Add pipeline tag and link to paper

README.md

@@ -1,15 +1,24 @@
 ---
-license: apache-2.0
-library_name: peft
-tags: [medical, radiology, vqa, medical-imaging, lora, diffusion-llm]
 base_model:
-  - google/diffusiongemma-26B-A4B-it
-  - google/gemma-4-26B-A4B-it
+- google/diffusiongemma-26B-A4B-it
+- google/gemma-4-26B-A4B-it
+library_name: peft
+license: apache-2.0
+pipeline_tag: image-text-to-text
+tags:
+- medical
+- radiology
+- vqa
+- medical-imaging
+- lora
+- diffusion-llm
 ---
 
 # DiffusionGemma finetunes for radiology VQA
 
-LoRA finetunes of **DiffusionGemma** (image-conditioned discrete-diffusion LLM) for radiology visual question answering, each paired with an **autoregressive Gemma-4** finetune as a controlled baseline. Mixed modalities/anatomy (VQA-RAD, SLAKE, VQA-Med: X-ray/CT/MRI, head/chest/abdomen). Judge-best checkpoint per cell.
+This repository contains LoRA finetunes of **DiffusionGemma** (image-conditioned discrete-diffusion LLM) for radiology visual question answering, each paired with an **autoregressive Gemma-4** finetune as a controlled baseline. It corresponds to the paper [Discrete Diffusion Language Models for Interactive Radiology Report Drafting](https://huggingface.co/papers/2607.01436).
+
+The dataset covers mixed modalities/anatomy (VQA-RAD, SLAKE, VQA-Med: X-ray/CT/MRI, head/chest/abdomen). Judge-best checkpoint per cell.
 
 **Code:** https://github.com/mxvp/discrete_diffusion_RRG
 
@@ -20,4 +29,4 @@ LoRA finetunes of **DiffusionGemma** (image-conditioned discrete-diffusion LLM)
 | diffusion-slake | discrete-diffusion | google/diffusiongemma-26B-A4B-it | SLAKE | 0.863 |
 | ar-slake | autoregressive |  google/gemma-4-26B-A4B-it | SLAKE | 0.817 |
 | diffusion-vqamed | discrete-diffusion | google/diffusiongemma-26B-A4B-it | VQA-Med | 0.666 |
-| ar-vqamed | autoregressive | google/gemma-4-26B-A4B-it | VQA-Med | 0.631 |
+| ar-vqamed | autoregressive | google/gemma-4-26B-A4B-it | VQA-Med | 0.631 |