Instructions to use OpenGVLab/InternVL2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL2-1B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-1B", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenGVLab/InternVL2-1B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL2-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL2-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL2-1B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/OpenGVLab/InternVL2-1B

SGLang

How to use OpenGVLab/InternVL2-1B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OpenGVLab/InternVL2-1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL2-1B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OpenGVLab/InternVL2-1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL2-1B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use OpenGVLab/InternVL2-1B with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL2-1B
```

czczup commited on Jul 16, 2024

Commit

0706313

verified ·

1 Parent(s): d3da944

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README.md +66 -42

README.md CHANGED Viewed

@@ -9,6 +9,8 @@ pipeline_tag: image-text-to-text
 [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/)  [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[🚀 Quick Start\]](#quick-start)  [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971)  \[🌟 [魔搭社区](https://modelscope.cn/organization/OpenGVLab) | [教程](https://mp.weixin.qq.com/s/OUaVLkxlk1zhFb1cvMCFjg) \]
 ## Introduction
 We are excited to announce the release of InternVL 2.0, the latest addition to the InternVL series of multimodal large language models. InternVL 2.0 features a variety of **instruction-tuned models**, ranging from 1 billion to 108 billion parameters. This repository contains the instruction-tuned InternVL2-1B model.
@@ -17,6 +19,16 @@ Compared to the state-of-the-art open-source multimodal large language models, I
 InternVL 2.0 is trained with an 8k context window and utilizes training data consisting of long texts, multiple images, and videos, significantly improving its ability to handle these types of inputs compared to InternVL 1.5. For more details, please refer to our blog and GitHub.
 ## Model Details
 InternVL 2.0 is a multimodal large language model series, featuring models of various sizes. For each size, we release instruction-tuned models optimized for multimodal tasks. InternVL2-1B consists of [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px), an MLP projector, and [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).
@@ -25,27 +37,28 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
 ### Image Benchmarks
-|          Benchmark           | PaliGemma-3B | Mini-InternVL-2B-1.5 | InternVL2-2B | InternVL2-1B |
-| :--------------------------: | :----------: | :------------------: | :----------: | :----------: |
-|          Model Size          |     2.9B     |         2.2B         |     2.2B     |     0.9B     |
-|                              |              |                      |              |              |
-|    DocVQA<sub>test</sub>     |      -       |         85.0         |     86.9     |     81.7     |
-|    ChartQA<sub>test</sub>    |      -       |         74.8         |     76.2     |     72.9     |
-|    InfoVQA<sub>test</sub>    |      -       |         55.4         |     58.9     |     50.9     |
-|    TextVQA<sub>val</sub>     |     68.1     |         70.5         |     73.4     |     70.5     |
-|           OCRBench           |     614      |         654          |     784      |     754      |
-|      MME<sub>sum</sub>       |    1686.1    |        1901.5        |    1876.8    |    1794.4    |
-|         RealWorldQA          |     55.2     |         57.9         |     57.3     |     50.3     |
-|     AI2D<sub>test</sub>      |     68.3     |         69.8         |     74.1     |     64.1     |
-|      MMMU<sub>val</sub>      |     34.9     |     34.6 / 37.4      | 34.3 / 36.3  | 35.4 / 36.7  |
-|  MMBench-EN<sub>test</sub>   |     71.0     |         70.9         |     73.2     |     65.4     |
-|  MMBench-CN<sub>test</sub>   |     63.6     |         66.2         |     70.9     |     60.7     |
-|    CCBench<sub>dev</sub>     |     29.6     |         63.5         |     74.7     |     75.7     |
-|  MMVet<sub>GPT-4-0613</sub>  |      -       |         39.3         |     44.6     |     37.8     |
-| MMVet<sub>GPT-4-Turbo</sub>  |     33.1     |         35.5         |     39.5     |     37.3     |
-|          SEED-Image          |     69.6     |         69.8         |     71.6     |     65.6     |
-|   HallBench<sub>avg</sub>    |     32.2     |         37.5         |     37.9     |     33.4     |
-| MathVista<sub>testmini</sub> |     28.7     |         41.1         |     46.3     |     37.7     |
 - We simultaneously use InternVL and VLMEvalKit repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA, TextVQA, MME, AI2D, MMBench, CCBench, MMVet, and SEED-Image were tested using the InternVL repository. OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit.
