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--- |
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datasets: |
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- EleutherAI/pile |
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language: |
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- en |
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--- |
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# Model Card |
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This model is pretrained Based model. |
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As a quality reference, we include a pretrained Mamba model provided here: https://huggingface.co/hazyresearch/mamba-1b-50b and a pretrained attention (Llama architecture) model provided here: https://huggingface.co/hazyresearch/attn-1b-50bn |
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All three checkpoints are pretrained on **50Bn tokens** of the Pile in the exact same data order using next token prediction. |
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A WandB report for training is here: https://api.wandb.ai/links/hazy-research/ggo9rst2 |
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### Model Sources |
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The model implementation and training code that produced the model are provided here: https://github.com/HazyResearch/based |
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### Uses |
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The purpose of this work is to evaluate the language modeling quality of a new efficient architecture, Based. |
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We include a series of benchmarks that you can use to evaluate quality: |
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- FDA: https://huggingface.co/datasets/hazyresearch/based-fda |
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- SWDE: https://huggingface.co/datasets/hazyresearch/based-swde |
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- SQUAD: https://huggingface.co/datasets/hazyresearch/based-squad |
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## Citation |
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Please consider citing this paper if you use our work: |
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``` |
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@article{arora2024simple, |
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title={Simple linear attention language models balance the recall-throughput tradeoff}, |
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author={Arora, Simran and Eyuboglu, Sabri and Zhang, Michael and Timalsina, Aman and Alberti, Silas and Zinsley, Dylan and Zou, James and Rudra, Atri and Ré, Christopher}, |
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journal={arXiv:2402.18668}, |
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year={2024} |
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} |
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``` |
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Please reach out to simarora@stanford.edu, eyuboglu@stanford.edu, and mzhang20@stanford.edu with questions. |
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