Improve model card: Correct pipeline tag, link to code, and project page (#1)

- Improve model card: Correct pipeline tag, link to code, and project page (031c4813290e1105c372a5cd336e3e965e529065)
- Update README.md (bd9eb3ded38209d6ba1960767425fc0568b5d82a)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,16 +1,17 @@
 ---
-library_name: transformers
-license: apache-2.0
-language:
-- en
 base_model:
 - Qwen/Qwen2.5-VL-7B-Instruct
-pipeline_tag: visual-question-answering
+language:
+- en
+library_name: transformers
+license: apache-2.0
+pipeline_tag: image-text-to-text
 tags:
 - multi-modal
 - large-language-model
 ---
 
+```markdown
 <p align="center">
     <img src="https://github.com/LengSicong/MMR1/blob/main/assets/logo.png?raw=true" width="150" style="margin-bottom: 0.2;"/>
 <p>
@@ -22,6 +23,10 @@ MMR1: Advancing the Frontiers of Multimodal Reasoning</a></h3>
 ## 📰 News
 * **[2025.03.11]**  🔥🔥 Release MMR1-Math-v0, achieving SOTA with only 6k data!
 
+## Links
+Code: https://github.com/LengSicong/MMR1
+
+This model was presented in the paper [LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL](https://arxiv.org/abs/2503.07536). Code can be found at https://github.com/LengSicong/MMR1
 
 ## Model Description
 MMR1-Math-v0-7B is a Large Multimodal Model specialized in mathematical tasks. Remarkably, MMR1-Math-v0-7B achieves state-of-the-art performance among open-source 7B multimodal models, competing effectively even against proprietary models with significantly larger parameter sizes—all trained using only 6k carefully curated data instances.
@@ -177,4 +182,5 @@ If you find MMR1 useful for your research and applications, please cite using th
   year={2025},
   howpublished={\url{https://github.com/LengSicong/MMR1}},
 }
+```
 ```