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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+  - medical
+  - multimodal
+  - grounding
+  - report-generation
+  - radiology
+  - clinical-reasoning
+  - mri
+  - ct
+  - histopathology
+  - x-ray
+  - fundus
+---
+
+# MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images
+
+[![Paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2602.06965)
+[![Model](https://img.shields.io/badge/🤗-MedMO--8B-blue)](https://huggingface.co/MBZUAI/MedMO-8B)
+[![Model](https://img.shields.io/badge/🤗-MedMO--4B-blue)](https://huggingface.co/MBZUAI/MedMO-4B)
+[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
+
+
+<p align="center">
+  <img src="MedMO-logo.png" alt="MedMO Logo" width="300"/>
+</p>
+
+
+**MedMO** is a powerful open-source multimodal foundation model designed for comprehensive medical image understanding and grounding. Built on Qwen3-VL architecture and trained on 26M+ diverse medical samples across 45 datasets, MedMO achieves state-of-the-art performance across multiple medical imaging tasks.
+
+## 🎯 Capabilities
+
+MedMO excels at a comprehensive range of medical imaging tasks:
+
+- **Visual Question Answering (VQA)**: Answer complex questions about medical images across radiology, pathology, ophthalmology, and dermatology
+- **Text-Based Medical QA**: Clinical reasoning and medical knowledge question answering
+- **Radiology Report Generation**: Generate detailed, clinically accurate radiology reports from medical images
+- **Disease Localization with Bounding Boxes**: Precise spatial detection and localization of pathological findings
+- **Anatomical Grounding**: Spatial localization and grounding of anatomical structures
+- **Clinical Reasoning**: Step-by-step diagnostic reasoning and clinical decision support
+- **Diagnostic Classification**: Multi-class disease classification across diverse imaging modalities
+- **Spatial Object Detection**: Fine-grained detection in microscopy, pathology slides, and cellular imaging
+- **Medical Report Summarization**: Extract and summarize key clinical findings from complex medical reports
+
+### Supported Modalities
+- Radiology (X-ray, CT, MRI, Ultrasound)
+- Pathology & Microscopy
+- Ophthalmology (Fundus, OCT)
+- Dermatology
+- Nuclear Medicine (PET, SPECT)
+
+## 🚀 Quick Start
+
+### Installation
+
+```bash
+pip install transformers torch qwen-vl-utils
+```
+
+### Basic Usage
+
+```python
+from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+import torch
+
+# Load model
+model = Qwen3VLForConditionalGeneration.from_pretrained(
+    "MBZUAI/MedMO-8B",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+)
+
+processor = AutoProcessor.from_pretrained("MBZUAI/MedMO-8B")
+
+# Prepare your input
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "path/to/medical/image.png",
+            },
+            {"type": "text", "text": "What abnormalities are present in this chest X-ray?"},
+        ],
+    }
+]
+
+# Process and generate
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to(model.device)
+
+# Generate output
+generated_ids = model.generate(**inputs, max_new_tokens=512)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text[0])
+```
+
+### Example: Disease Localization with Bounding Boxes
+
+```python
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "chest_xray.png"},
+            {"type": "text", "text": "Detect and localize all abnormalities in this image."},
+        ],
+    }
+]
+# Output: "Fractures <box>[[156, 516, 231, 607], [240, 529, 296, 581]]</box>"
+```
+
+### Example: Report Generation
+
+```python
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "ct_scan.png"},
+            {"type": "text", "text": "Generate a detailed radiology report for this CT scan."},
+        ],
+    }
+]
+# MedMO generates comprehensive clinical reports with findings and impressions
+```
+
+## 🏗️ Model Architecture
+
+MedMO is built on **Qwen3-VL-8B-Instruct** and trained through a 4-stage progressive pipeline:
+
+1. **Stage 1 - General Medical SFT**: Large-scale training on 18.5M image-text pairs for foundational medical understanding
+2. **Stage 2 - High-Resolution & Grounding**: Training on 3M curated samples at 1280×1280 resolution for spatial localization
+3. **Stage 3 - Instruction Tuning**: Fine-tuning on 4.3M instruction-response pairs for task-specific alignment
+4. **Stage 4 - Reinforcement Learning**: GRPO training with verifiable rewards (label accuracy, bbox IoU) for enhanced grounding
+
+**Total Training Data**: 26M+ samples from 45 medical datasets spanning diverse modalities and anatomical systems.
+
+
+
+For detailed benchmark results, please refer to our paper.
+
+## 📄 Citation
+
+If you use MedMO in your research, please cite our paper:
+
+```bibtex
+@article{deria2026medmo,
+  title={MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images},
+  author={Deria, Ankan and Kumar, Komal and Dukre, Adinath Madhavrao and Segal, Eran and Khan, Salman and Razzak, Imran},
+  journal={arXiv preprint arXiv:2602.06965},
+  year={2026}
+}
+```
+
+
+## 📜 License
+
+This project is licensed under the **Apache License 2.0** - see the [LICENSE](LICENSE) file for details.
+