Instructions to use ydeng9/OpenVLThinker-7B-v1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ydeng9/OpenVLThinker-7B-v1.2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="ydeng9/OpenVLThinker-7B-v1.2")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ydeng9/OpenVLThinker-7B-v1.2")
model = AutoModelForImageTextToText.from_pretrained("ydeng9/OpenVLThinker-7B-v1.2")
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

vLLM

How to use ydeng9/OpenVLThinker-7B-v1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ydeng9/OpenVLThinker-7B-v1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ydeng9/OpenVLThinker-7B-v1.2",
		"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/ydeng9/OpenVLThinker-7B-v1.2

SGLang

How to use ydeng9/OpenVLThinker-7B-v1.2 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 "ydeng9/OpenVLThinker-7B-v1.2" \
    --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": "ydeng9/OpenVLThinker-7B-v1.2",
		"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 "ydeng9/OpenVLThinker-7B-v1.2" \
        --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": "ydeng9/OpenVLThinker-7B-v1.2",
		"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 ydeng9/OpenVLThinker-7B-v1.2 with Docker Model Runner:
```
docker model run hf.co/ydeng9/OpenVLThinker-7B-v1.2
```

ydeng9

nielsr HF Staff commited on Aug 5, 2025

Commit

9e53a22

verified ·

1 Parent(s): 12de3fc

Add project page link to model card (#1)

Browse files

- Add project page link to model card (6a687ebb38d660bf913e62367310517bc953bfd3)

Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -1,15 +1,15 @@
 ---
 base_model:
 - Qwen/Qwen2.5-VL-7B-Instruct
-license: apache-2.0
 library_name: transformers
 pipeline_tag: image-text-to-text
 ---
 ## Overview
 OpenVLThinker-7B-v1.2 is a vision-language reasoning model designed to handle multimodal tasks. It is especially tuned for visual mathematical problem-solving.
-For more details: [Paper](https://arxiv.org/abs/2503.17352), [GitHub](https://github.com/yihedeng9/OpenVLThinker)
 ## How to use
 ```python

 ---
 base_model:
 - Qwen/Qwen2.5-VL-7B-Instruct
 library_name: transformers
+license: apache-2.0
 pipeline_tag: image-text-to-text
 ---
 ## Overview
 OpenVLThinker-7B-v1.2 is a vision-language reasoning model designed to handle multimodal tasks. It is especially tuned for visual mathematical problem-solving.
+For more details: [Paper](https://arxiv.org/abs/2503.17352), [GitHub](https://github.com/yihedeng9/OpenVLThinker), [Project Page](https://yihe-deng.notion.site/openvlthinker)
 ## How to use
 ```python