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---
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tags:
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- math
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license: apache-2.0
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datasets:
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- oumi-ai/MetaMathQA-R1
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language:
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- en
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metrics:
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- accuracy
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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pipeline_tag: text-generation
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---
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[](https://github.com/oumi-ai/oumi)
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[](https://github.com/oumi-ai/oumi)
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[](https://oumi.ai/docs/en/latest/index.html)
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[](https://oumi.ai/blog)
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[](https://discord.gg/oumi)
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# oumi-ai/MiniMath-R1-1.5B
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<!-- Provide a quick summary of what the model is/does. -->
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Supervised fine-tune of [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) using [oumi-ai/MetaMathQA-R1](https://huggingface.co/datasets/oumi-ai/MetaMathQA-R1).
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Achieves **44.4% accuracy on MMLU-Pro-Math**, the **highest of any model with <=1.5B parameters**.
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Improves the base model's accuracy by **+6 points**.
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- **Developed by:** [Oumi AI](https://oumi.ai/)
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- **Model type:** Small Language Model
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- **Language(s) (NLP):** English
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- **License:** [Apache 2.0](https://opensource.org/license/apache-2-0)
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- **Finetuned from model:** [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
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- **Demo:** [Fine-Tuning Notebook](https://github.com/oumi-ai/oumi/blob/307436bd98706cb9ce7b0bbf31204770af2b7c8c/notebooks/Oumi%20-%20MiniMath-R1-1.5B.ipynb)
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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Use as a conversational assistant for solving math problems with an exposed thought process.
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## Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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Smaller LLMs have limited capabilities and should be used with caution. Avoid using this model for purposes outside of mathematics.
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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This model was finetuned with DeepSeek-R1 data on top of an R1-distill model, so any biases or risks associated with those models may be present.
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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Training data: [oumi-ai/MetaMathQA-R1](https://huggingface.co/datasets/oumi-ai/MetaMathQA-R1)
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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Training notebook: [Fine-Tuning Notebook](https://github.com/oumi-ai/oumi/blob/307436bd98706cb9ce7b0bbf31204770af2b7c8c/notebooks/Oumi%20-%20MiniMath-R1-1.5B.ipynb)
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- **Hardware Type:** H100
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- **Hours used:** 0.8 (0.1 * 8 GPUs)
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- **Cloud Provider:** Google Cloud Platform
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- **Compute Region:** us-east5
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- **Carbon Emitted:** 0.07 kg
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## Citation
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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```
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@misc{miniMathR1_2025,
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author = {Jeremiah Greer},
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title = {MiniMath-R1-1.5B},
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month = {February},
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year = {2025},
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url = {https://huggingface.co/oumi-ai/MiniMath-R1-1.5B}
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}
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@software{oumi2025,
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author = {Oumi Community},
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title = {Oumi: an Open, End-to-end Platform for Building Large Foundation Models},
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month = {January},
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year = {2025},
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url = {https://github.com/oumi-ai/oumi}
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}
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``` |
