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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:
+  - 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
+processes either a 2D image or a slice-serialized 3D volume, generating interleaved textual
+reasoning and localized visual evidence through shared bounding-box syntax and region-token
+injection under a common grounded reasoning policy.
+
+- **Base model:** [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
+- **Training data:** [IQuestLab/UniReason-Med-Data](https://huggingface.co/datasets/IQuestLab/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 (a
+grounded chain-of-thought, GCoT, interface). The same shared interface is applied to 2D images
+and to 3D volumes serialized as ordered slice sequences, which allows grounded supervision
+collected on plentiful 2D data to transfer to 3D reasoning.
+
+A central result of the paper is that joint 2D+3D grounded supervision improves 3D reasoning
+compared with 3D-only training under matched schedules, while the shared grounding interface
+also benefits 2D tasks.
+
+## Training
+
+The model is built with a two-stage recipe:
+
+1. **Supervised fine-tuning (SFT)** on the UniMed-CoT dataset — 220K grounded chain-of-thought
+   samples (170K 2D + 50K 3D) with interleaved textual reasoning and grounded visual evidence.
+   Vision tower and the multimodal projector are frozen; the language model is fully fine-tuned.
+2. **Reinforcement learning (GRPO)** with outcome-level rewards. RL uses answer-correctness and
+   format rewards rather than ground-truth localization-overlap rewards such as IoU or Dice.
+
+This checkpoint is the merged Hugging Face model exported from the GRPO stage.
+
+Training code (LLaMA-Factory for SFT, verl for GRPO) and configs are released at:
+<https://github.com/IQuestLab/unireason-med>.
+
+## Usage
+
+```python
+from transformers import AutoModelForImageTextToText, AutoProcessor
+from PIL import Image
+
+model_id = "IQuestLab/UniReason-Med"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")
+
+image = Image.open("medical_image.png")
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": "What is the most likely diagnosis? Reason step by step."},
+        ],
+    }
+]
+prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = processor(text=[prompt], images=[image], return_tensors="pt").to(model.device)
+output = model.generate(**inputs, max_new_tokens=1024)
+print(processor.batch_decode(output, skip_special_tokens=True)[0])
+```
+
+The model produces interleaved reasoning with bounding boxes over the input image. Reproducing
+the full grounded crop-and-continue loop (crop the predicted region and feed it back as visual
+input) follows the agent/rollout logic in the released training code.
+
+## Intended Use and Limitations
+
+- **Intended use:** research on medical multimodal reasoning, visual grounding, and 2D-to-3D
+  transfer. Suitable for academic benchmarking and method development.
+- **Out of scope:** UniReason-Med is a research artifact and is **not** a medical device. It must
+  **not** be used for clinical diagnosis, treatment decisions, or any real patient care.
+- **Limitations:** outputs may be incorrect, incomplete, or biased; performance depends on
+  imaging modality, anatomy, and distribution shift from the training data. Predicted bounding
+  boxes are reasoning aids, not validated localization. Always involve qualified medical
+  professionals for any health-related decision.
+
+## Data Notice
+
+The public training-data release keeps 3D examples text-only and does not redistribute 3D image
+data, because those samples are derived from M3D whose underlying image sources include
+Radiopaedia and may require separate authorization. See the
+[dataset card](https://huggingface.co/datasets/IQuestLab/UniReason-Med-Data) for details.
+
+## License
+
+Released under the [Apache License 2.0](./LICENSE), consistent with the base model
+Qwen2.5-VL-7B-Instruct. Note the research-only intended use and the medical-use limitations above.
+
+## Citation
+
+If you use this model, please cite the UniReason-Med paper:
+
+```bibtex
+@article{unireasonmed,
+  title  = {UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA},
+  author = {UniReason-Med Team},
+  year   = {2025}
+}
+```