from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "epfl-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.

epfl-dlab/spp-filtered-3b-base

Filtered — Base (3B)

Model details

Training checkpoints

Intended use

Links

epfl-dlab/
spp-filtered-3b-base