Typed decisions — choice, score, noul — about any text or JSON state, from one encoder pass.
Every question of a call shares one encoding of the state: 3 ms for one question, 11 ms for ten questions over a 500-token document.
A self-hosted, Jev-compatible, faster and better-calibrated successor to Laya.

Results at a glance

Measured on one RTX 4090, cbjev and Laya side by side on byte-identical cases (BENCHMARKS.md). Jev numbers are third-party published figures; there was no TypeSafe API access, so they are indicative only.

cbjev is faster than both Laya paths in every one of the 10 latency cases measured, by 1.5x (one question on a long document) to 6.9x (ten questions on a document).

---

What is different from Laya

Laya encodes the state once per question: ten questions about a 500-token document means ten 500-token sequences. cbjev packs a call into one row — the state once, then every question as its own segment:

Laya   [CLS] q1 [SEP] opts [SEP] state [SEP]      (x N questions)
cbjev  [CLS] q1 | q2 | ... | qN | state [SEP]     (once)

The attention mask keeps questions from seeing each other, while the state reads all of them. Each question segment restarts its positions right after [CLS], so with a single question the row is token-for-token and position-for-position exactly what a Laya checkpoint was trained on — which is why fine-tuning from Laya starts from Laya's full ability instead of relearning it.

On top of that:

• Own encoder forward. A from-scratch ModernBERT/mmBERT implementation (cbjev/model.py) with an explicit attention mask, bf16 matmuls over an fp32 residual stream, and CUDA-graph replay per shape bucket (cbjev/engine.py) — a call is one graph launch instead of ~300 kernel launches.
• Cheaper CPU side. The state is tokenized once per call, question text is tokenized once and cached, and everything is shipped to the GPU in one pinned copy.
• Retrained checkpoints. Fine-tuned from Laya on 35 public datasets in many question framings, distilled against both English Laya checkpoints, with temperatures fitted on a held-back dev split.
• Optional order voting. cbjev.load(order_votes=2) also asks every choice/score question with its options reversed and averages the answers — one extra short segment, not another pass.
• Laya checkpoints still run. cbjev.load("laya") runs the original weights in their own layout (verified to match Laya's probabilities within bf16 rounding).

Install

python -m venv .venv
.venv/bin/pip install -e .            # core
.venv/bin/pip install -e ".[serve]"   # + HTTP server

Weights download on first use from Hugging Face: 0010101010-1/cbjev (English at the root, multilingual/ for the multilingual checkpoint). To use weights you trained yourself, put them in $CBJEV_HOME/cbjev/ and $CBJEV_HOME/cbjev-multilingual/ (default ~/.cache/cbjev) or pass a path to cbjev.load(...); see Training.

Quickstart

import cbjev

agent = cbjev.load()                                   # English checkpoint, GPU if available
res = agent.predict(
    {"subject": "Duplicate charge on invoice #4411",
     "body": "We were billed twice for March. Refund it today or we cancel."},
    {
        "department": {"type": "choice", "instructions": "Which team should handle this?",
                       "criteria": {"billing": "invoices, payments, refunds", "technical": "bugs, outages",
                                    "sales": "pricing, contracts", "other": "everything else"}},
        "urgency": {"type": "score", "instructions": "How urgent is it?",
                    "criteria": ["can wait", "this week", "today", "blocking right now"]},
        "churn": {"type": "noul", "instructions": "Does the customer threaten to cancel their subscription?"},
    },
)
res["answers"]["department"]["choice"]      # 'billing'
res["answers"]["churn"]["noul"]             # P(true), ~0.78

Many states at once: agent.predict_batch(states, questions).

Routing between languages

from cbjev import Router
router = Router()                                      # english + multilingual, loaded lazily
router.predict({"body": "Mir wurde zweimal abgebucht"}, questions)["routing"]
# {'model': 'multilingual', 'reason': "Latin script, language looks like 'de'"}

HTTP server (Jev wire format)

cbjev-serve                              # 127.0.0.1:8000, POST /v1/systemone
CBJEV_API_KEY=secret CBJEV_DEVICE=cuda CBJEV_PRELOAD=1 cbjev-serve

Command line

cbjev "I was charged twice, please refund"            # routing decision only, no model load
cbjev "I was charged twice, please refund" --predict  # triage preset answers as JSON

Presets and e-mail

cbjev.triage_questions(), guard_questions(), moderation_questions(), router_questions(), email_questions(); cbjev.email_state(subject, body) strips quoted history, signatures and disclaimers before the model sees an e-mail.

Benchmarks

See BENCHMARKS.md for every number, how it was measured, and how to reproduce it.

python benchmarks/accuracy.py --engines laya,laya-td,cbjev --out benchmarks/results/accuracy.json
python benchmarks/speed.py    --engines laya,laya-fast,cbjev --out benchmarks/results/speed.json
python benchmarks/plot.py

Training

python training/build.py --out .work/data                  # 35 public datasets -> cases + teacher answers
python training/train.py --data .work/data --out ~/.cache/cbjev/cbjev --repeat typed_decisions=8
python training/calibrate.py --ckpt ~/.cache/cbjev/cbjev --data .work/data

training/duty.py --duty 0.5 -- <command> runs any of these on part of the GPU if you share the machine.

Honest limits

• Not better everywhere. Against the better of Laya's two English checkpoints, cbjev trails on 4 of 15 suites: AG News (-0.8 points, 3 cases of 400), DAIR emotion (-2.5), prompt injection (-3.4, 116 cases, half of them German) and support triage (-4.0). Five training rounds and several weight soups moved these by a point or two at most, so treat them as real.
• Many labels still hurt. Banking77 with all 77 intents in one question: 0.620, better than Laya's 0.497 but well behind Jev's published 0.870. Prefer <= 30 options per question.
• The English checkpoint is English. German injection prompts are where it misses most; route non-English text to cbjev-multilingual (the Router does this).
• Answers can depend on the other questions in the call. The state reads every question of the call, so adding or removing a question can move another question's probabilities slightly. On single-question calls cbjev reads exactly the sequence Laya would.
• Train-split suites are not zero-shot. Six suites use datasets whose train split is in the mix (Laya's documentation lists the same datasets in its own). The nine held-out suites are the fair zero-shot test, and cbjev leads on seven of them.
• The training mix was iterated with the benchmark suites in view. Calibration temperatures were fitted only on the dev split, never on benchmark cases, but choices such as the per-source teacher mix were made after looking at benchmark results.
• Weights live on Hugging Face, not in git (about 800 MB each); cbjev.load() fetches them, and training/ rebuilds them from scratch in about an hour per checkpoint on an RTX 4090.

Support the project

If cbjev saves you time or GPU money, you can buy me a coffee:

License

GPL-3.0-or-later (see LICENSE). cbjev's checkpoints are fine-tuned from the Apache-2.0 Laya checkpoints by Convai Innovations; see NOTICE for attribution and the datasets used.