Downloads · 30 days
0
OpenMath2-Llama3.1-70B-nemo
nvidia/OpenMath2-Llama3.1-70B-nemo
OpenMath2-Llama3.1-70B-nemo is a machine learning model from nvidia. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as llama3.1.
NeMo checkpoint for OpenMath2-Llama3.1-70B which is obtained by finetuning Llama3.1-70B-Base with OpenMathInstruct-2.
Access
Public
Updated Nov 25, 2024
Repo size
141 GB
Likes
10
Public
Repo makeup
Click a slice to open those files.
.098.9 GB · 70%
At a glance
- License
- llama3.1
- Access
- Public
- Created
- Oct 1, 2024
- Updated
- Nov 25, 2024
- SHA
- 23f02027
Base models
Datasets
Model card
From the Hugging Face model README
OpenMath2-Llama3.1-70B-nemo
NeMo checkpoint for OpenMath2-Llama3.1-70B which is obtained by finetuning Llama3.1-70B-Base with OpenMathInstruct-2.
The model outperforms Llama3.1-70B-Instruct on MATH by 3.9%.
| 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!
See our paper 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 to run inference or finetune it. We also release a HuggingFace checkpoint and provide easy instructions on how to convert between different formats or run inference with these models using our codebase.
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
- License
- llama3.1
- Languages
- en
- Created
- Oct 1, 2024
- Updated
- Nov 25, 2024