spp-filtered-1.7b-base

Filtered — Base (1.7B)

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: model-raising/spp-filtered-1.7b-instruct.

Model details

• Architecture: SmolLM2-1.7B architecture, trained from scratch.
• Tokenizer: the original SmolLM2 tokenizer (vocabulary 49152).
• Pretraining: ~100B 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 = "model-raising/spp-filtered-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"
)

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.

Links

• Paper: to be released

License: to be finalised.