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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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datasets: |
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- allenai/olmo-mix-1124 |
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--- |
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# BPE Baseline |
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This 8B model was trained from scratch with a traditional subword BPE tokenizer, and serves as our baseline in experiments. |
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The model was trained with the Olmo2 7B architecture and pretraining data. It has a context length of 4,096 tokens and is trained on 321B tokens. The tokenizer has a vocabulary size of 200k. |
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## Example Usage |
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``` |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("UW/OLMo2-8B-BPE") |
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model = AutoModelForCausalLM.from_pretrained("UW/OLMo2-8B-BPE") |
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tokenizer.convert_ids_to_tokens(tokenizer.encode("By the way, I am a fan of the Milky Way.")) |
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# ['By', 'Ġthe', 'Ġway', ',', 'ĠI', 'Ġam', 'Ġa', 'Ġfan', 'Ġof', 'Ġthe', 'ĠMilky', 'ĠWay', '.'] |
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``` |
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# Citation |
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``` |
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@misc{liu-etal-2025-superbpe, |
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title={SuperBPE: Space Travel for Language Models}, |
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author={Alisa Liu and Jonathan Hayase and Valentin Hofmann and Sewoong Oh and Noah A. Smith and Yejin Choi}, |
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year={2025}, |
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eprint={2503.13423}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2503.13423}, |
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} |
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``` |
