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dlab-spp/filtered-1.7b-base.<assistant> marker token (vocabulary 49188).[N.M] citations; response-only loss, one epoch.<|im_start|><assistant>. Use the built-in chat template:1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4repo = "dlab-spp/filtered-1.7b-instruct"
5tok = AutoTokenizer.from_pretrained(repo)
6model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")
7
8msgs = [{"role": "user", "content": "How should I think about honesty?"}]
9ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
10out = model.generate(ids, max_new_tokens=512)
11print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=False))1@misc{minder2026syntheticpersonapretrainingalignment,
2 title={Synthetic Persona Pretraining: Alignment from Token Zero},
3 author={Julian Minder and Viktor Moskvoretskii and Raghav Singhal and Difan Jiao and Andy Arditi and Shaobo Cui and Yiderigun Borjigin and Kartik Bali and Stefan Krsteski and Harsh Raj and Huu Nguyen and Jannik Brinkmann and Ashton Anderson and Roland Aydin and Robert West},
4 year={2026},
5 eprint={2608.13482},
6 archivePrefix={arXiv},
7 primaryClass={cs.LG},
8 url={https://arxiv.org/abs/2608.13482},
9}