# OpenMath-Mistral-7B-v0.1-hf 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](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1), a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) model.

	greedy		majority@50
model	GSM8K	MATH	GMS8K	MATH
OpenMath-CodeLlama-7B (nemo \| HF)	75.9	43.6	84.8	55.6
OpenMath-Mistral-7B (nemo \| HF)	80.2	44.5	86.9	57.2
OpenMath-CodeLlama-13B (nemo \| HF)	78.8	45.5	86.8	57.6
OpenMath-CodeLlama-34B (nemo \| HF)	80.7	48.3	88.0	60.2
OpenMath-Llama2-70B (nemo \| HF)	84.7	46.3	90.1	58.3
OpenMath-CodeLlama-70B (nemo \| HF)	84.6	50.7	90.8	60.4

The pipeline we used to produce these models is fully open-sourced! - [Code](https://github.com/Kipok/NeMo-Skills) - [Models](https://huggingface.co/collections/nvidia/openmath-65c5619de2ba059be0775014) - [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1) See our [paper](https://arxiv.org/abs/2402.10176) for more details! # How to use the models? Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands! # Reproducing our results We provide [all instructions](https://github.com/Kipok/NeMo-Skills/blob/main/docs/reproducing-results.md) to fully reproduce our results. # Improving other models To improve other models or to learn more about our code, read through the docs below. - [NeMo-Skills Pipeline](https://github.com/Kipok/NeMo-Skills) - [Generating synthetic data](https://github.com/Kipok/NeMo-Skills/blob/main/docs/synthetic-data-generation.md) - [Finetuning models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/finetuning.md) - [Evaluating models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/evaluation.md) In our pipeline we use [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/), 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. # Citation If you find our work useful, please consider citing us! ```bibtex @article{toshniwal2024openmath, title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset}, author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman}, year = {2024}, journal = {arXiv preprint arXiv: Arxiv-2402.10176} } ```