Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+datasets:
+- rubricreward/R3-Dataset-14K
+base_model:
+- microsoft/Phi-4-reasoning-plus
+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-Phi-4-reasoning-plus-14k
+
+R3-Phi-4-reasoning-plus-14k 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/Qwen3-14B
+
+### Model Sources
+
+- **Project Page:** https://rubricreward.github.io
+- **Repository:** https://github.com/rubricreward/r3
+- **Paper:** https://arxiv.org/abs/2505.13388
+
+## Using the Model
+
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+model_path = "rubricreward/R3-Phi-4-reasoning-plus-14k"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+sampling_params = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8192, min_p=0, top_k=20)
+
+llm = LLM(
+  model=model_path,
+  dtype="bfloat16",
+  max_model_len=10000,
+  tensor_parallel_size=2,
+  gpu_memory_utilization=0.9,
+  enforce_eager=True,
+)
+
+messages: list[dict[str, str]] = [
+  {'content': "Evaluate the response based on the given task, input, response, and evaluation rubric. Provide a fair and detailed assessment following the rubric...", 'role': 'user'}
+]
+
+list_text = tokenizer.apply_chat_template(
+  messages,
+  tokenize=False,
+  add_generation_prompt=True,
+  enable_thinking=True # Switch between thinking and non-thinking modes. 
+)
+
+outputs = llm.generate(list_text, sampling_params)
+```
+
+## 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}
+}
+```