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"
)
| 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.
Links
- Paper: to be released
License: to be finalised.