license, language, library_name, pipeline_tag, tags

license	language	library_name	pipeline_tag	tags
other	en	transformers	text-generation	spp synthetic-persona-pretraining spp alignment safety

Filtered — Base (3B)

Type: base (pretrained) model. Not instruction-tuned and ships no chat template.

Filtered baseline. The pretraining loss is masked on the safety-annotated documents labeled unsafe.

Instruction-tuned counterpart: dlab-spp/filtered-3b-instruct.

Model details

• Architecture: Llama-3.2-3B-shaped, trained from scratch.
• Tokenizer: the original SmolLM2 tokenizer (vocabulary 49152).
• Pretraining: ~500B tokens on a subset of the Olmo 3 Dolma 3 mixture.

Training checkpoints

Intermediate checkpoints are published as git revisions on this repo, so any point in the trajectory can be loaded by passing revision=:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "dlab-spp/filtered-3b-base"
tok = AutoTokenizer.from_pretrained(repo)          # identical at every revision
model = AutoModelForCausalLM.from_pretrained(
    repo, revision="step-25000", dtype=torch.bfloat16, device_map="auto"
)

Revision	Pretraining step	Tokens seen	LR phase
step-25000	25,000 / 254,313	~49.2B	stable
step-50000	50,000 / 254,313	~98.3B	stable
step-75000	75,000 / 254,313	~147B	stable
step-100000	100,000 / 254,313	~197B	stable
step-125000	125,000 / 254,313	~246B	stable
step-150000	150,000 / 254,313	~295B	stable
step-175000	175,000 / 254,313	~344B	stable
step-200000	200,000 / 254,313	~393B	stable
step-225000	225,000 / 254,313	~442B	stable
step-240000	240,000 / 254,313	~472B	linear decay
step-254313	254,313 / 254,313	~500B	linear decay — same weights as main

main always holds the finished model (step 254,313). 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.

Links

• Paper: Synthetic Persona Pretraining: Alignment from Token Zero

Citation

@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.