Update README.md

README.md

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-# Med-R1
-Med-R1 is a reinforcement learning (RL)-enhanced vision-language model (VLM) designed for medical reasoning across 8 imaging modalities (CT, MRI, Ultrasound, Dermoscopy, Fundus Photography, Optical Coherence Tomography (OCT), Microscopy, and X-ray) and 5 key tasks (modality recognition, anatomy identification, disease diagnosis, lesion grading, and biological attribute analysis). Using Group Relative Policy Optimization (GRPO), Med-R1 improves generalization and trustworthiness, surpassing Qwen2-VL-2B by 29.94% and even outperforming the much larger Qwen2-VL-72B. Our model checkpoints provide researchers with a powerful tool for advancing medical AI with RL-driven enhancements.
-
-## Start with the Model
-
-
-## Citation
-
-
 ---
 license: apache-2.0
 datasets:
@@ -17,4 +8,13 @@ metrics:
 - accuracy
 base_model:
 - Qwen/Qwen2-VL-2B-Instruct
----
+new_version: Qwen/Qwen2-VL-2B-Instruct
+pipeline_tag: visual-question-answering
+---
+# Med-R1
+Med-R1 is a reinforcement learning (RL)-enhanced vision-language model (VLM) designed for medical reasoning across 8 imaging modalities (CT, MRI, Ultrasound, Dermoscopy, Fundus Photography, Optical Coherence Tomography (OCT), Microscopy, and X-ray) and 5 key tasks (modality recognition, anatomy identification, disease diagnosis, lesion grading, and biological attribute analysis). Using Group Relative Policy Optimization (GRPO), Med-R1 improves generalization and trustworthiness, surpassing Qwen2-VL-2B by 29.94% and even outperforming the much larger Qwen2-VL-72B. Our model checkpoints provide researchers with a powerful tool for advancing medical AI with RL-driven enhancements.
+
+## Start with the Model
+
+
+## Citation