System One, open — a Jev-style decision model you can run yourself

State in, typed calibrated decisions out, one forward pass, no decoding. An open replica of TypeSafe's Jev built on Gemma 4 E2B (attention LoRA) and Gemma 3 270M, trained and served on Modal. MIT licensed.

	Jev (published)	This replica (Gemma 4 E2B)
TypeSafe public eval, strict common subset (343 pairs)	86.9%	76.7%
Same eval, stock Qwen 7B (jev-on-a-laptop study)		73.8%
27-question launch-demo ticket, one call	114 ms	97 ms (H100)
1,000 real emails, 4 decisions each		13.4 s · 74.6 emails/s · 95.4% spam accuracy
Demo task families (Doom, smart home, support, security, invoice, agent trace, catalog)		98.8% · ECE 0.003
Held-out task types (never trained)		74.8%
Weights	closed, API only	open (merged safetensors on the Modal volume; HF upload pending)
Price	$0.042 / M input tokens	GPU seconds on your own L4/H100, or CPU

Full report with baselines and caveats: https://claude.ai/artifact/8xJE6T4ovza9JwAFpMbQay

Eight demos, all real recordings of the live model, no task-specific fine-tuning

#	Page / script	Replica of	What is real
1	/	TypeSafe presets	4 scenarios, 28 typed fields each, one call
2	/race	launch-video terminal race	the 27 launch questions, 97 ms on an H100
3	/emails	@ryanvogel inbox classifier	1,500 Enron emails, 74.6/s, 95.4% spam accuracy
4	/viral	@rileybrown viral-post analyzer	judged 500 ms after you stop typing
5	games.py --game doom	TypeSafe's Doom	ViZDoom (freedoom), model picks every action from labels + depth + game vars
6	rec/agent.mjs	@gregpr07 browser-use agent	real Google Flights, model picks element + operation each step
7	/drive	@Neel490 self-driving sim	lidar sectors, code reflexes, model makes the tactical call at ~3 Hz
8	/home	TypeSafe smart-home demo	typed commands -> device, action, ambiguity, confirmation

Base URL: https://mithalouni--jev-serve-e2b-full-server-web.modal.run (scales to zero; first request after idle takes ~60 s).

Dropped on purpose: a Super Mario Bros replica of @faadilhshaik's demo. The base model played Doom zero-shot but stood still in Mario ("noop" ~77%); Jev played it zero-shot. A 10-minute task fine-tune fixed it, but that would not be a fair comparison, so it is not one of the demos. games.py --game mario still runs it if you want to see the failure. Videos in media/ were recorded from these pages on one H100.

State in, typed calibrated decisions out, one forward pass, no decoding. Three primitives, same shape as TypeSafe's Jev API: choice (distribution over options + confidence), score (distribution over ordered levels + expected level), noul (P(true)). Up to 52 options in a single pass (A–Z, a–z are single tokens in Gemma's vocabulary); larger option sets are scored in chunks with a "none of the above" slot and a final round.

Everything runs on Modal. Nothing is installed locally except the modal CLI.

Layout

• s1/schema.py prompt format (<state>…</state> + one Answer k: ( slot per question), tokenisation, collate
• s1/data_real.py 92 public HF decision datasets (intents, routing, moderation, NLI, fact-check, relevance, QA, rubric scores, pairwise judging, tool selection, agent next-action); 23 are held-out (never trained)
• s1/data_synth.py rule-based generators for the demo families: doom game state, smart home, support triage, security alerts, AP invoice packets, agent-trace observability, catalog alignment (exact teacher labels)
• s1/modal_common.py shared Modal image/volume; S1_GPU picks the GPU (or none for CPU)
• s1/doom_sim.py closed-loop arena: run the model as a live policy vs teacher and random
• s1/engine.py slot-logit scoring, decide(state, questions) API, option chunking, temperature
• build_data.py builds the mixture (one CPU container per task) -> volume /vol/data/v1
• typesafe_eval.py rebuilds TypeSafe's public eval (20 cases / 372 reference pairs) from evals.typesafe.ai
• train.py CE + Brier training, periodic in-task / held-out eval, temperature scaling, save
• evaluate.py in-task, held-out, synthetic, TypeSafe head-to-head (strict common subset vs Opus/Sol/Jev), latency
• serve.py one-origin FastAPI app: GET / interactive demo (4 preset scenarios, 28 fields each), POST /decide (Jev-style or schema-style JSON), GET /info
• s1/demo_presets.py, s1/demo_page.py demo scenarios (adapted from jev-on-a-laptop, MIT) and the demo page

Run order

modal run build_data.py                                   # ~5 min, CPU only
modal run typesafe_eval.py                                # seconds
S1_GPU=H100 modal run train.py --name lite-270m --model unsloth/gemma-3-270m-it --tier lite
S1_GPU=H100 modal run evaluate.py --run lite-270m
S1_GPU=H100 modal run train.py --name e2b-full --model google/gemma-4-E2B-it --tier full --lora 1
S1_GPU=H100 modal run evaluate.py --run e2b-full
S1_GPU=L4 S1_RUN=e2b-full modal deploy serve.py
./demo_client.sh https://<workspace>--jev-serve-e2b-full-server-decide.modal.run

S1_GPU=none runs any app on CPU (smoke tests).

Live demo

https://mithalouni--jev-serve-e2b-full-server-web.modal.run/ (E2B on an L4; scales to zero after 2 min idle, first request after idle takes ~60 s)