Instructions to use OpenGVLab/InternVL2-40B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL2-40B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-40B", 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/InternVL2-40B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL2-40B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL2-40B"
# 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/InternVL2-40B",
		"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/InternVL2-40B

SGLang

How to use OpenGVLab/InternVL2-40B 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/InternVL2-40B" \
    --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/InternVL2-40B",
		"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/InternVL2-40B" \
        --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/InternVL2-40B",
		"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/InternVL2-40B with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL2-40B
```

czczup commited on Jul 8, 2024

Commit

979f6ba

verified ·

1 Parent(s): c9be6a0

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README.md +4 -4

README.md CHANGED Viewed

@@ -29,9 +29,9 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
 | :--------------------------: | :-------------: | :------------: | :-----------: | :-----------: |
 |          Model Size          |        -        |       -        |     25.5B     |      40B      |
 |                              |                 |                |               |               |
-|    DocVQA<sub>test</sub>     |      87.2       |      86.5      |     92.9      |               |
 |    ChartQA<sub>test</sub>    |      78.1       |      81.3      |     84.9      |     86.2      |
-|    InfoVQA<sub>test</sub>    |        -        |      72.7      |     75.9      |               |
 |    TextVQA<sub>val</sub>     |        -        |      73.5      |     82.3      |     83.0      |
 |           OCRBench           |       678       |      754       |      825      |      837      |
 |      MME<sub>sum</sub>       |     2070.2      |     2110.6     |    2260.7     |    2315.0     |
@@ -469,9 +469,9 @@ InternVL 2.0 是一个多模态大语言模型系列，包含各种规模的模
 | :--------------------------: | :-------------: | :------------: | :-----------: | :-----------: |
 |           模型大小           |        -        |       -        |     25.5B     |      40B      |
 |                              |                 |                |               |               |
-|    DocVQA<sub>test</sub>     |      87.2       |      86.5      |     92.9      |               |
 |    ChartQA<sub>test</sub>    |      78.1       |      81.3      |     84.9      |     86.2      |
-|    InfoVQA<sub>test</sub>    |        -        |      72.7      |     75.9      |               |
 |    TextVQA<sub>val</sub>     |        -        |      73.5      |     82.3      |     83.0      |
 |           OCRBench           |       678       |      754       |      825      |      837      |
 |      MME<sub>sum</sub>       |     2070.2      |     2110.6     |    2260.7     |    2315.0     |

 | :--------------------------: | :-------------: | :------------: | :-----------: | :-----------: |
 |          Model Size          |        -        |       -        |     25.5B     |      40B      |
 |                              |                 |                |               |               |
+|    DocVQA<sub>test</sub>     |      87.2       |      86.5      |     92.9      |     93.9      |
 |    ChartQA<sub>test</sub>    |      78.1       |      81.3      |     84.9      |     86.2      |
+|    InfoVQA<sub>test</sub>    |        -        |      72.7      |     75.9      |     78.7      |
 |    TextVQA<sub>val</sub>     |        -        |      73.5      |     82.3      |     83.0      |
 |           OCRBench           |       678       |      754       |      825      |      837      |
 |      MME<sub>sum</sub>       |     2070.2      |     2110.6     |    2260.7     |    2315.0     |
 | :--------------------------: | :-------------: | :------------: | :-----------: | :-----------: |
 |           模型大小           |        -        |       -        |     25.5B     |      40B      |
 |                              |                 |                |               |               |
+|    DocVQA<sub>test</sub>     |      87.2       |      86.5      |     92.9      |     93.9      |
 |    ChartQA<sub>test</sub>    |      78.1       |      81.3      |     84.9      |     86.2      |
+|    InfoVQA<sub>test</sub>    |        -        |      72.7      |     75.9      |     78.7      |
 |    TextVQA<sub>val</sub>     |        -        |      73.5      |     82.3      |     83.0      |
 |           OCRBench           |       678       |      754       |      825      |      837      |
 |      MME<sub>sum</sub>       |     2070.2      |     2110.6     |    2260.7     |    2315.0     |