Add model card and Apache-2.0 license
#4
by RobinChen2001 - opened
README.md
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@@ -32,6 +32,7 @@ injection under a common grounded reasoning policy.
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- **Base model:** [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
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- **Training data:** [IQuestLab/UniReason-Med-Data](https://huggingface.co/datasets/IQuestLab/UniReason-Med-Data)
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- **Modalities:** image + text → text
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- **License:** Apache-2.0
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Training code (LLaMA-Factory for SFT, verl for GRPO) and configs are released at:
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<https://github.com/IQuestLab/unireason-med>.
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## Usage
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```python
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from transformers import AutoModelForImageTextToText, AutoProcessor
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from PIL import Image
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model_id = "IQuestLab/UniReason-Med"
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processor = AutoProcessor.from_pretrained(model_id)
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model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")
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image = Image.open("medical_image.png")
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": "What is the most likely diagnosis? Reason step by step."},
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],
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}
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]
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prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = processor(text=[prompt], images=[image], return_tensors="pt").to(model.device)
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output = model.generate(**inputs, max_new_tokens=1024)
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print(processor.batch_decode(output, skip_special_tokens=True)[0])
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```
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The model produces interleaved reasoning with bounding boxes over the input image. Reproducing
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the full grounded crop-and-continue loop (crop the predicted region and feed it back as visual
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input) follows the agent/rollout logic in the released training code.
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## Intended Use and Limitations
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- **Intended use:** research on medical multimodal reasoning, visual grounding, and 2D-to-3D
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boxes are reasoning aids, not validated localization. Always involve qualified medical
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professionals for any health-related decision.
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## Data Notice
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The public training-data release keeps 3D examples text-only and does not redistribute 3D image
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data, because those samples are derived from M3D whose underlying image sources include
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Radiopaedia and may require separate authorization. See the
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[dataset card](https://huggingface.co/datasets/IQuestLab/UniReason-Med-Data) for details.
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## License
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Released under the [Apache License 2.0](./LICENSE), consistent with the base model
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- **Base model:** [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
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- **Training data:** [IQuestLab/UniReason-Med-Data](https://huggingface.co/datasets/IQuestLab/UniReason-Med-Data)
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- **Code:** [github.com/IQuestLab/unireason-med](https://github.com/IQuestLab/unireason-med)
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- **Modalities:** image + text → text
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- **License:** Apache-2.0
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Training code (LLaMA-Factory for SFT, verl for GRPO) and configs are released at:
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<https://github.com/IQuestLab/unireason-med>.
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## Intended Use and Limitations
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- **Intended use:** research on medical multimodal reasoning, visual grounding, and 2D-to-3D
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boxes are reasoning aids, not validated localization. Always involve qualified medical
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professionals for any health-related decision.
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## License
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Released under the [Apache License 2.0](./LICENSE), consistent with the base model
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