Instructions to use OpenGVLab/InternVL2-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL2-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-2B", 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/InternVL2-2B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL2-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL2-2B"
# 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/InternVL2-2B",
		"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/InternVL2-2B

SGLang

How to use OpenGVLab/InternVL2-2B 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/InternVL2-2B" \
    --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/InternVL2-2B",
		"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/InternVL2-2B" \
        --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/InternVL2-2B",
		"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/InternVL2-2B with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL2-2B
```

Weiyun1025 commited on Jul 3, 2024

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@@ -19,26 +19,26 @@ InternVL 2.0 is trained with an 8k context window and utilizes training data con
 ## Performance
-|         Benchmark         | PaliGemma-3B | Phi-3-Vision | Mini-InternVL-2B-1.5 | InternVL2-2B |
-| :-----------------------: | :----------: | :----------: | :------------------: | :----------: |
-|        Model Size         |     2.9B     |     4.2B     |         2.2B         |     2.2B     |
-|                           |              |              |                      |              |
-|   DocVQA<sub>test</sub>   |      -       |      -       |         85.0         |     TODO     |
-|  ChartQA<sub>test</sub>   |      -       |     81.4     |         74.8         |     76.2     |
-|  InfoVQA<sub>test</sub>   |      -       |      -       |         55.4         |     TODO     |
-|   TextVQA<sub>val</sub>   |     68.1     |     70.9     |         70.5         |     73.4     |
-|         OCRBench          |     614      |     639      |         654          |     784      |
-|     MME<sub>sum</sub>     |    1686.1    |    1508.0    |        1901.5        |    1876.8    |
-|        RealWorldQA        |     55.2     |     58.8     |         57.9         |     57.3     |
-|    AI2D<sub>test</sub>    |     68.3     |     76.7     |         69.8         |     74.1     |
-|    MMMU<sub>val</sub>     |     34.9     |     40.4     |         34.6         |     34.3     |
-| MMBench-EN<sub>test</sub> |     71.0     |     73.6     |         70.9         |     73.2     |
-| MMBench-CN<sub>test</sub> |     63.6     |      -       |         66.2         |     70.9     |
-|   CCBench<sub>dev</sub>   |     29.6     |     24.1     |         63.5         |     74.7     |
-| MMVet<sub>GPT4-0613</sub> |     33.1     |      -       |         39.3         |     44.6     |
-|        SEED-Image         |     69.6     |     70.9     |         69.8         |     71.6     |
-| HallBench<sub>avg.</sub>  |     32.2     |     39.0     |         37.5         |     37.9     |
-| MathVista<sub>mini</sub>  |     28.7     |     44.5     |         41.1         |     46.3     |
 - 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. MMMU, OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit.

 ## Performance
+|          Benchmark           | PaliGemma-3B | Phi-3-Vision | Mini-InternVL-2B-1.5 | InternVL2-2B |
+| :--------------------------: | :----------: | :----------: | :------------------: | :----------: |
+|          Model Size          |     2.9B     |     4.2B     |         2.2B         |     2.2B     |
+|                              |              |              |                      |              |
+|    DocVQA<sub>test</sub>     |      -       |      -       |         85.0         |     86.9     |
+|    ChartQA<sub>test</sub>    |      -       |     81.4     |         74.8         |     76.2     |
+|    InfoVQA<sub>test</sub>    |      -       |      -       |         55.4         |     58.9     |
+|    TextVQA<sub>val</sub>     |     68.1     |     70.9     |         70.5         |     73.4     |
+|           OCRBench           |     614      |     639      |         654          |     784      |
+|      MME<sub>sum</sub>       |    1686.1    |    1508.0    |        1901.5        |    1876.8    |
+|         RealWorldQA          |     55.2     |     58.8     |         57.9         |     57.3     |
+|     AI2D<sub>test</sub>      |     68.3     |     76.7     |         69.8         |     74.1     |
+|      MMMU<sub>val</sub>      |     34.9     |     40.4     |         34.6         |     34.3     |
+|  MMBench-EN<sub>test</sub>   |     71.0     |     73.6     |         70.9         |     73.2     |
+|  MMBench-CN<sub>test</sub>   |     63.6     |      -       |         66.2         |     70.9     |
+|    CCBench<sub>dev</sub>     |     29.6     |     24.1     |         63.5         |     74.7     |
+|  MMVet<sub>GPT-4-0613</sub>  |     33.1     |      -       |         39.3         |     44.6     |
+|          SEED-Image          |     69.6     |     70.9     |         69.8         |     71.6     |
+|   HallBench<sub>avg</sub>    |     32.2     |     39.0     |         37.5         |     37.9     |
+| MathVista<sub>testmini</sub> |     28.7     |     44.5     |         41.1         |     46.3     |
 - 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. MMMU, OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit.