pytorch safetensors llama en dataset:gair-prox/RedPajama-pro arxiv:2409.17115 license:apache-2.0 region:us
---
datasets:
- gair-prox/RedPajama-pro
language:
- en
license: apache-2.0
tags:
- llama
---

# RedPJ-ProX-0.3B

<p align="center">
  <img src="prox-teaser.png">
</p>

[ArXiv](http://arxiv.org/abs/2409.17115) | [Models](https://huggingface.co/gair-prox/RedPJ-ProX-0.3B) | [Data](https://huggingface.co/datasets/gair-prox/RedPajama-pro) | [Code](https://github.com/GAIR-NLP/program-every-example)

**RedPJ-ProX-0.3B** is a tiny language model. It was and trained on the [RedPajama-V2-pro](https://huggingface.co/datasets/gair-prox/RedPajama-pro) for 25B tokens. 

## Evaluations

ProX models are evaluated over 10 language model benchmarks in zero-shot setting.

|                       | ArC-c | ARC-e | CSQA  | HellaS | MMLU  | OBQA  | PiQA  | SIQA  | WinoG | SciQ  | AVG  |
|-----------------------|-------|-------|-------|-----------|-------|-------|-------|-------|-------|-------|------|
| raw | 22.6 | 41.9 | 29.7 | 32.8     | 26.2 | 26.4 | 62.2 | 39.3 | 51.3 | 63.3 | 39.6 |
| ours       | 25.9 | 47.5 | 29.2 | 36.7     | 28.1 | 30.2 | 64.6 | 38.0 | 51.7 | 71.4 | 42.3 |

### 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}
}
```