@@ -393,6 +406,16 @@ If you find this project useful in your research, please consider citing:
 InternVL 2.0 使用 8k 上下文窗口进行训练，训练数据包含长文本、多图和视频数据，与 InternVL 1.5 相比，其处理这些类型输入的能力显著提高。更多详细信息，请参阅我们的博客和 GitHub。
 ## 模型细节
 InternVL 2.0 是一个多模态大语言模型系列，包含各种规模的模型。对于每个规模的模型，我们都会发布针对多模态任务优化的指令微调模型。InternVL2-1B 包含 [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)、一个 MLP 投影器和 [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)。
@@ -401,27 +424,28 @@ InternVL 2.0 是一个多模态大语言模型系列，包含各种规模的模
 ### 图像相关评测
-|          评测数据集          | PaliGemma-3B | Mini-InternVL-2B-1.5 | InternVL2-2B | InternVL2-1B |
-| :--------------------------: | :----------: | :------------------: | :----------: | :----------: |
-|           模型大小           |     2.9B     |         2.2B         |     2.2B     |     0.9B     |
-|                              |              |                      |              |              |
-|    DocVQA<sub>test</sub>     |      -       |         85.0         |     86.9     |     81.7     |
-|    ChartQA<sub>test</sub>    |      -       |         74.8         |     76.2     |     72.9     |
-|    InfoVQA<sub>test</sub>    |      -       |         55.4         |     58.9     |     50.9     |
-|    TextVQA<sub>val</sub>     |     68.1     |         70.5         |     73.4     |     70.5     |
-|           OCRBench           |     614      |         654          |     784      |     754      |
-|      MME<sub>sum</sub>       |    1686.1    |        1901.5        |    1876.8    |    1794.4    |
-|         RealWorldQA          |     55.2     |         57.9         |     57.3     |     50.3     |
-|     AI2D<sub>test</sub>      |     68.3     |         69.8         |     74.1     |     64.1     |
-|      MMMU<sub>val</sub>      |     34.9     |     34.6 / 37.4      | 34.3 / 36.3  | 35.4 / 36.7  |
-|  MMBench-EN<sub>test</sub>   |     71.0     |         70.9         |     73.2     |     65.4     |
-|  MMBench-CN<sub>test</sub>   |     63.6     |         66.2         |     70.9     |     60.7     |
-|    CCBench<sub>dev</sub>     |     29.6     |         63.5         |     74.7     |     75.7     |
-|  MMVet<sub>GPT-4-0613</sub>  |      -       |         39.3         |     44.6     |     37.8     |
-| MMVet<sub>GPT-4-Turbo</sub>  |     33.1     |         35.5         |     39.5     |     37.3     |
-|          SEED-Image          |     69.6     |         69.8         |     71.6     |     65.6     |
-|   HallBench<sub>avg</sub>    |     32.2     |         37.5         |     37.9     |     33.4     |
-| MathVista<sub>testmini</sub> |     28.7     |         41.1         |     46.3     |     37.7     |
 - 我们同时使用 InternVL 和 VLMEvalKit 仓库进行模型评估。具体来说，DocVQA、ChartQA、InfoVQA、TextVQA、MME、AI2D、MMBench、CCBench、MMVet 和 SEED-Image 的结果是使用 InternVL 仓库测试的。MMMU、OCRBench、RealWorldQA、HallBench 和 MathVista 是使用 VLMEvalKit 进行评估的。

 [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/)  [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[🚀 Quick Start\]](#quick-start)  [\[📖 中文解读\]](https://zhuanlan.zhihu.com/p/706547971)  \[🌟 [魔搭社区](https://modelscope.cn/organization/OpenGVLab) | [教程](https://mp.weixin.qq.com/s/OUaVLkxlk1zhFb1cvMCFjg) \]
+[切换至中文版](#简介)
 ## Introduction
 We are excited to announce the release of InternVL 2.0, the latest addition to the InternVL series of multimodal large language models. InternVL 2.0 features a variety of **instruction-tuned models**, ranging from 1 billion to 108 billion parameters. This repository contains the instruction-tuned InternVL2-1B model.
 InternVL 2.0 is trained with an 8k context window and utilizes training data consisting of long texts, multiple images, and videos, significantly improving its ability to handle these types of inputs compared to InternVL 1.5. For more details, please refer to our blog and GitHub.
