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--- |
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license: llama2 |
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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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pipeline_tag: text-generation |
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tags: |
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- llama2 |
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- math |
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- reasoning |
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base_model: |
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- meta-llama/Llama-2-7b-hf |
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--- |
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# Llama-2-7B-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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**Llama-2-7B-ProXMath** is a math-adapted Llama-2-7B 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 **10**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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| Llama-2-7B | 51.6 | 14.1 | 12.5 | 63.6 | 3.8 | 32.9 | 34.4 | 39.5 | 30.9 | 31.48 | |
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| Llama-2-7B-ProXMath | 63.7 | 30.6 | 40.1 | 79.3 | 16.8 | 43.8 | 53.1 | 50.2 | 37.3 | 46.1 | |
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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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