circuit-1.7b

A System One decision model: typed questions in, calibrated probability distributions out, one forward pass, no text generation. It speaks TypeSafe's POST /v1/systemone contract and is the open-weights backend for decision-circuits.

circuit-1.7b is a LoRA adapter plus a pointer readout head on Qwen/Qwen3-1.7B-Base. A request packs a state and a question; each option is wrapped in delimiter tokens and the sequence ends with a decide token. The head scores every option's closing delimiter against the decide token and applies softmax. Those probabilities are the answer, trained with cross-entropy against outcome labels, so calibration is learned rather than derived.

Results

Same items for every model, human labels, accuracy / ECE (15 bins).

cold eval (1,200 items)	MultiNLI	SMS spam	Civil toxicity	CLINC 151-way
Jev (TypeSafe, API)	88% / 0.04	96% / 0.05	82% / 0.06	90% / 0.05
Bespoke-Nimble-9B	84% / 0.09	91% / 0.06	86% / 0.08	not supported (26-option cap)
kev-0.5b	46% / 0.28	50% / 0.30	62% / 0.16	62% / 0.17
circuit-1.7b	81% / 0.09	98% / 0.02	90% / 0.16	86% / 0.06

The four cold-eval tasks' train splits are in this model's training mix; they are presumably not in Jev's. Two checks nothing was trained on:

out of distribution	Jev	Nimble-9B	kev-0.5b	circuit-1.7b
100 water-utility calls, 11-way (Barney 2025)	98% / 0.02	93% / 0.05	80% / 0.13	92% / 0.08
generalization grid, 9 operations x 6 formats, code-labeled	95%	85%	48%	97%*
ClassicMiniDIY typesafe-bench, 546 production questions (agreement with Jev / ECE)	1.0 by definition	0.84 / 0.05	0.49 / 0.11	0.70 / 0.05

* the grid generator is ours, so this is held-out items, not held-out structure. Trained with an operation and a format withheld, the same recipe scores 91% on the unseen format and 57% on the unseen operation: layouts transfer, new kinds of judgment must be in the training data.

Known weakness shared with every model above, Jev included: on inputs built to be undecidable, mean confidence is 0.5 to 0.9 where it should be near 0. Calibration on ambiguity is the open problem.

Training

• Base: Qwen/Qwen3-1.7B-Base (Apache 2.0), frozen. LoRA rank 16 on all attention and MLP projections (17.4M params). Pointer head: two 2048 x 256 linear maps.
• Data, 16,738 items, every label computed by code or by humans, every source permissive (CC BY / CC0 / MIT / Apache): the generalization grid (8,100 + 838 oversampled ambiguous items with soft 0.5 labels), a commercial-safe slice of public classification tasks (2,100), and CC-licensed real data (MNLI, civil_comments, sms_spam, CLINC; 5,700). No teacher-model outputs.
• 2 epochs, batch 4, max 1,024 tokens, lr 1e-4 (LoRA) / 1e-3 (head), soft-target cross-entropy, early stopping on validation ECE. 61 minutes on one RTX 4090.
• Training code, data generators, and the evaluation harness: github.com/Barneyjm/circuit.

Use

Serve it with the circuit repo and point any System One client at it:

S1_MODEL=lora:runs/circuit-1.7b uv run python -m s1proto        # :8901

from decision_circuits import Circuit, Q, argmax
from decision_circuits.backends import SystemOne

c = Circuit()
c.noul("pii", "Does the message contain personal information?")
c.choice("dept", "Which team?", {"billing": None, "technical": None, "other": None})
c.gate("redact", Q("pii") >= 0.7, on_uncertain="escalate")
c.gate("route", argmax("dept", min_confidence=0.35))
out = c.run(SystemOne("http://localhost:8901/v1/systemone", api_key="x"), "Card charged twice, my card ends in 4412")

Files: adapter/ (PEFT LoRA), head.pt (pointer head, keys q.weight, k.weight), config.json (base, hidden size, head type, layout, training args).

Intended use and limits

Research and evaluation of calibrated decision models, and as a local backend for decision circuits. Not a production system for decisions that affect people. English only. Confidence on undecidable inputs is not yet reliable (see above); put thresholds inside an uncertainty band and route the band to a human, which is what decision-circuits does.

License

Adapter and head: Apache 2.0. Base model: Qwen license (Apache 2.0 for Qwen3).