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--- |
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library_name: transformers |
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license: apache-2.0 |
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datasets: |
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- monology/pile-uncopyrighted |
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- MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5 |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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--- |
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# MinPLM-Qwen-500M |
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[paper](https://arxiv.org/abs/2410.17215) | [code](https://github.com/thu-coai/MiniPLM) |
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**MiniPLM-Qwen-500M** is a 500M model with Qwen achitecture pre-trained from scratch on [the Pile](https://huggingface.co/datasets/monology/pile-uncopyrighted) using the MiniPLM knowledge distillation framework with the [offcial Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) as the teacher model. |
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We also open-source the [pre-training corpus](https://huggingface.co/datasets/MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5) refined by Difference Sampling in MiniPLM for reproducibility. |
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<p align='left'> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/2BqT0NgkmIXYlktovw9kG.png" width="1000"> |
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</p> |
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## Evaluation |
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MiniPLM models achieves better performance given the same computation and scales well across model sizes: |
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<p align='left'> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/EOYzajQcwQFT5PobqL3j0.png" width="1000"> |
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</p> |
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## Baseline Models |
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+ [Conventional Pre-Training](https://huggingface.co/MiniLLM/Pretrain-Qwen-500M) |
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+ [VanillaKD](https://huggingface.co/MiniLLM/VanillaKD-Pretrain-Qwen-500M) |
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## Citation |
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```bibtex |
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@article{miniplm, |
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title={MiniPLM: Knowledge Distillation for Pre-Training Language Models}, |
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author={Yuxian Gu and Hao Zhou and Fandong Meng and Jie Zhou and Minlie Huang}, |
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journal={arXiv preprint arXiv:2410.17215}, |
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year={2024} |
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} |
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``` |
