Architecture: SmolLM2-1.7B architecture, trained from scratch.
Tokenizer: SmolLM2 tokenizer with an added <assistant> marker token (vocabulary 49188).
Pretraining: ~100B tokens on a subset of the Olmo 3 Dolma 3 mixture.
Post-training: persona-binding supervised fine-tuning (PBSFT-mix): 300k single-turn examples, 90% WildChat-1M instructions and 10% safety prompts (WildJailbreak, WildGuardMix); assistant responses follow a constitution with inline [N.M] citations; response-only loss, one epoch.

Chat format

There is no system prompt. Each assistant turn opens with <|im_start|><assistant>. Use the built-in chat template:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "model-raising/spp-vanilla-1.7b-instruct"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")

msgs = [{"role": "user", "content": "How should I think about honesty?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=512)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=False))

Intended use

Research on alignment and safety (constitutional alignment, value generalization, jailbreak robustness). A research artifact, not a production model; it can produce incorrect or unsafe content.

model-raising/spp-vanilla-1.7b-instruct

Vanilla — Instruct (1.7B)

Model details

Chat format

Intended use

Links

model-raising/
spp-vanilla-1.7b-instruct