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", device_map="auto")
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
```

OpenVLThinker-7B / README.md

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metadata

base_model:
  - Qwen/Qwen2.5-VL-7B-Instruct
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text

Overview

OpenVLThinker-7B is a vision-language reasoning model designed to handle multimodal tasks. It is especially tuned for visual mathematical problem-solving.

For more details: Blog, GitHub

How to use

from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
import torch
from qwen_vl_utils import process_vision_info
import requests
from PIL import Image

# 1. Define model and processor names
model_name = "ydeng9/OpenVLThinker-7B"
processor_name = "Qwen/Qwen2.5-VL-7B-Instruct"

# 2. Load the OpenVLThinker-7B model and processor
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map=device
)
processor = AutoProcessor.from_pretrained(processor_name)

# 3. Define a sample image URL and an instruction
image_url = "https://example.com/sample_image.jpg"  # replace with your image URL
instruction = "Example question"

# 4. Create a multimodal prompt using a chat message structure
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image_url},
            {"type": "text", "text": instruction},
        ],
    }
]

# 5. Generate a text prompt from the chat messages
text_prompt = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

# 6. Process image (and video) inputs from the messages
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text_prompt],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
).to(device)

# 7. Generate the model's response (with specified generation parameters)
generated_ids = model.generate(
    **inputs,
    do_sample=True,
    max_new_tokens=2048,
    top_p=0.001,
    top_k=1,
    temperature=0.01,
    repetition_penalty=1.0,
)

# 8. Decode the generated tokens into human-readable text
generated_text = processor.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]

# 9. Print the generated response
print("Generated Response:")
print(generated_text)

Citation

@misc{deng2025openvlthinker,
      title={OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-Improvement}, 
      author={Yihe Deng and Hritik Bansal and Fan Yin and Nanyun Peng and Wei Wang and Kai-Wei Chang},
      year={2025},
      eprint={2503.17352},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.17352}, 
}