license: llama3.1
base_model:
- meta-llama/Llama-3.1-8B
datasets:
- nvidia/OpenMathInstruct-2
language:
- en
tags:
- nvidia
- math
OpenMath2-Llama3.1-8B
OpenMath2-Llama3.1-8B is obtained by finetuning Llama3.1-8B-Base with OpenMathInstruct-2.
The model outperforms Llama3.1-8B-Instruct on all the popular math benchmarks we evaluate on, especially on MATH by 15.9%.
| Model | GSM8K | MATH | AMC 2023 | AIME 2024 | Omni-MATH |
|---|---|---|---|---|---|
| Llama3.1-8B-Instruct | 84.5 | 51.9 | 9/40 | 2/30 | 12.7 |
| OpenMath2-Llama3.1-8B (nemo | HF) | 91.7 | 67.8 | 16/40 | 3/30 | 22.0 |
| + majority@256 | 94.1 | 76.1 | 23/40 | 3/30 | 24.6 |
| Llama3.1-70B-Instruct | 95.8 | 67.9 | 19/40 | 6/30 | 19.0 |
| OpenMath2-Llama3.1-70B (nemo | HF) | 94.9 | 71.9 | 20/40 | 4/30 | 23.1 |
| + majority@256 | 96.0 | 79.6 | 24/40 | 6/30 | 27.6 |
The pipeline we used to produce the data and models is fully open-sourced!
How to use the models?
Our models are fully compatible with Llama3.1-instruct format, so you should be able to just replace an existing Llama3.1 checkpoint and use it in the same way. Please note that these models have not been instruction tuned and might not provide good answers outside of math domain.
If you don't know how to use Llama3.1 models, we provide convenient instructions in our repo.
Reproducing our results
We provide all instructions to fully reproduce our results.
Citation
If you find our work useful, please consider citing us!
@article{toshniwal2024openmath2,
title = {OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data},
author = {Shubham Toshniwal and Wei Du and Ivan Moshkov and Branislav Kisacanin and Alexan Ayrapetyan and Igor Gitman},
year = {2024},
journal = {arXiv preprint arXiv:2410.01560}
}
Terms of use
By accessing this model, you are agreeing to the LLama 3.1 terms and conditions of the license, acceptable use policy and Meta’s privacy policy