MiniLLM
/

Ref-Pretrain-Qwen-104M

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 ---
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- monology/pile-uncopyrighted
+- MiniLLM/pile-tokenized
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-generation
 ---
+# Ref-Pretrain-Qwen-104M
+[paper](https://arxiv.org/abs/2410.17215) | [code](https://github.com/thu-coai/MiniPLM)
+**Ref-Pretrain-Qwen-104M** is a 104M model with Qwen achitecture conventionally pre-trained from scratch on [the Pile](https://huggingface.co/datasets/monology/pile-uncopyrighted) for 5B tokens.
+We also open-source the tokenized [pre-training corpus](https://huggingface.co/datasets/MiniLLM/pile-tokenized) for reproducibility.
+**It is used as the reference model in the MiniPLM knwoledge distillation framework to construct the [refined pre-training corpus](https://huggingface.co/datasets/MiniLLM/pile-diff_samp-qwen_1.8B-qwen_104M-r0.5).**
+**The data is then used to train [MiniPLM models](https://huggingface.co/collections/MiniLLM/miniplm-6712c0fdf09ef7e8da7d39bd).**
 ## 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
+```bibtext
+@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}
+}
+```