OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
Recent work has shown the immense potential of synthetically generated
datasets for training large language models (LLMs), especially for acquiring
targeted skills. Current large-scale math instruction tuning datasets such as
MetaMathQA (Yu et al., 2024) and MAmmoTH (Yue et al., 2024) are constructed
using outputs from closed-source LLMs with commercially restrictive licenses. A
key reason limiting the use of open-source LLMs in these data generation
pipelines has been the wide gap between the mathematical skills of the best
closed-source LLMs, such as GPT-4, and the best open-source LLMs. Building on
the recent progress in open-source LLMs, our proposed prompting novelty, and
some brute-force scaling, we construct OpenMathInstruct-1, a math instruction
tuning dataset with 1.8M problem-solution pairs. The dataset is constructed by
synthesizing code-interpreter solutions for GSM8K and MATH, two popular math
reasoning benchmarks, using the recently released and permissively licensed
Mixtral model. Our best model, OpenMath-CodeLlama-70B, trained on a subset of
OpenMathInstruct-1, achieves a score of 84.6% on GSM8K and 50.7% on MATH, which
is competitive with the best gpt-distilled models. We release our code, models,
and the OpenMathInstruct-1 dataset under a commercially permissive license.
65cec22a6da01df53622b5f8
Shubham Toshniwal
admin_assigned
2024-03-22T08:43:37.415Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1618580776839-5fa75229a13e063b8b2b5f74.png
Shubham Toshniwal
user
shtoshni
65cec22a6da01df53622b5f9
Ivan Moshkov
admin_assigned
2024-02-16T11:03:29.189Z
65cd2166c52afc14cebc171a
https://cdn-avatars.huggingface.co/v1/production/uploads/65cd2166c52afc14cebc171a/LhIg07Yaqo0gmqYzGCcTe.png
Ivan Moshkov
user
imoshkov
65cec22a6da01df53622b5fa
Sean Narenthiran
admin_assigned
2024-02-16T11:03:36.036Z
62ce959a539556d82c9ebc59
/avatars/b4ef43a356c24dcbb3ba6a26aab6e12a.svg
Sean Narenthiran
user
SeanNaren
65cec22a6da01df53622b5fb
Daria Gitman
65cec22a6da01df53622b5fc
Fei Jia
65cec22a6da01df53622b5fd
Igor Gitman
admin_assigned
2024-02-16T11:04:03.985Z
6356f834e75acf88c902b538
https://cdn-avatars.huggingface.co/v1/production/uploads/1666644004634-noauth.jpeg
Igor Gitman
user
igitman
OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset
Recent work has shown the immense potential of synthetically generated
datasets for training large language models (LLMs), especially for acquiring
targeted skills. Current large-scale math instruction tuning datasets such as
MetaMathQA (Yu et al., 2024) and MAmmoTH (Yue et al., 2024) are constructed
using outputs from closed-source LLMs with commercially restrictive licenses. A
key reason limiting the use of open-source LLMs in these data generation
pipelines has been the wide gap between the mathematical skills of the best
closed-source LLMs, such as GPT-4, and the best open-source LLMs. Building on
the recent progress in open-source LLMs, our proposed prompting novelty, and
some brute-force scaling, we construct OpenMathInstruct-1, a math instruction
tuning dataset with 1.8M problem-solution pairs. The dataset is constructed by
synthesizing code-interpreter solutions for GSM8K and MATH, two popular math
reasoning benchmarks, using the recently released and permissively licensed
Mixtral model. Our best model, OpenMath-CodeLlama-70B, trained on a subset of
OpenMathInstruct-1, achieves a score of 84.6% on GSM8K and 50.7% on MATH, which
is competitive with the best gpt-distilled models. We release our code, models,
and the OpenMathInstruct-1 dataset under a commercially permissive license.
A new math instruction tuning dataset, OpenMathInstruct-1, leverages the Mixtral model to achieve competitive performance on math reasoning benchmarks using open-source LLMs.
Large language models LLMs MetaMathQA MAmmoTH GPT-4 Mixtral math instruction tuning GSM8K MATH OpenMath-CodeLlama-70B code-interpreter solutions