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--- |
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license: apache-2.0 |
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datasets: |
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- togethercomputer/RedPajama-Data-1T |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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## PDS-470M |
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[paper](https://arxiv.org/abs/2410.07064) | [code](https://github.com/microsoft/LMOps/tree/main/data_selection) |
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**PDS-470M** is a 470M model with [Mistral](https://arxiv.org/abs/2310.06825) achitecture pre-trained from scratch on the data selected from the CC split of [Redpajama](https://github.com/togethercomputer/RedPajama-Data), using the PDS framework. |
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The PDS framework is based on the [Pontryagin's maximum principle](https://en.wikipedia.org/wiki/Pontryagin%27s_maximum_principle#:~:text=Pontryagin's%20maximum%20principle%20is%20used,the%20state%20or%20input%20controls.) for optimal pre-training data selection, which not only enjoy strong theoretical support but is also scalable for training large language models. |
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Please refer to our [paper](https://arxiv.org/abs/2410.07064) for more details. |
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### Overview of the theory: |
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<p align='left'> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/Hdw83Vsb305GRlsqB7c34.png" width="700"> |
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</p> |
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### Overview of the PDS framework: |
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<p align='left'> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/YPwluLyZGK7DACH1WqDUN.png" width="700"> |
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</p> |
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### Evaluation |
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PDS-selected data improves the performance of language models pre-trained from scratch and saves pre-training comptation. The improvement scales up to large model sizes. |
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<p align='left'> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/6undIr37d10qD73TDiPDK.png" width="600"> |
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</p> |
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### Baseline |
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[Conventional Pre-training](https://huggingface.co/Data-Selection/BSL-470M) |
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### Citation |
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```bibtex |
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@article{gu2024data, |
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title={Data Selection via Optimal Control for Language Models}, |
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author={Gu, Yuxian and Dong, Li and Wang, Hongning and Hao, Yaru and Dong, Qingxiu and Wei, Furu and Huang, Minlie}, |
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journal={arXiv preprint arXiv:2410.07064}, |
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year={2024} |
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} |
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``` |
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