RichardErkhov/MiniLLM_-_MiniPLM-llama3.1-212M-gguf

Quantization made by Richard Erkhov.

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MiniPLM-llama3.1-212M - GGUF

• —Model creator: https://huggingface.co/MiniLLM/
• —Original model: https://huggingface.co/MiniLLM/MiniPLM-llama3.1-212M/

Original model description: --- library_name: transformers license: apache-2.0 datasets:

• —monology/pile-uncopyrighted
• —MiniLLM/pile-diffsamp-qwen1.8B-qwen_104M-r0.5 language:
• —en metrics:
• —accuracy pipeline_tag: text-generation ---

MiniPLM-llama3.1-212M

paper | code

MiniPLM-llama3.1-212M is a 212M model with the LLaMA3.1 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. This model shows the flexibility of the MiniPLM framework in conducting knowledge distillation across model families.

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

<p align='left'> <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/2BqT0NgkmIXYlktovw9kG.png" width="1000"> </p>

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>

Baseline Models

• —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}
}