|
--- |
|
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}, |
|
} |
|
``` |