Update README.md

README.md

@@ -10,7 +10,7 @@ datasets:
 pipeline_tag: image-feature-extraction
 ---
 
-# Model Card for InternViT-6B-224px
+# InternViT-6B-224px
 
 <p align="center">
 <img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/jSJ7TChEGvGP_gwNhrYoA.webp" alt="Image Description" width="300" height="300">
@@ -20,15 +20,6 @@ pipeline_tag: image-feature-extraction
 
 [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[🚀 Quick Start\]](#model-usage)  [\[🌐 Community-hosted API\]](https://rapidapi.com/adushar1320/api/internvl-chat)  [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/675877376)
 
-| Model                   | Date       | Download                                                               | Note                             |
-| ----------------------- | ---------- | ---------------------------------------------------------------------- | -------------------------------- |
-| InternViT-6B-448px-V1-5 | 2024.04.20 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) | support dynamic resolution, super strong OCR (🔥new) |
-| InternViT-6B-448px-V1-2 | 2024.02.11 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2) | 448 resolution                   |
-| InternViT-6B-448px-V1-0 | 2024.01.30 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-0) | 448 resolution                   |
-| InternViT-6B-224px      | 2023.12.22 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternViT-6B-224px)      | vision foundation model          |
-| InternVL-14B-224px      | 2023.12.22 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-14B-224px)      | vision-language foundation model |
-
-
 ## Model Details
 - **Model Type:** vision foundation model, feature backbone
 - **Model Stats:**
@@ -37,7 +28,6 @@ pipeline_tag: image-feature-extraction
 - **Pretrain Dataset:** LAION-en, LAION-COCO, COYO, CC12M, CC3M, SBU, Wukong, LAION-multi
 - **Note:** This model has 48 blocks, and we found that using the output after the fourth-to-last block worked best for VLLM. Therefore, when building a VLLM with this model, **please use the features from the fourth-to-last layer.**
 
-
 ## Linear Probing Performance
 
 See this [document](https://github.com/OpenGVLab/InternVL/tree/main/classification#-evaluation) for more details about the linear probing evaluation.
@@ -88,7 +78,6 @@ If you find this project useful in your research, please consider citing:
 }
 ```
 
-
 ## Acknowledgement
 
 InternVL is built with reference to the code of the following projects: [OpenAI CLIP](https://github.com/openai/CLIP), [Open CLIP](https://github.com/mlfoundations/open_clip), [CLIP Benchmark](https://github.com/LAION-AI/CLIP_benchmark), [EVA](https://github.com/baaivision/EVA/tree/master), [InternImage](https://github.com/OpenGVLab/InternImage), [ViT-Adapter](https://github.com/czczup/ViT-Adapter), [MMSegmentation](https://github.com/open-mmlab/mmsegmentation), [Transformers](https://github.com/huggingface/transformers), [DINOv2](https://github.com/facebookresearch/dinov2), [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2), [Qwen-VL](https://github.com/QwenLM/Qwen-VL/tree/master/eval_mm), and [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). Thanks for their awesome work!