---
datasets: open-r1/openr1-220k-math
library_name: transformers
model_name: OpenR1-Qwen-7B
tags:
- generated_from_trainer
- trl
- sft
licence: license
license: apache-2.0
---

# OpenR1-Qwen-7B

This is a finetune of [Qwen2.5-Math-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on [OpenR1-220k-Math](https://huggingface.co/datasets/open-r1/openr1-220k-math) (`default` split).

> [!NOTE]
> Check out [OpenR1-Distill-7B](https://huggingface.co/open-r1/OpenR1-Distill-7B) for an improved model that was trained on [open-r1/Mixture-of-Thoughts](https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts) and replicates the performance of [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) across multiple reasoning domains.

## Quick start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "open-r1/OpenR1-Qwen-7B"
device = "cuda" 

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."

messages = [
    {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
    {"role": "user", "content": prompt}
]
```

## Training 

We train the model on the `default` split of [OpenR1-220k-Math](https://huggingface.co/datasets/open-r1/openr1-220k-math) for 3 epochs. We use learning rate of 5e-5 and extend the context length from 4k to 32k, by increasing RoPE frequency to 300k. The training follows a linear learning rate schedule with a 10% warmup phase. The table below compares the performance of OpenR1-Qwen-7B to [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) and [OpenThinker-7B](https://huggingface.co/open-thoughts/OpenThinker-7B) using [lighteval](https://github.com/huggingface/open-r1/tree/main?tab=readme-ov-file#evaluating-models).

You can find the training and evaluation code at: https://github.com/huggingface/open-r1/

| Model                    | MATH-500 | AIME 2024 | AIME 2025 | GPQA-D |
|--------------------------|----------|-----------|-----------|--------|
| DeepSeek-Distill-Qwen-7B | 93.5     | 51.3      | 35.8      | 52.4   |
| OpenR1-Qwen-7B           | 90.6     | 47.0      | 33.2      | 42.4   |
| OpenThinker-7B           | 86.4     | 31.3      | 24.6      | 39.1   |