Downloads · 30 days
507
2% of all-time downloads
nvidia_-_OpenMath-Mistral-7B-v0.1-hf-gguf
RichardErkhov/nvidia_-_OpenMath-Mistral-7B-v0.1-hf-gguf
nvidia_-_OpenMath-Mistral-7B-v0.1-hf-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
All-time downloads
22.8K
Public
Repo size
82.2 GB
Likes
1
Public
Repo makeup
Click a slice to open those files.
.gguf95.2 GB · 100%
At a glance
- Access
- Public
- Created
- Jun 19, 2024
- Updated
- Jun 19, 2024
- SHA
- 1e93d424
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
OpenMath-Mistral-7B-v0.1-hf - GGUF
- Model creator: https://huggingface.co/nvidia/
- Original model: https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf/
Original model description:
license: apache-2.0 base_model:
- mistralai/Mistral-7B-v0.1 datasets:
- nvidia/OpenMathInstruct-1 language:
- en tags:
- nvidia
- code
- math
OpenMath-Mistral-7B-v0.1-hf
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.
<table border="1"> <tr> <td></td> <td colspan="2" style="text-align: center;">greedy</td> <td colspan="2" style="text-align: center;">majority@50</td> </tr> <tr> <td style="text-align: center;">model</td> <td style="text-align: center;">GSM8K</td> <td style="text-align: center;">MATH</td> <td style="text-align: center;">GMS8K</td> <td style="text-align: center;">MATH</td> </tr> <tr> <td style="text-align: right;">OpenMath-CodeLlama-7B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-7b-Python-hf">HF</a>)</td> <td style="text-align: center;">75.9</td> <td style="text-align: center;">43.6</td> <td style="text-align: center;">84.8</td> <td style="text-align: center;">55.6</td> </tr> <tr> <td style="text-align: right;">OpenMath-Mistral-7B (<a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Mistral-7B-v0.1-hf">HF</a>)</td> <td style="text-align: center;">80.2</td> <td style="text-align: center;">44.5</td> <td style="text-align: center;">86.9</td> <td style="text-align: center;">57.2</td> </tr> <tr> <td style="text-align: right;">OpenMath-CodeLlama-13B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-13b-Python-hf">HF</a>)</td> <td style="text-align: center;">78.8</td> <td style="text-align: center;">45.5</td> <td style="text-align: center;">86.8</td> <td style="text-align: center;">57.6</td> </tr> <tr> <td style="text-align: right;">OpenMath-CodeLlama-34B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-34b-Python-hf">HF</a>)</td> <td style="text-align: center;">80.7</td> <td style="text-align: center;">48.3</td> <td style="text-align: center;">88.0</td> <td style="text-align: center;">60.2</td> </tr> <tr> <td style="text-align: right;">OpenMath-Llama2-70B (<a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-Llama-2-70b-hf">HF</a>)</td> <td style="text-align: center;"><b>84.7</b></td> <td style="text-align: center;">46.3</td> <td style="text-align: center;">90.1</td> <td style="text-align: center;">58.3</td> </tr> <tr> <td style="text-align: right;">OpenMath-CodeLlama-70B (<a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python">nemo</a> | <a href="https://huggingface.co/nvidia/OpenMath-CodeLlama-70b-Python-hf">HF</a>)</td> <td style="text-align: center;">84.6</td> <td style="text-align: center;"><b>50.7</b></td> <td style="text-align: center;"><b>90.8</b></td> <td style="text-align: center;"><b>60.4</b></td> </tr> </table>The pipeline we used to produce these models is fully open-sourced!
See our paper for more details!
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 other models
To improve other models or to learn more about our code, read through the 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.
Citation
If you find our work useful, please consider citing us!
@article{toshniwal2024openmath,
title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
year = {2024},
journal = {arXiv preprint arXiv: Arxiv-2402.10176}
}
- Created
- Jun 19, 2024
- Updated
- Jun 19, 2024