---
license: apache-2.0
language:
- en
datasets:
- rubricreward/R3-Dataset-14K
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
---

<img alt="R3 Logo" src="https://cdn-avatars.huggingface.co/v1/production/uploads/651803f834c26962535eb022/hj3UEN9_9wlkmvMfUY1OL.png" width="150px">

# R3-Qwen3-8B-14k

R3-Qwen3-8B-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/Qwen3-8B

### 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-Qwen3-8B-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}
}
```