+|      Model Name      |                                     Vision Part                                     |                                        Language Part                                         |                             HF Link                              |                                MS Link                                 |
+| :------------------: | :---------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: | :--------------------------------------------------------------: | :--------------------------------------------------------------------: |
+|     InternVL2-1B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |            [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)            |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-1B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-1B)     |
+|     InternVL2-2B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |          [internlm2-chat-1_8b](https://huggingface.co/internlm/internlm2-chat-1_8b)          |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-2B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-2B)     |
+|     InternVL2-4B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |    [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)     |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-4B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-4B)     |
+|     InternVL2-8B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |          [internlm2_5-7b-chat](https://huggingface.co/internlm/internlm2_5-7b-chat)          |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-8B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-8B)     |
+|    InternVL2-26B     | [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) |           [internlm2-chat-20b](https://huggingface.co/internlm/internlm2-chat-20b)           |    [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-26B)     |    [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-26B)     |
+|    InternVL2-40B     | [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) |       [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)       |    [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-40B)     |    [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-40B)     |
+| InternVL2-Llama3-76B | [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) | [Hermes-2-Theta-Llama-3-70B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-70B) | [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B) | [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-Llama3-76B) |
 ## Model Details
 InternVL 2.0 is a multimodal large language model series, featuring models of various sizes. For each size, we release instruction-tuned models optimized for multimodal tasks. InternVL2-1B consists of [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px), an MLP projector, and [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).
 ### Image Benchmarks
+|            Benchmark            | PaliGemma-3B | Mini-InternVL-2B-1-5 | InternVL2-2B | InternVL2-1B |
+| :-----------------------------: | :----------: | :------------------: | :----------: | :----------: |
+|           Model Size            |     2.9B     |         2.2B         |     2.2B     |     0.9B     |
+|                                 |              |                      |              |              |
+|      DocVQA<sub>test</sub>      |      -       |         85.0         |     86.9     |     81.7     |
+|     ChartQA<sub>test</sub>      |      -       |         74.8         |     76.2     |     72.9     |
+|     InfoVQA<sub>test</sub>      |      -       |         55.4         |     58.9     |     50.9     |
+|      TextVQA<sub>val</sub>      |     68.1     |         70.5         |     73.4     |     70.5     |
+|            OCRBench             |     614      |         654          |     784      |     754      |
+|        MME<sub>sum</sub>        |    1686.1    |        1901.5        |    1876.8    |    1794.4    |
+|           RealWorldQA           |     55.2     |         57.9         |     57.3     |     50.3     |
+|       AI2D<sub>test</sub>       |     68.3     |         69.8         |     74.1     |     64.1     |
+|       MMMU<sub>val</sub>        |     34.9     |     34.6 / 37.4      | 34.3 / 36.3  | 35.4 / 36.7  |
+|    MMBench-EN<sub>test</sub>    |     71.0     |         70.9         |     73.2     |     65.4     |
+|    MMBench-CN<sub>test</sub>    |     63.6     |         66.2         |     70.9     |     60.7     |
+|      CCBench<sub>dev</sub>      |     29.6     |         63.5         |     74.7     |     75.7     |
+|   MMVet<sub>GPT-4-0613</sub>    |      -       |         39.3         |     44.6     |     37.8     |
+|   MMVet<sub>GPT-4-Turbo</sub>   |     33.1     |         35.5         |     39.5     |     33.3     |
+|           SEED-Image            |     69.6     |         69.8         |     71.6     |     65.6     |
+|     HallBench<sub>avg</sub>     |     32.2     |         37.5         |     37.9     |     33.4     |
+|  MathVista<sub>testmini</sub>   |     28.7     |         41.1         |     46.3     |     37.7     |
+| OpenCompass<sub>avg-score</sub> |     46.6     |         49.8         |     54.0     |     48.3     |
 - We simultaneously use InternVL and VLMEvalKit repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA, TextVQA, MME, AI2D, MMBench, CCBench, MMVet, and SEED-Image were tested using the InternVL repository. OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit.
