42 lines
1.5 KiB
Markdown
42 lines
1.5 KiB
Markdown
---
|
|
datasets:
|
|
- monology/pile-uncopyrighted
|
|
- MiniLLM/pile-tokenized
|
|
language:
|
|
- en
|
|
library_name: transformers
|
|
license: apache-2.0
|
|
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}
|
|
}
|
|
``` |