Update README.md

README.md

@@ -8,14 +8,14 @@ pipeline_tag: image-text-to-text
 [![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/#/)
 
 
-Here, we introduce TinyLLaVA-Qwen2-0.5B-siglip-so400m-patch14-384-base, which is trained by the [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) codebase. For LLM and vision tower, we choose [Qwen-2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively.
+Here, we introduce TinyLLaVA-Qwen2-SigLIP-0.5B , which is trained by the [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory) codebase. For LLM and vision tower, we choose [Qwen-2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) and [siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384), respectively.
 
 ### Usage
 Execute the following test code:
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-hf_path = 'Zhang199/TinyLLaVA-Qwen2-0.5B-siglip-so400m-patch14-384-base'
+hf_path = 'Zhang199/TinyLLaVA-Qwen2-SigLIP-0.5B'
 model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True)
 model.cuda()
 config = model.config
@@ -34,7 +34,7 @@ print('runing time:', genertaion_time)
 | [bczhou/TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B) (our legacy model) | 79.9  | 62.0    | 69.1  | 59.1 | 32.0  | 86.4  | 1464.9   | - |
 | [tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B](https://huggingface.co/tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B) | 78.4 | 61.6   | 64.4 | 53.6 | 26.9 | 86.4  | 1339.0 | 31.7 |
 | [tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B](https://huggingface.co/tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B) | 80.1 | 62.1   | 73.0 | 60.3 | 37.5 | 87.2  | 1466.4 | 38.4 |
-| [Zhang199/TinyLLaVA-Qwen2-0.5B-siglip-so400m-patch14-384-base](https://huggingface.co/Zhang199/TinyLLaVA-Qwen2-0.5B-siglip-so400m-patch14-384-base) | 72.33 | 55.84 | 60.14 | 45.17 | 19.5 | 86.59  | 1153 | 29.7 |
+| [Zhang199/TinyLLaVA-Qwen2-SigLIP-0.5B](https://huggingface.co/Zhang199/TinyLLaVA-Qwen2-0.5B-siglip-so400m-patch14-384-base) | 72.33 | 55.84 | 60.14 | 45.17 | 19.5 | 86.59  | 1153 | 29.7 |
 
 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.