MiniPLM-Qwen-200M/README.md

library_name, license, datasets, language, metrics, pipeline_tag

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

MinPLM-Qwen-200M

paper | code

MiniPLM-Qwen-200M is a 200M model with Qwen achitecture pre-trained from scratch on the Pile using the MiniPLM knowledge distillation framework with the offcial Qwen1.5-1.8B as the teacher model.

We also open-source the pre-training corpus refined by Difference Sampling in MiniPLM for reproducibility.

Evaluation

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

Baseline Models

• Conventional Pre-Training
• VanillaKD

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}
}