Instructions to use OpenGVLab/Mono-InternVL-2B-S1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/Mono-InternVL-2B-S1-2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/Mono-InternVL-2B-S1-2", 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/Mono-InternVL-2B-S1-2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/Mono-InternVL-2B-S1-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/Mono-InternVL-2B-S1-2"
# 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/Mono-InternVL-2B-S1-2",
		"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/Mono-InternVL-2B-S1-2

SGLang

How to use OpenGVLab/Mono-InternVL-2B-S1-2 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/Mono-InternVL-2B-S1-2" \
    --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/Mono-InternVL-2B-S1-2",
		"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/Mono-InternVL-2B-S1-2" \
        --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/Mono-InternVL-2B-S1-2",
		"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/Mono-InternVL-2B-S1-2 with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/Mono-InternVL-2B-S1-2
```

Update model card with Mono-InternVL-1.5 paper details and expanded information

by nielsr HF Staff - opened Jul 22, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+399

-18

Update model card with Mono-InternVL-1.5 paper details and expanded informationa76dfd54

nielsr

Jul 22, 2025

This PR significantly enhances the model card for Mono-InternVL-2B-S1-2.
It updates the primary paper reference to Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models, which details recent advancements relevant to this model.
Additionally, it incorporates comprehensive details from the project's GitHub README, including:

News updates
A detailed Introduction to the Mono-InternVL architecture and principles
Performance benchmarks
Inference code examples using transformers and lmdeploy
Supervised Finetuning instructions
An updated Citation section to include both Mono-InternVL V1 and V1.5 papers
A clear License section.

The "File information" section has been removed as it's not standard model card content. This update provides users with a much richer and more informative resource for understanding and utilizing the model.

douwh changed pull request status to merged Jul 22, 2025

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