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@@ -84,6 +84,8 @@ The pipeline we used to produce these models is fully open-sourced!
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  - [Models](https://huggingface.co/collections/nvidia/openmath-65c5619de2ba059be0775014)
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  - [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1)
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  # How to use the models?
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  Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands!
@@ -110,4 +112,11 @@ offering enterprises an easy, cost-effective, and fast way to adopt generative A
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  If you find our work useful, please consider citing us!
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- TODO
 
 
 
 
 
 
 
 
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  - [Models](https://huggingface.co/collections/nvidia/openmath-65c5619de2ba059be0775014)
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  - [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1)
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+ See our [paper](https://arxiv.org/abs/2402.10176) for more details!
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+
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  # How to use the models?
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  Try to [run inference with our models](https://github.com/Kipok/NeMo-Skills/blob/main/docs/inference.md) with just a few commands!
 
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  If you find our work useful, please consider citing us!
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+ ```bibtex
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+ @article{toshnival2024openmath,
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+ title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
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+ author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
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+ year = {2024},
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+ journal = {arXiv preprint arXiv: Arxiv-2402.10176}
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+ }
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+ ```