README.md

---
library_name: transformers
license: apache-2.0
datasets:
- monology/pile-uncopyrighted
- MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
---

# MinPLM-QWen-200M

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

**MiniPLM-QWen-200M** is a 200M model with QWen achitecture pre-trained from scratch on [the Pile](https://huggingface.co/datasets/monology/pile-uncopyrighted) using the MiniPLM knowledge distillation framework with the [offcial QWen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) as the teacher model.

We also open-source the [pre-training corpus](https://huggingface.co/datasets/MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5) refined by Difference Sampling in MiniPLM for reproducibility.

<p align='left'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/2BqT0NgkmIXYlktovw9kG.png" width="1000">
</p>

## 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>

## Baseline Models
+ [Conventional Pre-Training](https://huggingface.co/MiniLLM/Pretrain-Qwen-200M)
+ [VanillaKD](https://huggingface.co/MiniLLM/VanillaKD-Pretrain-Qwen-200M)

## Citation

```bibtex
@misc{gu2024miniplmknowledgedistillationpretraining,
      title={MiniPLM: Knowledge Distillation for Pre-Training Language Models}, 
      author={Yuxian Gu and Hao Zhou and Fandong Meng and Jie Zhou and Minlie Huang},
      year={2024},
      eprint={2410.17215},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.17215}, 
}
```