Add metadata for pipeline tag, library name and link to paper (#1)

- Add metadata for pipeline tag, library name and link to paper (243ff1ab6b49c940ae318cb05405268df38c93c4)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,65 +1,78 @@
----
-license: cc-by-nc-sa-4.0
----
-
-# 🌤️ Introduction
-
-While Vision Language Models (VLMs) show advancing reasoning capabilities, their application in meteorology is constrained by a domain gap and a reasoning faithfulness gap. Mainstream Reinforcement Fine-Tuning (RFT) can induce Self-Contradictory Reasoning (Self-Contra), where the reasoning process contradicts the final answer, which is unacceptable in this high-stakes domain.
-
-To address these challenges, we construct WeatherQA, a multimodal multiple-choice benchmark for meteorology comprising 15,400 entries that cover four themes and seven imaging modality tasks. We propose Logically Consistent Reinforcement Fine-Tuning (LoCo-RFT), which introduces a logical consistency reward to resolve Self-Contra. Based on this paradigm and WeatherQA, we present Weather-R1, the first reasoning VLM with logical faithfulness in meteorology, to the best of our knowledge. Weather-R1 (7B) achieves 52.9% accuracy on WeatherQA, a 9.8 percentage point gain over the baseline model Qwen2.5-VL-7B; it surpasses Supervised Fine-Tuning and RFT baselines, exceeds the original Qwen2.5-VL-32B, and improves out-of-domain ScienceQA performance by 4.98 percentage points.
-
-<div align="center">
-  <img src="./asserts/Case_Study.png" width="70%" />
-  <p><em>Response Comparison.</em></p>
-</div>
-
-# 🗂️ Folder Structure
-This repository provides model checkpoints organized by training strategy and task:
-
-```
-Weather-R1/
-├─ LoCo-RFT/                 # Weather-R1 checkpoints
-│  ├─ WeatherQA-500hPa/
-│  ├─ WeatherQA-850hPa/
-│  ├─ WeatherQA-Land/
-│  ├─ WeatherQA-Max-Temp/
-│  ├─ WeatherQA-Min-Temp/
-│  ├─ WeatherQA-Phenom/
-│  └─ WeatherQA-Rain/
-├─ RFT/                      # Standard RFT checkpoints
-│  ├─ WeatherQA-500hPa/
-│  ├─ WeatherQA-850hPa/
-│  ├─ WeatherQA-Land/
-│  ├─ WeatherQA-Max-Temp/
-│  ├─ WeatherQA-Min-Temp/
-│  ├─ WeatherQA-Phenom/
-│  └─ WeatherQA-Rain/
-└─ asserts/                  # Figures used in README
-```
-
-Each task folder contains HuggingFace-style model files such as `config.json`,
-`tokenizer.json`, and sharded weights like `model-00001-of-00004.safetensors`.
-
-# 🚀 Training and Evaluation
-
-Please refer to our official repository: [Weather-R1](https://github.com/Marcowky/Weather-R1)
-
-# 🙏 Acknowledgements
-
-Training code is built on [EasyR1](https://github.com/hiyouga/EasyR1).
-
-# 📝 Citation
-
-If you use Weather-R1 resources, please cite the following paper:
-
-```bibtex
-@misc{wu2026weatherr1logicallyconsistentreinforcement,
-      title={Weather-R1: Logically Consistent Reinforcement Fine-Tuning for Multimodal Reasoning in Meteorology}, 
-      author={Kaiyu Wu and Pucheng Han and Hualong Zhang and Naigeng Wu and Keze Wang},
-      year={2026},
-      eprint={2601.14044},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2601.14044}, 
-}
-```
+---
+license: cc-by-nc-sa-4.0
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- meteorology
+- reasoning
+- vlm
+- weather
+---
+
+# 🌤️ Weather-R1: Multimodal Reasoning in Meteorology
+
+This repository contains the checkpoints for **Weather-R1**, as presented in the paper [Weather-R1: Logically Consistent Reinforcement Fine-Tuning for Multimodal Reasoning in Meteorology](https://huggingface.co/papers/2601.14044).
+
+[**Paper (ArXiv)**](https://arxiv.org/abs/2601.14044) | [**Code (GitHub)**](https://github.com/Marcowky/Weather-R1)
+
+# 🌤️ Introduction
+
+While Vision Language Models (VLMs) show advancing reasoning capabilities, their application in meteorology is constrained by a domain gap and a reasoning faithfulness gap. Mainstream Reinforcement Fine-Tuning (RFT) can induce Self-Contradictory Reasoning (Self-Contra), where the reasoning process contradicts the final answer, which is unacceptable in this high-stakes domain.
+
+To address these challenges, we construct WeatherQA, a multimodal multiple-choice benchmark for meteorology comprising 15,400 entries that cover four themes and seven imaging modality tasks. We propose Logically Consistent Reinforcement Fine-Tuning (LoCo-RFT), which introduces a logical consistency reward to resolve Self-Contra. Based on this paradigm and WeatherQA, we present Weather-R1, the first reasoning VLM with logical faithfulness in meteorology, to the best of our knowledge. Weather-R1 (7B) achieves 52.9% accuracy on WeatherQA, a 9.8 percentage point gain over the baseline model Qwen2.5-VL-7B; it surpasses Supervised Fine-Tuning and RFT baselines, exceeds the original Qwen2.5-VL-32B, and improves out-of-domain ScienceQA performance by 4.98 percentage points.
+
+<div align="center\">
+  <img src="https://huggingface.co/Marcowky/Weather-R1/resolve/main/asserts/Case_Study.png" width="70%" />
+  <p><em>Response Comparison.</em></p>
+</div>
+
+# 🗂️ Folder Structure
+This repository provides model checkpoints organized by training strategy and task:
+
+```
+Weather-R1/
+├─ LoCo-RFT/                 # Weather-R1 checkpoints
+│  ├─ WeatherQA-500hPa/
+│  ├─ WeatherQA-850hPa/
+│  ├─ WeatherQA-Land/
+│  ├─ WeatherQA-Max-Temp/
+│  ├─ WeatherQA-Min-Temp/
+│  ├─ WeatherQA-Phenom/
+│  └─ WeatherQA-Rain/
+├─ RFT/                      # Standard RFT checkpoints
+│  ├─ WeatherQA-500hPa/
+│  ├─ WeatherQA-850hPa/
+│  ├─ WeatherQA-Land/
+│  ├─ WeatherQA-Max-Temp/
+│  ├─ WeatherQA-Min-Temp/
+│  ├─ WeatherQA-Phenom/
+│  └─ WeatherQA-Rain/
+└─ asserts/                  # Figures used in README
+```
+
+Each task folder contains HuggingFace-style model files such as `config.json`,
+`tokenizer.json`, and sharded weights like `model-00001-of-00004.safetensors`.
+
+# 🚀 Training and Evaluation
+
+Please refer to our official repository: [Weather-R1](https://github.com/Marcowky/Weather-R1)
+
+# 🙏 Acknowledgements
+
+Training code is built on [EasyR1](https://github.com/hiyouga/EasyR1).
+
+# 📝 Citation
+
+If you use Weather-R1 resources, please cite the following paper:
+
+```bibtex
+@misc{wu2026weatherr1logicallyconsistentreinforcement,
+      title={Weather-R1: Logically Consistent Reinforcement Fine-Tuning for Multimodal Reasoning in Meteorology}, 
+      author={Kaiyu Wu and Pucheng Han and Hualong Zhang and Naigeng Wu and Keze Wang},
+      year={2026},
+      eprint={2601.14044},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2601.14044}, 
+}
+```