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--- |
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license: apache-2.0 |
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datasets: |
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- gair-prox/open-web-math-pro |
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language: |
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- en |
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base_model: |
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- TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T |
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--- |
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# TinyLlama-1.1B-ProXMath |
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<p align="center"> |
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<img src="prox-teaser.png"> |
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</p> |
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[ArXiv](https://arxiv.org/abs/2409.17115) | [Data: OpenWebMath-Pro](https://huggingface.co/datasets/gair-prox/open-web-math-pro) | [Code](https://github.com/GAIR-NLP/program-every-example) |
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**TinyLlama-1.1B-ProXMath** is a math-adapted TinyLlama-1.1B model that is continually pre-trained on [OpenWebMath-Pro](https://huggingface.co/datasets/gair-prox/open-web-math-pro) (a refined version by ProX) for **15**B tokens. |
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## Evaluations |
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ProX models are evaluated on 9 common math reasoning benchmarks. |
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| Model | asdiv | gsm8k | mathqa | mawps | minerva_math | mmlu_stem | sat_math | svamp | tabmwp | average | |
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|-------------------------|:--------:|:-------:|:--------:|:--------:|:------------:|:---------:|:--------:|:--------:|:--------:|:--------:| |
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| TinyLlama-1.1B | 18.0 | 2.8 | 14.6 | 20.2 | 3.2 | 16.3 | 21.9 | 10.9 | 12.5 | 13.4 | |
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| TinyLlama-1.1B-ProXMath | **41.9** | **9.0** | **15.6** | **56.9** | **5.6** | **26.8** | **31.2** | **23.8** | **22.2** | **25.7** | |
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### Citation |
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``` |
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@article{zhou2024programming, |
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title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale}, |
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author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei}, |
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journal={arXiv preprint arXiv:2409.17115}, |
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year={2024} |
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} |
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``` |
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