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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+math-doc-refining-lm - AWQ
+- Model creator: https://huggingface.co/gair-prox/
+- Original model: https://huggingface.co/gair-prox/math-doc-refining-lm/
+
+
+
+
+Original model description:
+---
+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}
+}
+```
+