YongchengYAO/MedVision
Updated • 7.75k • 19
How to use YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD")
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("YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD")
model = AutoModelForImageTextToText.from_pretrained("YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD")
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]:]))How to use YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD",
"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 run hf.co/YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD
How to use YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD" \
--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": "YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD",
"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 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 "YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD" \
--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": "YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD",
"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"
}
}
]
}
]
}'How to use YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD with Docker Model Runner:
docker model run hf.co/YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD
This is a supervised finetuned version of Qwen/Qwen2.5-VL-7B-Instruct.
Qwen/Qwen2.5-VL-7B-InstructQwen/Qwen2.5-VL-7B-InstructThe model is trained on the MedVision dataset. Specifically, this list defines the training data.
See the angle/distance (A/D) measurement results for Qwen2.5VL (7B, SFT_{5K}) on the MedVision project page.
@misc{yao2025medvisiondatasetbenchmarkquantitative,
title={MedVision: Dataset and Benchmark for Quantitative Medical Image Analysis},
author={Yongcheng Yao and Yongshuo Zong and Raman Dutt and Yongxin Yang and Sotirios A Tsaftaris and Timothy Hospedales},
year={2025},
eprint={2511.18676},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2511.18676},
}
docker model run hf.co/YongchengYAO/MedVision__SFT-m__qwen25vl-7b__AD