Update README.md

README.md

@@ -10,12 +10,22 @@ datasets:
 pipeline_tag: visual-question-answering
 ---
 
-# Model Card for InternVL-Chat-Chinese-V1.2-Plus
+# Model Card for InternVL-Chat-V1.2-Plus
 
-\[[Paper](https://arxiv.org/abs/2312.14238)\]  \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\]
+<img src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/X8AXMkOlKeUpNcoJIXKna.webp" alt="Image Description" width="300" height="300">
 
+\[[Paper](https://arxiv.org/abs/2312.14238)\]  \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
 
-InternVL-Chat-V1.2-Plus uses the same model architecture as [InternVL-Chat-V1.2](https://huggingface.co/OpenGVLab/InternVL-Chat-Chinese-V1-2), but the difference lies in the SFT dataset. InternVL-Chat-V1.2 only utilizes an SFT dataset with 1.2M samples, while **our plus version employs an SFT dataset with 12M samples**.
+| Model                   | Date       | Download                                                                    | Note                               |
+| ----------------------- | ---------- | --------------------------------------------------------------------------- | ---------------------------------- |
+| InternVL-Chat-V1.5      | 2024.04.18 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5)            | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (🔥new)|
+| InternVL-Chat-V1.2-Plus | 2024.02.21 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2-Plus)       | more SFT data and stronger  |
+| InternVL-Chat-V1.2      | 2024.02.11 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2)            | scaling up LLM to 34B       |
+| InternVL-Chat-V1.1      | 2024.01.24 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1)            | support Chinese and stronger OCR   |
+
+
+## InternVL-Chat-V1.2-Plus Blog
+InternVL-Chat-V1.2-Plus uses the same model architecture as [InternVL-Chat-V1.2](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2), but the difference lies in the SFT dataset. InternVL-Chat-V1.2 only utilizes an SFT dataset with 1.2M samples, while **our plus version employs an SFT dataset with 12M samples**.
 
 <img width="600" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/GIEKCvNc1Y5iMQqLv645p.png">
 
@@ -32,46 +42,43 @@ InternVL-Chat-V1.2-Plus uses the same model architecture as [InternVL-Chat-V1.2]
 | Qwen−VL−Max\*           | unknown    | 51.4          | 46.8           | 51.0                    | 77.6          | 75.7             | -    | -        | -                    | -    | 79.5             | -                 | -                | -             |
 |                         |            |               |                |                         |               |                  |      |          |                      |      |                  |                   |                  |               |
 | LLaVA−NEXT−34B          | 672x672    | 51.1          | 44.7           | 46.5                    | 79.3          | 79.0             | -    | 1631/397 | 81.8                 | 87.7 | 69.5             | 75.9              | 63.8             | 67.1†          |
-| InternVL−Chat−V1.2      | 448x448    | 51.6          | 46.2           | 47.7                    | 82.2          | 81.2             | 56.7 | 1672/509 | 83.3                 | 88.0 | 69.7             | 75.6              | 60.0             | 64.0†          |
-| InternVL−Chat−V1.2−Plus | 448x448    | 50.3          | 45.6           | 59.9                    | 83.8          | 82.0             | 58.7 | 1624/551 | 98.1†                | 88.7 | 71.3†            | 76.4              | -                | 66.9†          |
+| InternVL−Chat−V1.2      | 448x448    | 51.6          | 46.2           | 47.7                    | 82.2          | 81.2             | 56.7 | 1687/489 | 83.3                 | 88.0 | 72.5             | 75.6              | 60.0             | 64.0†          |
+| InternVL−Chat−V1.2−Plus | 448x448    | 50.3          | 45.6           | 59.9                    | 83.8          | 82.0             | 58.7 | 1625/553 | 98.1†                | 88.7 | 74.1†            | 76.4              | -                | 66.9†          |
 
 - MMBench results are collected from the [leaderboard](https://mmbench.opencompass.org.cn/leaderboard).
+- Update (2024-04-21): We have fixed a bug in the evaluation code, and the TextVQA results have been corrected.
 
 
 ## Model Details
-- **Model Type:** vision large language model, multimodal chatbot
+- **Model Type:** multimodal large language model (MLLM)
 - **Model Stats:**
   - Architecture: [InternViT-6B-448px-V1-2](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2) + MLP + [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)
+  - Image size: 448 x 448 (256 tokens)
   - Params: 40B
-  - Image size: 448 x 448
-  - Number of visual tokens: 256
 
 - **Training Strategy:**
   - Pretraining Stage
     - Learnable Component: MLP
     - Data: Trained on 8192x4800=39.3M samples, including COYO, LAION, CC12M, CC3M, SBU, Wukong, GRIT, Objects365, OpenImages, and OCR data.
     - Note: In this stage, we load the pretrained weights of [InternViT-6B-448px-V1-2](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-2). Moreover, in order to reduce the number of visual tokens, we use a pixel shuffle to reduce 1024 tokens to 256 tokens.
-  - SFT Stage
+  - Supervised Finetuning Stage
     - Learnable Component: ViT + MLP + LLM
     - Data: 12 million SFT samples.
 
 
 ## Model Usage
 
-We provide a minimum code example to run InternVL-Chat using only the `transformers` library.
+We provide an example code to run InternVL-Chat-V1.2 using `transformers`.
 
 You also can use our [online demo](https://internvl.opengvlab.com/) for a quick experience of this model.
 
-Note: If you meet this error `ImportError: This modeling file requires the following packages that were not found in your environment: fastchat`, please run `pip install fschat`.
-
-
 ```python
 import torch
 from PIL import Image
 from transformers import AutoModel, CLIPImageProcessor
 from transformers import AutoTokenizer
 
-path = "OpenGVLab/InternVL-Chat-Chinese-V1-2-Plus"
+path = "OpenGVLab/InternVL-Chat-V1-2-Plus"
 # If you have an 80G A100 GPU, you can put the entire model on a single GPU.
 model = AutoModel.from_pretrained(
     path,