Plumb-4B

A 4B decision model: give it evidence, a question and 2-16 options, and it returns a probability for every option from one forward pass, with no generated tokens. Probabilities use a single temperature (T = 2.07) fitted on held-out decisions. Trained on hard decisions written and double-checked by Qwen3.8-27B, with the questions it got wrong or was unsure of weighted up.

Split	n	Accuracy	ECE	p50 latency
easy	48	1.000	0.056	28 ms
standard	72	0.972	0.152	28 ms
hard	111	0.802	0.094	78 ms

On JevBench hard, Plumb-4B scores 89/111 (80.2%) versus 82/111 (73.9%) for JevK5 v0.2: 8 items fixed, 1 regressed. Easy and standard accuracy are unchanged.

Public JevBench items, JevBench's own runner, RTX 4080 Super. No JevBench item was used for training, tuning, checkpoint selection or calibration. Aggregate results on the public JevBench set did inform later training-recipe decisions; see https://github.com/crh225/plumb for the full disclosure, audits and per-item results.

Training data: crh225/plumb-decisions. GGUF for llama.cpp and Ollama: crh225/plumb-4b-GGUF.

Use it

It runs with the open jevk5 runtime (Apache-2.0), which reads the temperature from jevk5_config.json in this repo. It needs a CUDA GPU with about 10 GB free (bf16).

pip install "jevk5[fast] @ git+https://github.com/allebee/jevk5@v0.2.0"

From Python, one decision per call:

from jevk5 import JevK5

model = JevK5("crh225/plumb-4b")

model.decide(
    "Refunds need a receipt and a purchase within 30 days. "
    "The customer bought 12 days ago and has no receipt.",
    {"type": "noul", "instructions": "Is a refund permitted under the policy?"},
)
# {'type': 'noul', 'noul': <probability of true>, 'confidence': ..., 'input_tokens': ...}

model.decide(
    "Order #7120 shows delivered to No. 17; the customer lives at No. 71.",
    {"type": "choice", "instructions": "What happened to the parcel?",
     "criteria": ["delivered", "misdelivered", "unknown"]},
)
# {'type': 'choice', 'choice': ..., 'probabilities': {...}, 'confidence': ..., 'input_tokens': ...}

Question types: noul (true/false), choice (2-16 options, a list or a {key: description} map) and score (ordinal levels). Every answer is a full probability over the options, from one forward pass, with no generated tokens.

As a service, with a TypeSafe-style request shape (several questions about one state per request):

jevk5-serve --model crh225/plumb-4b --port 8090
curl -s localhost:8090/v1/systemone -d '{"state": "I was billed twice, please refund",
  "questions": {"refund": {"type": "noul", "instructions": "Asks for money back?"}}}'