyLLaVA-0.55B](https://huggingface.co/jiajunlong/TinyLLaVA-0.89B) | 50.38 | 36.37   | 50.02 | 65.44 | 1056.69 | 26.29 | 15.4   |
+| [TinyLLaVA-0.55B](https://huggingface.co/jiajunlong/TinyLLaVA-OpenELM-450M-CLIP-0.55B) | 50.38 | 36.37   | 50.02 | 65.44 | 1056.69 | 26.29 | 15.4   |
 
 P.S. [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) is an open-source modular codebase for small-scale LMMs with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. This code repository provides standard training&evaluating pipelines, flexible data preprocessing&model configurations, and easily extensible architectures. Users can customize their own LMMs with minimal coding effort and less coding mistake.
 TinyLLaVA Factory integrates a suite of cutting-edge models and methods.