Instructions to use OpenGVLab/InternVL2-8B-MPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL2-8B-MPO with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-8B-MPO", 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/InternVL2-8B-MPO", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL2-8B-MPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL2-8B-MPO"
# 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/InternVL2-8B-MPO",
		"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/InternVL2-8B-MPO

SGLang

How to use OpenGVLab/InternVL2-8B-MPO 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/InternVL2-8B-MPO" \
    --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/InternVL2-8B-MPO",
		"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/InternVL2-8B-MPO" \
        --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/InternVL2-8B-MPO",
		"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/InternVL2-8B-MPO with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL2-8B-MPO
```

Weiyun1025 commited on Nov 14, 2024

Commit

239f8fe

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1 Parent(s): 19fbddb

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README.md CHANGED Viewed

@@ -49,10 +49,10 @@ This model exhibits enhanced multimodal reasoning abilities and fewer hallucinat
 | LLaVA-OneVision-7B      | 52.3  |   63.2    |      18.4       |        51.4        |    79.9     | 88.4  | 73.7  |    3.1     |
 | InternVL2-26B           | 58.2  |   59.4    |      23.4       |        62.1        |    92.3     | 88.0  | 75.6  |    3.7     |
 | InternVL2-40B           | 63.6  |   63.7    |      21.4       |        65.5        |    100.5    | 88.4  | 77.3  |    3.9     |
-| InternVL2-76B           | 65.4  |   65.5    |      23.7       |        65.7        |    99.3     | 89.0  | 77.8  |    3.8     |
 | InternVL2-Pro           | 65.6  |   66.3    |      18.8       |        69.4        |    99.5     | 88.2  | 77.6  |    3.7     |
 | InternVL2-8B            | 59.3  |   58.3    |      20.4       |        54.2        |    73.2     | 86.9  | 75.0  |    3.3     |
-| InternVL2-8B-MPO (ours) | 79.2  |   66.2    |      25.7       |        56.2        |    76.7     | 88.1  | 75.4  |    3.5     |
 ### Invitation to Evaluate InternVL
 We welcome MLLM benchmark developers to assess our InternVL1.5 and InternVL2 series models. If you need to add your evaluation results here, please contact me at [wangweiyun@pjlab.org.cn](mailto:wangweiyun@pjlab.org.cn).
@@ -541,10 +541,10 @@ InternVL2-8B-MPO基于[InternVL2-8B](https://huggingface.co/OpenGVLab/InternVL2-
 | LLaVA-OneVision-7B      | 52.3  |   63.2    |      18.4       |        51.4        |    79.9     | 88.4  | 73.7  |    3.1     |
 | InternVL2-26B           | 58.2  |   59.4    |      23.4       |        62.1        |    92.3     | 88.0  | 75.6  |    3.7     |
 | InternVL2-40B           | 63.6  |   63.7    |      21.4       |        65.5        |    100.5    | 88.4  | 77.3  |    3.9     |
-| InternVL2-76B           | 65.4  |   65.5    |      23.7       |        65.7        |    99.3     | 89.0  | 77.8  |    3.8     |
 | InternVL2-Pro           | 65.6  |   66.3    |      18.8       |        69.4        |    99.5     | 88.2  | 77.6  |    3.7     |
 | InternVL2-8B            | 59.3  |   58.3    |      20.4       |        54.2        |    73.2     | 86.9  | 75.0  |    3.3     |
-| InternVL2-8B-MPO (ours) | 79.2  |   66.2    |      25.7       |        56.2        |    76.7     | 88.1  | 75.4  |    3.5     |
 ### 邀请评测 InternVL
 我们欢迎各位 MLLM benchmark 的开发者对我们的 InternVL2-8B-MPO 模型进行评测。如果需要在此处添加评测结果，请与我联系（[wangweiyun@pjlab.org.cn](mailto:wangweiyun@pjlab.org.cn)）。

 | LLaVA-OneVision-7B      | 52.3  |   63.2    |      18.4       |        51.4        |    79.9     | 88.4  | 73.7  |    3.1     |
 | InternVL2-26B           | 58.2  |   59.4    |      23.4       |        62.1        |    92.3     | 88.0  | 75.6  |    3.7     |
 | InternVL2-40B           | 63.6  |   63.7    |      21.4       |        65.5        |    100.5    | 88.4  | 77.3  |    3.9     |
+| InternVL2-76B           | 65.4  |   67.5    |      23.7       |        65.7        |    99.3     | 89.0  | 77.8  |    3.8     |
 | InternVL2-Pro           | 65.6  |   66.3    |      18.8       |        69.4        |    99.5     | 88.2  | 77.6  |    3.7     |
 | InternVL2-8B            | 59.3  |   58.3    |      20.4       |        54.2        |    73.2     | 86.9  | 75.0  |    3.3     |
+| InternVL2-8B-MPO (ours) | 79.2  |   67.0    |      25.7       |        56.2        |    76.7     | 88.1  | 75.4  |    3.5     |
 ### Invitation to Evaluate InternVL
 We welcome MLLM benchmark developers to assess our InternVL1.5 and InternVL2 series models. If you need to add your evaluation results here, please contact me at [wangweiyun@pjlab.org.cn](mailto:wangweiyun@pjlab.org.cn).
 | LLaVA-OneVision-7B      | 52.3  |   63.2    |      18.4       |        51.4        |    79.9     | 88.4  | 73.7  |    3.1     |
 | InternVL2-26B           | 58.2  |   59.4    |      23.4       |        62.1        |    92.3     | 88.0  | 75.6  |    3.7     |
 | InternVL2-40B           | 63.6  |   63.7    |      21.4       |        65.5        |    100.5    | 88.4  | 77.3  |    3.9     |
+| InternVL2-76B           | 65.4  |   67.5    |      23.7       |        65.7        |    99.3     | 89.0  | 77.8  |    3.8     |
 | InternVL2-Pro           | 65.6  |   66.3    |      18.8       |        69.4        |    99.5     | 88.2  | 77.6  |    3.7     |
 | InternVL2-8B            | 59.3  |   58.3    |      20.4       |        54.2        |    73.2     | 86.9  | 75.0  |    3.3     |
+| InternVL2-8B-MPO (ours) | 79.2  |   67.0    |      25.7       |        56.2        |    76.7     | 88.1  | 75.4  |    3.5     |
 ### 邀请评测 InternVL
 我们欢迎各位 MLLM benchmark 的开发者对我们的 InternVL2-8B-MPO 模型进行评测。如果需要在此处添加评测结果，请与我联系（[wangweiyun@pjlab.org.cn](mailto:wangweiyun@pjlab.org.cn)）。