Filtered — Instruct (1.7B)
Type: instruction-tuned model (base model + persona-binding supervised fine-tuning).
Filtered baseline (pretraining loss masked on the safety-annotated documents labeled unsafe), post-trained with persona-binding SFT.
Base counterpart: model-raising/spp-filtered-1.7b-base.
Model details
- 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:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "model-raising/spp-filtered-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.
Links
- Paper: to be released
License: to be finalised.