Certo-R1 (Qwen3-4B)

Concise, verified reasoning for short calculation/compositional decisions. The escalation model of the Certo decision cascade: a lightweight decider handles the confident majority; low-confidence cases escalate here for a brief reasoned answer.

Credit / base model

Built on Qwen/Qwen3-4B (Alibaba Qwen team), used under Apache-2.0. Certo-R1 is a LoRA fine-tune (rank 16) merged into the base weights. All credit for the base model goes to the Qwen team; this repo adds only the concise-reasoning adaptation. Base revision 1cfa9a72.

What it does

Given a decision (context + options A/B/C…), it reasons in one or two short sentences and ends with FINAL ANSWER: <letter>. On a held-out set of ten short calculation/compositional families it is non-inferior to full verbose reasoning within a pre-registered −0.03 margin (cascade Δ +0.004, 95% CI [−0.004, +0.012]) while generating ~15× fewer tokens (≈32 vs ≈489, p50) and cutting routed latency ~8× (mean) / ~11× (p95).

JevBench 1.4.2 (public suite, 231 tasks)

Measured locally on the public tiers (the official score additionally blends 308 sealed decisions held by Benchmark Heaven — not included here). Certo-R1 is a reasoner, not a calibrated decider: it emits an argmax answer, so its Calibration axis is ~0 and the geometric-mean composite is gated — read the per-tier Intelligence instead.

Speed axis 79.5 (p50 0.50 s), Cost axis 58.1 ($0.025 / 1000 decisions at a $0.04/M input tariff). The generative Certo-R1 reasons far above the shipped encoder Certo (JevBench Intelligence ~28), trading calibration for reasoning.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("altslate/certo-r1-qwen3-4b")
m = AutoModelForCausalLM.from_pretrained("altslate/certo-r1-qwen3-4b", torch_dtype="bfloat16", device_map="auto")
body = ("Is the minimum rest period satisfied?\n\nContext: A shift ends at 20:00 UTC. The next shift begins "
        "at 06:00 CET (UTC+1) the following day. At least 11 hours of real rest are required.\n\nOptions:\n"
        "A) at least 11 hours of real rest\nB) less than 11 hours of real rest\n\n"
        "Reason in at most two short sentences, then end with a line 'FINAL ANSWER: <letter>'.")
ids = tok.apply_chat_template([{"role":"user","content":body}], return_tensors="pt",
                              add_generation_prompt=True, enable_thinking=False).to(m.device)
print(tok.decode(m.generate(ids, max_new_tokens=96, do_sample=False)[0][ids.shape[1]:], skip_special_tokens=True))

Decoding: greedy (temperature 0); the answer is the letter after FINAL ANSWER:.

Related: Certo-SR (experimental self-router)

A follow-up merges the decider into this backbone — one model with a fast option-scoring head plus this LM reasoning head, generating only when the head is unsure (~⅓ the serving memory). On a pre-registered fresh confirmation the self-route was not accuracy-non-inferior to always-reasoning (routing trades ~2.5 pp for speed/memory), so it is kept experimental. Certo-R1 remains the released reference.

Scope & caveats

• Validated on ten short calculation/compositional families; not a general chat model, not validated on long documents or other task types.
• Trained on deterministic oracle-verified rationales; the evidence is held-out generalization, not train loss.
• Emits an argmax answer + rationale, not a calibrated probability.

See certo_manifest.json for full training/eval provenance and hashes.