1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3tok = AutoTokenizer.from_pretrained("K1zE/BPM")
4model = AutoModelForCausalLM.from_pretrained("K1zE/BPM", dtype="auto", device_map="auto")
5
6msgs = [{"role": "user", "content": "What is the remainder of 7^100 modulo 13?"}]
7ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
8print(tok.decode(model.generate(ids, max_new_tokens=2048)[0][ids.shape[-1]:], skip_special_tokens=True))
Prompts:
K1zE/BPM. Research checkpoint, not
instruction-tuned for general use.
1@misc{wang2026crosstokenizeronpolicydistillationbyteprefix,
2 title={Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization},
3 author={Hao Wang and Kun Yuan and Wenlin Zhong and Minglei Zhang and Han Xiao and Ming Sun and Honggang Qi},
4 year={2026},
5 eprint={2607.22334},
6 archivePrefix={arXiv},
7 primaryClass={cs.LG},
8 url={https://arxiv.org/abs/2607.22334},
9}