Instructions to use IQuestLab/UniReason-Med with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IQuestLab/UniReason-Med with Transformers:

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

pipe = pipeline("image-text-to-text", model="IQuestLab/UniReason-Med")
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("IQuestLab/UniReason-Med")
model = AutoModelForMultimodalLM.from_pretrained("IQuestLab/UniReason-Med")
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 IQuestLab/UniReason-Med with vLLM:

Install from pip and serve model

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

SGLang

How to use IQuestLab/UniReason-Med 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 "IQuestLab/UniReason-Med" \
    --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": "IQuestLab/UniReason-Med",
		"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 "IQuestLab/UniReason-Med" \
        --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": "IQuestLab/UniReason-Med",
		"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 IQuestLab/UniReason-Med with Docker Model Runner:
```
docker model run hf.co/IQuestLab/UniReason-Med
```

Add link to research paper

by nielsr HF Staff - opened 7 days ago

base: refs/heads/main

←

from: refs/pr/5

Discussion Files changed

+17

-17

Files changed (1) hide show

README.md +17 -17

README.md CHANGED Viewed

@@ -1,28 +1,28 @@
 ---
-license: apache-2.0
 base_model:
-  - Qwen/Qwen2.5-VL-7B-Instruct
-pipeline_tag: image-text-to-text
 library_name: transformers
 tags:
-  - medical
-  - multimodal
-  - vqa
-  - visual-grounding
-  - chain-of-thought
-  - reinforcement-learning
-  - grpo
-  - qwen2_5_vl
-language:
-  - en
-datasets:
-  - IQuestLab/UniReason-Med-Data
 ---
 # UniReason-Med
 UniReason-Med is a medical multimodal model that accompanies the paper
-**"UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA"**.
 It studies whether grounded reasoning supervision from abundant 2D medical images can improve
 3D medical VQA when both modalities share a common reasoning interface. A single checkpoint
@@ -90,4 +90,4 @@ If you use this model, please cite the UniReason-Med paper:
   author = {UniReason-Med Team},
   year   = {2025}
 }
-```

 ---
 base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+datasets:
+- IQuestLab/UniReason-Med-Data
+language:
+- en
 library_name: transformers
+license: apache-2.0
+pipeline_tag: image-text-to-text
 tags:
+- medical
+- multimodal
+- vqa
+- visual-grounding
+- chain-of-thought
+- reinforcement-learning
+- grpo
+- qwen2_5_vl
 ---
 # UniReason-Med
 UniReason-Med is a medical multimodal model that accompanies the paper
+**["UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA"](https://huggingface.co/papers/2606.11740)**.
 It studies whether grounded reasoning supervision from abundant 2D medical images can improve
 3D medical VQA when both modalities share a common reasoning interface. A single checkpoint
   author = {UniReason-Med Team},
   year   = {2025}
 }
+```