Instructions to use ydeng9/OpenVLThinker-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ydeng9/OpenVLThinker-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="ydeng9/OpenVLThinker-7B")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ydeng9/OpenVLThinker-7B")
model = AutoModelForMultimodalLM.from_pretrained("ydeng9/OpenVLThinker-7B")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ydeng9/OpenVLThinker-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ydeng9/OpenVLThinker-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ydeng9/OpenVLThinker-7B",
		"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/ydeng9/OpenVLThinker-7B

SGLang

How to use ydeng9/OpenVLThinker-7B 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 "ydeng9/OpenVLThinker-7B" \
    --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": "ydeng9/OpenVLThinker-7B",
		"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 "ydeng9/OpenVLThinker-7B" \
        --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": "ydeng9/OpenVLThinker-7B",
		"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 ydeng9/OpenVLThinker-7B with Docker Model Runner:
```
docker model run hf.co/ydeng9/OpenVLThinker-7B
```

Enhance OpenVLThinker-7B model card: Add paper info, abstract, and descriptive tags

by nielsr HF Staff - opened Jul 24, 2025

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+18

-4

Enhance OpenVLThinker-7B model card: Add paper info, abstract, and descriptive tagsb1d67b90

nielsr

Jul 24, 2025

This PR significantly improves the model card for OpenVLThinker-7B by:

Adding comprehensive tags to metadata: Enhances discoverability on the Hub with relevant keywords like reasoning, multimodal, vlm, math, and visual-question-answering, extracted from the paper's abstract.
Introducing a prominent paper section: Provides immediate access to the paper's title, direct Hugging Face paper page link, and the full abstract, offering users crucial context about the model.
Refining the citation: Updates the BibTeX entry's URL to the official Hugging Face paper page and corrects the title, ensuring accurate and consistent referencing within the Hub. The citation block format is also explicitly set to bibtex for proper rendering.

These updates aim to make the model card more informative, discoverable, and aligned with Hugging Face Hub documentation best practices.

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