Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+datasets:
+- allenai/olmo-mix-1124
+---
+
+# SuperBPE
+This 8B model was trained from scratch with a SuperBPE tokenizer. [SuperBPE](https://arxiv.org/abs/2503.13423) extends the BPE algorithm to include both traditional subword tokens (contained within word boundaries), as well as new **superword** tokens (containing parts of multiple words)! Due to encoding the same amount of text in fewer tokens, this model is **33% more efficient at inference-time** on average compared to a model trained with BPE.
+
+The model was trained with the Olmo2 7B architecture and pretraining data. It has a context length of 2,756 tokens (to match the effective context size in bytes of a BPE model with a context length of 4,096 tokens), and is trained on 334B tokens. The tokenizer has a vocabulary size of 200k and transitions from learning subword to learning superword tokens at vocabulary size of 80k.
+
+## Example Usage
+
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("UW/OLMo2-8B-SuperBPE-t180k")
+model = AutoModelForCausalLM.from_pretrained("UW/OLMo2-8B-SuperBPE-t180k")
+
+tokenizer.convert_ids_to_tokens(tokenizer.encode("By the way, I am a fan of the Milky Way."))
+# ['ByĠtheĠway', ',ĠIĠamĠa', 'ĠfanĠofĠthe', 'ĠMilkyĠWay', '.']
+```
+
+# 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}, 
+}
+```