SPP-T0 — Base (3B)
Type: base (pretrained) model. Not instruction-tuned and ships no chat template.
Trained with Synthetic Persona Pretraining (SPP) from token zero: first-person reflections are inserted into the roughly 10% of annotated documents that carry one, throughout the entire pretraining run.
Synthetic Persona Pretraining (SPP)
Synthetic Persona Pretraining (SPP) installs a target value persona during pretraining rather than only during alignment. Value-laden, first-person reflections, generated against a constitution, are appended to a subset of pretraining documents after a special <assistant> token. Attention masking and RoPE position aliasing keep the reflection from changing the continuation of the original document. This model is trained with SPP.
Instruction-tuned counterpart: model-raising/spp-t0-3b-instruct.
Model details
- Architecture: Llama-3.2-3B-shaped, trained from scratch.
- Tokenizer: the SmolLM2 tokenizer extended with an
<assistant>marker and constitution tokens (vocabulary 49280). - Pretraining: ~500B tokens on a subset of the Olmo 3 Dolma 3 mixture, with SPP reflections inserted into the safety-annotated documents within it.
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 = "model-raising/spp-t0-3b-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: to be released
License: to be finalised.