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metadata
base_model:
  - meta-llama/Llama-3.1-70B
datasets:
  - nvidia/OpenMathInstruct-2
language:
  - en
license: llama3.1
tags:
  - nvidia
  - math
pipeline_tag: text-generation
library_name: nemo

OpenMath2-Llama3.1-70B-nemo

NeMo checkpoint for OpenMath2-Llama3.1-70B which is obtained by finetuning Llama3.1-70B-Base with OpenMathInstruct-2.

This model is presented in the paper OpenCodeReasoning: Advancing Data Distillation for Competitive Coding.

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
+ 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
+ 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