metadata

datasets:
  - monology/pile-uncopyrighted
  - MiniLLM/pile-tokenized
language:
  - en
library_name: transformers
license: apache-2.0
metrics:
  - accuracy
pipeline_tag: text-generation
base_model:
  - Qwen/Qwen1.5-1.8B

VanillaKD-Pretrain-Qwen-200M

paper | code

VanillaKD-Pretrain-Qwen-200M is a 200M model with Qwen achitecture pre-trained with vanilla token-level knowledge distillation on the Pile for 50B tokens. The teacher model is Qwen1.5-1.8B.

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

It is used as the baseline for MiniLLM-Qwen-200M

Evaluation

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

Other Baselines

Conventional Pre-Training

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}
}