| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - gair-prox/RedPajama-pro |
| | language: |
| | - en |
| | base_model: |
| | - gair-prox/RedPJ-ProX-0.7B |
| | pipeline_tag: text-generation |
| | library_name: transformers |
| | tags: |
| | - llama |
| | - code |
| | --- |
| | |
| | # Math-doc-refining-lm |
| |
|
| | <p align="center"> |
| | <img src="prox-teaser.png"> |
| | </p> |
| |
|
| | [ArXiv](http://arxiv.org/abs/2409.17115) | [Code](https://github.com/GAIR-NLP/program-every-example) |
| |
|
| | **Math-doc-refining-lm** is an adapted [0.7B-ProX](https://huggingface.co/gair-prox/RedPJ-ProX-0.7B) model, fine-tuned for doc level refining via program generation, and can be applied over math pre-training corpus such as open-web-math. |
| |
|
| | <p align="center"> |
| | <img src="func_design.png"> |
| | </p> |
| |
|
| | ### Citation |
| | ``` |
| | @article{zhou2024programming, |
| | title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale}, |
| | author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei}, |
| | journal={arXiv preprint arXiv:2409.17115}, |
| | year={2024} |
| | } |
| | ``` |
