README.md

---
library_name: transformers
license: apache-2.0
base_model:
- Qwen/Qwen2.5-Math-7B
---

# Qwen2.5-Math-7B-Oat-Zero

## Links

- 📜 [Paper](https://github.com/sail-sg/understand-r1-zero/blob/main/understand-r1-zero.pdf)
- 💻 [GitHub](https://github.com/sail-sg/understand-r1-zero)
- 🤗 [Oat-Zero Collection](https://huggingface.co/collections/sail/oat-zero-understanding-r1-zero-like-training-67dcdb07b9f3eb05f1501c4a)

## Introduction

This model is trained by the minimalist R1-Zero recipe introduced in our paper: 
- **Algorithm**: Dr. DRPO
- **Data**: level 3-5 questions from MATH dataset
- **Base model**: [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B)
- **Template**: Qwen-Math

Evaluation results on widely used math benchmarks are shown below:

<img src="https://raw.githubusercontent.com/sail-sg/understand-r1-zero/refs/heads/main/assets/benchmark_table.png" width=100%/>

## Usage

```python
import vllm


def apply_qwen_math_template(question: str):
    return (
        "<|im_start|>system\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>user\n"
        + question
        + "<|im_end|>\n<|im_start|>assistant\n"
    )

def apply_r1_template(question: str):
    return (
        "A conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. "
        "The reasoning process is enclosed within <think> </think> and answer is enclosed within <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>.\nUser: "
        + question
        + "\nAssistant: <think>"
    )

model_name = "sail/Qwen2.5-Math-7B-Oat-Zero"

sampling_params = vllm.SamplingParams(
    n=1,
    temperature=0,
    top_p=1,
    max_tokens=3000,
)

model = vllm.LLM(
    model_name,
    max_model_len=4096,
    dtype="bfloat16",
    enable_prefix_caching=True,
)

if "Llama-3.2-3B-Oat-Zero" in model_name:
    apply_template = apply_r1_template
else:
    apply_template = apply_qwen_math_template

prompts = [
    "How many positive whole-number divisors does 196 have?"
]
prompts = list(map(apply_template, prompts))
outputs = model.generate(prompts, sampling_params)

print(outputs)
```

## Citation

```latex
@article{liu2025understanding,
  title={Understanding r1-zero-like training: A critical perspective},
  author={Liu, Zichen and Chen, Changyu and Li, Wenjun and Qi, Penghui and Pang, Tianyu and Du, Chao and Lee, Wee Sun and Lin, Min},
  journal={arXiv preprint arXiv:2503.20783},
  year={2025}
}
```