license: llama2
datasets:
- nvidia/OpenMathInstruct-1
language:
- en
library_name: nemo
tags:
- nvidia
- code
- math
OpenMath-CodeLlama-7b-Python
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.
| greedy | majority@50 | |||
| model | GSM8K | MATH | GMS8K | MATH |
| OpenMath-CodeLlama-7B (nemo | HF) | 75.9 | 43.6 | 84.8 | 55.6 |
| OpenMath-Mistral-7B (nemo | HF) | 80.2 | 44.5 | 86.9 | 57.2 |
| OpenMath-CodeLlama-13B (nemo | HF) | 78.8 | 45.5 | 86.8 | 57.6 |
| OpenMath-CodeLlama-34B (nemo | HF) | 80.7 | 48.3 | 88.0 | 60.2 |
| OpenMath-Llama2-70B (nemo | HF) | 84.7 | 46.3 | 90.1 | 58.3 |
| OpenMath-CodeLlama-70B (nemo | HF) | 84.6 | 50.7 | 90.8 | 60.4 |
The pipeline we used to produce these models is fully open-sourced!
How to use the models?
Try to run inference with our models with just a few commands!
Reproducing our results
We provide all instructions to fully reproduce our results.
Improving your own models
If you want to improve your own models or to learn more about our pipeline, read through the relevant docs below.
In our pipeline we use 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.
Contact
E-Mail Igor Gitman at [email protected]
Citation
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License
The use of this model is governed by the Llama 2 Community License Agreement