README
OpenMath-Llama-2-70b-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, a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed Mixtral-8x7B 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!
See our paper for more details!
How to use the models?
Try to run inference with our models with just a few commands!
Reproducing our results
We provide all instructions to fully reproduce our results.
Improving other models
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.
Citation
If you find our work useful, please consider citing us!
@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}
}
License
The use of this model is governed by the Llama 2 Community License Agreement
Contributors
3 commits
1 commits
Similar repos
README
OpenMath-Llama-2-70b-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, a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed Mixtral-8x7B 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!
See our paper for more details!
How to use the models?
Try to run inference with our models with just a few commands!
Reproducing our results
We provide all instructions to fully reproduce our results.
Improving other models
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.
Citation
If you find our work useful, please consider citing us!
@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}
}
License
The use of this model is governed by the Llama 2 Community License Agreement
Similar repos
Linked in READMEs
Contributors
3 commits
1 commits