MiniLLM_-_Ref-Pretrain-Qwen-104M-gguf

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Ref-Pretrain-Qwen-104M - GGUF

• Model creator: https://huggingface.co/MiniLLM/
• Original model: https://huggingface.co/MiniLLM/Ref-Pretrain-Qwen-104M/

Original model description:

library_name: transformers license: apache-2.0 datasets:

• monology/pile-uncopyrighted
• MiniLLM/pile-tokenized language:
• en metrics:
• accuracy pipeline_tag: text-generation

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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:

<p align='left'> <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/EOYzajQcwQFT5PobqL3j0.png" width="1000"> </p>

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}
}