Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+datasets:
+- rubricreward/R3-Dataset-4K
+base_model:
+- Qwen/Qwen3-8B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- lora
+---
+
+<img alt="R3 Logo" src="https://cdn-avatars.huggingface.co/v1/production/uploads/651803f834c26962535eb022/hj3UEN9_9wlkmvMfUY1OL.png" width="150px">
+
+# R3-Qwen3-8B-LoRA-4k
+
+R3-Qwen3-8B-LoRA-4k is part of the R3 family, a series of **R**obust **R**ubric-Agnostic **R**eward Models. 
+We perform SFT on the Qwen3 model family on the 4B, 8B, and 14B scales as well as on Phi-4-reasoning plus.
+Check out [our paper](https://arxiv.org/abs/2505.13388) for more information!
+
+
+## Model description
+
+- **Model type:** A reward model trained on a curated R3 dataset collected from 45 diverse sources that covers
+tasks such as classification, preference optimization, and question answering. Each example in the dataset contains an instruction and task description, input, response(s),
+evaluation rubrics, and a score along with the corresponding reasoning.
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** Qwen/Qwen3-8B
+
+### Model Sources
+
+- **Project Page:** https://rubricreward.github.io
+- **Repository:** https://github.com/rubricreward/r3
+- **Paper:** https://arxiv.org/abs/2505.13388
+
+## License and use
+
+R3 is licensed under the Apache 2.0 license.
+
+## Citation
+
+```bibtex
+@article{anugraha2025r3,
+  title={R3: Robust Rubric-Agnostic Reward Models},
+  author={Anugraha, David and Tang, Zilu and Miranda, Lester James V. and Zhao, Hanyang and Farhansyah, Mohammad Rifqi and Kuwanto, Garry and Wijaya, Derry and Winata, Genta Indra},
+  journal={arXiv preprint arXiv:2505.13388},
+  year={2025}
+}
+```