Instructions to use ifms111/UniReason-Med with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ifms111/UniReason-Med with Transformers:

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

pipe = pipeline("image-text-to-text", model="ifms111/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("ifms111/UniReason-Med")
model = AutoModelForMultimodalLM.from_pretrained("ifms111/UniReason-Med", 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 ifms111/UniReason-Med with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ifms111/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": "ifms111/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/ifms111/UniReason-Med

SGLang

How to use ifms111/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 "ifms111/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": "ifms111/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 "ifms111/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": "ifms111/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 ifms111/UniReason-Med with Docker Model Runner:
```
docker model run hf.co/ifms111/UniReason-Med
```

UniReason-Med / README.md

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	---
	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:
	- ifms111/UniReason-Med-Data
	---

	# UniReason-Med

	UniReason-Med is a medical multimodal model for grounded reasoning over 2D medical images and slice-serialized 3D volumes.

	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 processes either a 2D image or a 3D volume serialized as ordered slices, generating interleaved textual reasoning and localized visual evidence through shared bounding-box syntax and region-token injection.

	- Base model: [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
	- Training data: [ifms111/UniReason-Med-Data](https://huggingface.co/datasets/ifms111/UniReason-Med-Data)
	- Modalities: image + text -> text
	- License: Apache-2.0

	## Model Description

	UniReason-Med is trained to interleave free-form reasoning with localized visual evidence. During reasoning, the model emits bounding boxes over the input image; the referenced region is cropped and re-injected as additional visual context for the next reasoning step. The same shared interface is applied to 2D images and to 3D volumes serialized as ordered slice sequences.

	## Training

	The model is built with supervised fine-tuning followed by GRPO reinforcement learning. RL uses answer-correctness and format rewards rather than ground-truth localization-overlap rewards such as IoU or Dice.

	## Intended Use and Limitations

	- Intended use: research on medical multimodal reasoning, visual grounding, and 2D-to-3D transfer.
	- Out of scope: this is a research artifact and is not a medical device. It must not be used for clinical diagnosis, treatment decisions, or real patient care.
	- Limitations: outputs may be incorrect, incomplete, or biased; predicted bounding boxes are reasoning aids, not validated localization.

	## License

	Released under the Apache License 2.0, consistent with the base model Qwen2.5-VL-7B-Instruct.