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.

model-raising/spp-filtered-1.7b-base

Filtered — Base (1.7B)

Model details

Training checkpoints

Intended use

Links

model-raising/
spp-filtered-1.7b-base