Data-Selection
/

PDS-470M

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
+license: apache-2.0
+datasets:
+- togethercomputer/RedPajama-Data-1T
+language:
+- en
+pipeline_tag: text-generation
 library_name: transformers
 ---
+## PDS-470M
+[paper](https://arxiv.org/abs/2410.07064) | [code](https://github.com/microsoft/LMOps/tree/main/data_selection)
+**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.
+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.
+Please refer to our [paper](https://arxiv.org/abs/2410.07064) for more details.
+### Overview of the theory:
+<p align='left'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/Hdw83Vsb305GRlsqB7c34.png" width="700">
+</p>
+### Overview of the PDS framework:
+<p align='left'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/YPwluLyZGK7DACH1WqDUN.png" width="700">
+</p>
+### Evaluation
+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.
+<p align='left'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/6undIr37d10qD73TDiPDK.png" width="600">
+</p>
+### Baseline
+[Conventional Pre-training](https://huggingface.co/Data-Selection/BSL-470M)
+### Citation
+```bibtex
+@article{gu2024data,
+  title={Data Selection via Optimal Control for Language Models},
+  author={Gu, Yuxian and Dong, Li and Wang, Hongning and Hao, Yaru and Dong, Qingxiu and Wei, Furu and Huang, Minlie},
+  journal={arXiv preprint arXiv:2410.07064},
+  year={2024}
+}
+```