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