README.md

---
library_name: transformers
license: apache-2.0
datasets:
- monology/pile-uncopyrighted
- MiniLLM/pile-tokenized
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
---

# Ref-Pretrain-Qwen-104M

[paper](https://arxiv.org/abs/2410.17215) | [code](https://github.com/thu-coai/MiniPLM)

**Ref-Pretrain-Qwen-104M** is a 104M model with Qwen achitecture conventionally pre-trained from scratch on [the Pile](https://huggingface.co./datasets/monology/pile-uncopyrighted) for 5B tokens.

We also open-source the tokenized [pre-training corpus](https://huggingface.co./datasets/MiniLLM/pile-tokenized) for reproducibility.

**It is used as the reference model in the MiniPLM knwoledge distillation framework to construct the [refined pre-training corpus](https://huggingface.co./datasets/MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5).**
**The data is then used to train [MiniPLM models](https://huggingface.co./collections/MiniLLM/miniplm-6712c0fdf09ef7e8da7d39bd).**

## Evaluation

MiniPLM models achieves better performance given the same computation and scales well across model sizes:

<p align='left'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/EOYzajQcwQFT5PobqL3j0.png" width="1000">
</p>

## Citation

```bibtext
@article{miniplm,
    title={MiniPLM: Knowledge Distillation for Pre-Training Language Models}, 
    author={Yuxian Gu and Hao Zhou and Fandong Meng and Jie Zhou and Minlie Huang},
    journal={arXiv preprint arXiv:2410.17215},
    year={2024}
}
```