初始化项目，由ModelHub XC社区提供模型

Model: taki555/Qwen3-30B-A3B-Instruct-2507-Art
Source: Original Platform

README.md

@@ -0,0 +1,34 @@
+---
+base_model:
+- Qwen/Qwen3-30B-A3B-Instruct-2507
+datasets:
+- taki555/DeepScaleR-Easy
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Qwen3-30B-A3B-Art
+
+This is the Chain-of-Thought (CoT) efficient version of the **Qwen3-30B-A3B-Instruct-2507** model, introduced in the paper [The Art of Efficient Reasoning: Data, Reward, and Optimization](https://huggingface.co/papers/2602.20945).
+
+The model is designed to generate short yet accurate reasoning trajectories, reducing computational overhead while maintaining high performance. It was trained on the [DeepScaleR-Easy](https://huggingface.co/datasets/taki555/DeepScaleR-Easy) dataset using reward shaping with Reinforcement Learning (RL).
+
+## Project Resources
+- **Project Page:** [https://wutaiqiang.github.io/project/Art](https://wutaiqiang.github.io/project/Art)
+- **Paper:** [arXiv:2602.20945](https://huggingface.co/papers/2602.20945)
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@inproceedings{wu2026art,
+  title={The Art of Efficient Reasoning: Data, Reward, and Optimization},
+  author={Taiqiang Wu and Zenan Xu and Bo Zhou and Ngai Wong},
+  year={2026},
+  url={https://arxiv.org/pdf/2602.20945}
+}
+```