license: llama2
datasets:
- nvidia/OpenMathInstruct-1
language:
- en
library_name: transformers
tags:
- nvidia
- code
- math
OpenMath-CodeLlama-7b-Python
Description:
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.
| Model | Size | GSM8K | MATH |
|---|---|---|---|
| GPT-4 [1] | - | 94.4 | 56.2 |
| GPT-4 + code [2] | - | 92.9 | 69.7 |
| OpenMath-CodeLlama-7B (nemo, HF) | 7B | 75.9 | 43.6 |
| OpenMath-CodeLlama-7B + self-consistency (k=50) | 7B | 84.8 | 55.6 |
| OpenMath-Mistral-7B (nemo, HF) | 7B | 80.2 | 44.5 |
| OpenMath-Mistral-7B + self-consistency (k=50) | 7B | 86.9 | 57.2 |
| OpenMath-CodeLlama-13B (nemo, HF) | 13B | 78.8 | 45.5 |
| OpenMath-CodeLlama-13B + self-consistency (k=50) | 13B | 86.8 | 57.6 |
| OpenMath-CodeLlama-34B (nemo, HF) | 34B | 80.7 | 48.3 |
| OpenMath-CodeLlama-34B + self-consistency (k=50) | 34B | 88.0 | 60.2 |
| OpenMath-Llama2-70B (nemo, HF) | 70B | 84.7 | 46.3 |
| OpenMath-Llama2-70B + self-consistency (k=50) | 70B | 90.1 | 58.3 |
| OpenMath-CodeLlama-70B (nemo, HF) | 70B | 84.6 | 50.7 |
| OpenMath-CodeLlama-70B + self-consistency (k=50) | 70B | 90.8 | 60.4 |
The pipeline we used to produce these models is fully open-sourced under a commercially permissive license.
How to use the models?
Try to run inference with our models with just a few commands!
We provide all instructions to fully reproduce our results.
If you want to improve your own models or to learn more about our pipeline, read through the relevant docs below.
Training
This model is trained with 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.
Please see NeMo-Skills Github repo for training details.
Contact
E-Mail: Igor Gitman
Citation
If you find this model useful, please cite the following works
TODO
License
The use of this model is governed by the Llama 2 Community License Agreement