Instructions to use OpenGVLab/InternVL2-Llama3-76B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL2-Llama3-76B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-Llama3-76B", 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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenGVLab/InternVL2-Llama3-76B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenGVLab/InternVL2-Llama3-76B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL2-Llama3-76B"
# 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-Llama3-76B",
		"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-Llama3-76B

SGLang

How to use OpenGVLab/InternVL2-Llama3-76B 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-Llama3-76B" \
    --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-Llama3-76B",
		"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-Llama3-76B" \
        --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-Llama3-76B",
		"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-Llama3-76B with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL2-Llama3-76B
```

czczup commited on Jul 19, 2024

Commit

8e7cb30

verified ·

1 Parent(s): c35c17f

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -58,7 +58,7 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
 |          SEED-Image          |      77.1       |        -         |     78.2      |         78.2         |
 |   HallBench<sub>avg</sub>    |      55.0       |       49.9       |     56.9      |         55.2         |
 | MathVista<sub>testmini</sub> |      63.8       |       67.7       |     63.7      |         65.5         |
-|  OpenCompass<sub>avg</sub>   |      69.9       |       67.9       |     69.7      |         70+          |
 - 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.
@@ -478,7 +478,7 @@ InternVL 2.0 是一个多模态大语言模型系列，包含各种规模的模
 |          SEED-Image          |      77.1       |        -         |     78.2      |         78.2         |
 |   HallBench<sub>avg</sub>    |      55.0       |       49.9       |     56.9      |         55.2         |
 | MathVista<sub>testmini</sub> |      63.8       |       67.7       |     63.7      |         65.5         |
-|  OpenCompass<sub>avg</sub>   |      69.9       |       67.9       |     69.7      |         70+          |
 - 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。OCRBench、RealWorldQA、HallBench 和 MathVista 是使用 VLMEvalKit 进行评估的。

 |          SEED-Image          |      77.1       |        -         |     78.2      |         78.2         |
 |   HallBench<sub>avg</sub>    |      55.0       |       49.9       |     56.9      |         55.2         |
 | MathVista<sub>testmini</sub> |      63.8       |       67.7       |     63.7      |         65.5         |
+|  OpenCompass<sub>avg</sub>   |      69.9       |       67.9       |     69.7      |         71.0          |
 - 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.
 |          SEED-Image          |      77.1       |        -         |     78.2      |         78.2         |
 |   HallBench<sub>avg</sub>    |      55.0       |       49.9       |     56.9      |         55.2         |
 | MathVista<sub>testmini</sub> |      63.8       |       67.7       |     63.7      |         65.5         |
+|  OpenCompass<sub>avg</sub>   |      69.9       |       67.9       |     69.7      |         71.0          |
 - 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。OCRBench、RealWorldQA、HallBench 和 MathVista 是使用 VLMEvalKit 进行评估的。