 InternVL 2.0 使用 8k 上下文窗口进行训练，训练数据包含长文本、多图和视频数据，与 InternVL 1.5 相比，其处理这些类型输入的能力显著提高。更多详细信息，请参阅我们的博客和 GitHub。
+|       模型名称       |                                      视觉部分                                       |                                           语言部分                                           |                             HF 链���                              |                                MS 链接                                 |
+| :------------------: | :---------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: | :--------------------------------------------------------------: | :--------------------------------------------------------------------: |
+|     InternVL2-1B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |            [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)            |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-1B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-1B)     |
+|     InternVL2-2B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |          [internlm2-chat-1_8b](https://huggingface.co/internlm/internlm2-chat-1_8b)          |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-2B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-2B)     |
+|     InternVL2-4B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |    [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)     |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-4B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-4B)     |
+|     InternVL2-8B     |    [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)    |          [internlm2_5-7b-chat](https://huggingface.co/internlm/internlm2_5-7b-chat)          |     [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-8B)     |     [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-8B)     |
+|    InternVL2-26B     | [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) |           [internlm2-chat-20b](https://huggingface.co/internlm/internlm2-chat-20b)           |    [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-26B)     |    [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-26B)     |
+|    InternVL2-40B     | [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) |       [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)       |    [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-40B)     |    [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-40B)     |
+| InternVL2-Llama3-76B | [InternViT-6B-448px-V1-5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) | [Hermes-2-Theta-Llama-3-70B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-70B) | [🤗 link](https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B) | [🤖 link](https://modelscope.cn/models/OpenGVLab/InternVL2-Llama3-76B) |
 ## 模型细节
 InternVL 2.0 是一个多模态大语言模型系列，包含各种规模的模型。对于每个规模的模型，我们都会发布针对多模态任务优化的指令微调模型。InternVL2-1B 包含 [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px)、一个 MLP 投影器和 [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)。
 ### 图像相关评测
+|           评测数据集            | PaliGemma-3B | Mini-InternVL-2B-1-5 | InternVL2-2B | InternVL2-1B |
+| :-----------------------------: | :----------: | :------------------: | :----------: | :----------: |
+|            模型大小             |     2.9B     |         2.2B         |     2.2B     |     0.9B     |
+|                                 |              |                      |              |              |
+|      DocVQA<sub>test</sub>      |      -       |         85.0         |     86.9     |     81.7     |
+|     ChartQA<sub>test</sub>      |      -       |         74.8         |     76.2     |     72.9     |
+|     InfoVQA<sub>test</sub>      |      -       |         55.4         |     58.9     |     50.9     |
+|      TextVQA<sub>val</sub>      |     68.1     |         70.5         |     73.4     |     70.5     |
+|            OCRBench             |     614      |         654          |     784      |     754      |
+|        MME<sub>sum</sub>        |    1686.1    |        1901.5        |    1876.8    |    1794.4    |
+|           RealWorldQA           |     55.2     |         57.9         |     57.3     |     50.3     |
+|       AI2D<sub>test</sub>       |     68.3     |         69.8         |     74.1     |     64.1     |
+|       MMMU<sub>val</sub>        |     34.9     |     34.6 / 37.4      | 34.3 / 36.3  | 35.4 / 36.7  |
+|    MMBench-EN<sub>test</sub>    |     71.0     |         70.9         |     73.2     |     65.4     |
+|    MMBench-CN<sub>test</sub>    |     63.6     |         66.2         |     70.9     |     60.7     |
+|      CCBench<sub>dev</sub>      |     29.6     |         63.5         |     74.7     |     75.7     |
+|   MMVet<sub>GPT-4-0613</sub>    |      -       |         39.3         |     44.6     |     37.8     |
+|   MMVet<sub>GPT-4-Turbo</sub>   |     33.1     |         35.5         |     39.5     |     37.3     |
+|           SEED-Image            |     69.6     |         69.8         |     71.6     |     65.6     |
+|     HallBench<sub>avg</sub>     |     32.2     |         37.5         |     37.9     |     33.4     |
+|  MathVista<sub>testmini</sub>   |     28.7     |         41.1         |     46.3     |     37.7     |
+| OpenCompass<sub>avg-score</sub> |     46.6     |         49.8         |     54.0     |     48.3     |
 - 我们同时使用 InternVL 和 VLMEvalKit 仓库进行模型评估。具体来说，DocVQA、ChartQA、InfoVQA、TextVQA、MME、AI2D、MMBench、CCBench、MMVet 和 SEED-Image 的结果是使用 InternVL 仓库测试的。MMMU、OCRBench、RealWorldQA、HallBench 和 MathVista 是使用 VLMEvalKit 进行评估的。