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---
license: llama3.1
base_model:
- meta-llama/Llama-3.1-8B
datasets:
- nvidia/OpenMathInstruct-2
language:
- en
tags:
- nvidia
- math
---
# OpenMath2-Llama3.1-8B-nemo
[NeMo](https://github.com/NVIDIA/NeMo) checkpoint for [OpenMath2-Llama3.1-8B](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B) is obtained by finetuning [Llama3.1-8B-Base](https://huggingface.co/meta-llama/Llama-3.1-8B) with [OpenMathInstruct-2](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2).
The model outperforms [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on all the popular math benchmarks we evaluate on, especially on [MATH](https://github.com/hendrycks/math) by 15.9%.
<!-- <p align="center">
<img src="scaling_plot.jpg" width="350"><img src="math_level_comp.jpg" width="350">
</p> -->
<style>
.image-container {
display: flex;
justify-content: center;
align-items: center;
gap: 20px;
}
.image-container img {
width: 350px;
height: auto;
}
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<div class="image-container">
<img src="scaling_plot.jpg" title="Performance of Llama-3.1-8B-Instruct as it is trained on increasing proportions of OpenMathInstruct-2">
<img src="math_level_comp.jpg" title="Comparison of OpenMath2-Llama3.1-8B vs. Llama-3.1-8B-Instruct across MATH levels">
</div>
| 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](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B-nemo) \| [HF](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B)) | 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](https://huggingface.co/nvidia/OpenMath2-Llama3.1-70B-nemo) \| [HF](https://huggingface.co/nvidia/OpenMath2-Llama3.1-70B)) | 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!
- [Code](https://github.com/NVIDIA/NeMo-Skills)
- [Models](https://huggingface.co/collections/nvidia/openmath-2-66fb142317d86400783d2c7b)
- [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2)
See our [paper](https://arxiv.org/abs/2410.01560) to learn more details!
# How to use the models?
Our models are trained with the same "chat format" as Llama3.1-instruct models (same system/user/assistant tokens).
Please note that these models have not been instruction tuned on general data and thus might not provide good answers outside of math domain.
This is a NeMo checkpoint, so you need to use [NeMo Framework](https://github.com/NVIDIA/NeMo) to run inference or finetune it.
We also release a [HuggingFace checkpoint](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B) and provide easy instructions on how to
[convert between different formats](https://nvidia.github.io/NeMo-Skills/pipelines/checkpoint-conversion/) or
[run inference](https://nvidia.github.io/NeMo-Skills/basics/inference/) with these models using our codebase.
# Reproducing our results
We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathinstruct2/) to fully reproduce our results.
## Citation
If you find our work useful, please consider citing us!
```bibtex
@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](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE), [acceptable use policy](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md) and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/)
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