OpenMath models were designed to solve mathematical problems by integrating text-based reasoning with code blocks executed by Python interpreter. The models were trained on OpenMathInstruct-1, a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed Mixtral-8x7B model.

The pipeline we used to produce these models is fully open-sourced!

See our paper for more details!

Try to run inference with our models with just a few commands!

We provide all instructions to fully reproduce our results.

To improve other models or to learn more about our code, read through the docs below.

In our pipeline we use NVIDIA NeMo, an end-to-end, cloud-native framework to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI.

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Quantization of Model nvidia/OpenMath-Mistral-7B-v0.1-hf. Created using llm-quantizer Pipeline