Ref-Pretrain-Qwen-104M/README.md

datasets, language, library_name, license, metrics, pipeline_tag

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

Ref-Pretrain-Qwen-104M

paper | code

Ref-Pretrain-Qwen-104M is a 104M model with Qwen achitecture conventionally pre-trained from scratch on the Pile for 5B tokens.

We also open-source the tokenized pre-training corpus for reproducibility.

It is used as the reference model in the MiniPLM knwoledge distillation framework to construct the refined pre-training corpus. The data is then used to train MiniPLM models.

Evaluation

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

Citation

@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}
}