language:
- en
license: apache-2.0
tags:
- nvidia
- code
- math
base_model:
- mistralai/Mistral-7B-v0.1
datasets:
- nvidia/OpenMathInstruct-1
model-index:
- name: OpenMath-Mistral-7B-v0.1-hf
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 59.39
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 81.78
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 59.34
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 46.13
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.27
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0.08
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nvidia/OpenMath-Mistral-7B-v0.1-hf
name: Open LLM Leaderboard
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.
| 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!
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}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 54.00 |
| AI2 Reasoning Challenge (25-Shot) | 59.39 |
| HellaSwag (10-Shot) | 81.78 |
| MMLU (5-Shot) | 59.34 |
| TruthfulQA (0-shot) | 46.13 |
| Winogrande (5-shot) | 77.27 |
| GSM8k (5-shot) | 0.08 |