from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "dlab-spp/vanilla-1.7b-base"
tok = AutoTokenizer.from_pretrained(repo)          # identical at every revision
model = AutoModelForCausalLM.from_pretrained(
    repo, revision="step-5000", dtype=torch.bfloat16, device_map="auto"
)

Revision	Pretraining step	Tokens seen	LR phase
`step-5000`	5,000 / 50,863	~9.8B	stable
`step-10000`	10,000 / 50,863	~19.7B	stable
`step-15000`	15,000 / 50,863	~29.5B	stable
`step-20000`	20,000 / 50,863	~39.3B	stable
`step-25000`	25,000 / 50,863	~49.2B	stable
`step-30000`	30,000 / 50,863	~59.0B	stable
`step-35000`	35,000 / 50,863	~68.8B	stable
`step-40000`	40,000 / 50,863	~78.6B	stable
`step-45000`	45,000 / 50,863	~88.5B	stable
`step-50863`	50,863 / 50,863	~100B	linear decay — same weights as `main`

main always holds the finished model (step 50,863). Only model weights are published — optimizer and RNG state are not included, so these revisions support evaluation, probing, and fine-tuning, but not exact resumption of the original run.

Intended use

Research on alignment and safety. As a base model it is meant for continuation, probing, or further fine-tuning; it is not instruction-tuned and can produce incorrect or unsafe content.

Citation

bibtex

@misc{minder2026syntheticpersonapretrainingalignment,
      title={Synthetic Persona Pretraining: Alignment from Token Zero},
      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},
      year={2026},
      eprint={2608.13482},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2608.13482},
}

License: to be finalised.

dlab-spp/vanilla-1.7b-base

Vanilla — Base (1.7B)

Model details

Training checkpoints

Intended use

Links

Citation

dlab-spp/
vanilla-1.7b-base