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 17, 2024

Commit

57094a0

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1 Parent(s): dd4d5dd

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README.md +18 -14

README.md CHANGED Viewed

@@ -68,13 +68,15 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
 ### Video Benchmarks
-|      Benchmark       | VideoChat2-Phi3 | Mini-InternVL-2B-1-5 | InternVL2-2B | InternVL2-1B |
-| :------------------: | :-------------: | :------------------: | :----------: | :----------: |
-|      Model Size      |       4B        |         2.2B         |     2.2B     |     0.9B     |
-|                      |                 |                      |              |              |
-|       MVBench        |      55.1       |         37.0         |     60.2     |     57.9     |
-| Video-MME<br>wo subs |        -        |         TODO         |     TODO     |     TODO     |
-| Video-MME<br>w/ subs |        -        |         TODO         |     TODO     |     TODO     |
 - We evaluate our models on MVBench by extracting 16 frames from each video, and each frame was resized to a 448x448 image.
@@ -453,13 +455,15 @@ InternVL 2.0 是一个多模态大语言模型系列，包含各种规模的模
 ### 视频相关评测
-|      评测数据集      | VideoChat2-Phi3 | Mini-InternVL-2B-1-5 | InternVL2-2B | InternVL2-1B |
-| :------------------: | :-------------: | :------------------: | :----------: | :----------: |
-|       模型大小       |       4B        |         2.2B         |     2.2B     |     0.9B     |
-|                      |                 |                      |              |              |
-|       MVBench        |      55.1       |         37.0         |     60.2     |     57.9     |
-| Video-MME<br>wo subs |        -        |         TODO         |     TODO     |     TODO     |
-| Video-MME<br>w/ subs |        -        |         TODO         |     TODO     |     TODO     |
 - 我们通过从每个视频中提取16帧来评估我们的模型在MVBench上的性能，每个视频帧被调整为448x448的图像。

 ### Video Benchmarks
+|          Benchmark          | VideoChat2-Phi3 | Mini-InternVL-2B-1-5 | InternVL2-2B | InternVL2-1B |
+| :-------------------------: | :-------------: | :------------------: | :----------: | :----------: |
+|         Model Size          |       4B        |         2.2B         |     2.2B     |     0.9B     |
+|                             |                 |                      |              |              |
+|           MVBench           |      55.1       |         37.0         |     60.2     |     57.9     |
+| MMBench-Video<sub>8f</sub>  |        -        |         0.99         |     0.97     |     0.95     |
+| MMBench-Video<sub>16f</sub> |        -        |         1.04         |     1.03     |     0.98     |
+|    Video-MME<br>wo subs     |        -        |         TODO         |     TODO     |     TODO     |
+|    Video-MME<br>w/ subs     |        -        |         TODO         |     TODO     |     TODO     |
 - We evaluate our models on MVBench by extracting 16 frames from each video, and each frame was resized to a 448x448 image.
 ### 视频相关评测
+|         评测数据集          | VideoChat2-Phi3 | Mini-InternVL-2B-1-5 | InternVL2-2B | InternVL2-1B |
+| :-------------------------: | :-------------: | :------------------: | :----------: | :----------: |
+|          模型大小           |       4B        |         2.2B         |     2.2B     |     0.9B     |
+|                             |                 |                      |              |              |
+|           MVBench           |      55.1       |         37.0         |     60.2     |     57.9     |
+| MMBench-Video<sub>8f</sub>  |        -        |         0.99         |     0.97     |     0.95     |
+| MMBench-Video<sub>16f</sub> |        -        |         1.04         |     1.03     |     0.98     |
+|    Video-MME<br>wo subs     |        -        |         TODO         |     TODO     |     TODO     |
+|    Video-MME<br>w/ subs     |        -        |         TODO         |     TODO     |     TODO     |
 - 我们通过从每个视频中提取16帧来评估我们的模型在MVBench上的性能，每个视频帧被调整为448x448的图像。