53 lines
2.4 KiB
Markdown
53 lines
2.4 KiB
Markdown
---
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datasets: open-r1/openr1-220k-math
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library_name: transformers
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model_name: OpenR1-Qwen-7B
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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license: apache-2.0
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---
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# OpenR1-Qwen-7B
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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).
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> [!NOTE]
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> 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.
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## Quick start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "open-r1/OpenR1-Qwen-7B"
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device = "cuda"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
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messages = [
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{"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
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{"role": "user", "content": prompt}
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]
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```
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## Training
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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).
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You can find the training and evaluation code at: https://github.com/huggingface/open-r1/
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| Model | MATH-500 | AIME 2024 | AIME 2025 | GPQA-D |
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|--------------------------|----------|-----------|-----------|--------|
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| DeepSeek-Distill-Qwen-7B | 93.5 | 51.3 | 35.8 | 52.4 |
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| OpenR1-Qwen-7B | 90.6 | 47.0 | 33.2 | 42.4 |
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| OpenThinker-7B | 86.4 | 31.3 | 24.6 | 39.1 | |