bonzi-27b-v2-jev

Bonsai 2 27B as a typed decision function. No weights here. This is a recipe and a measurement for running prism-ml/Ternary-Bonsai-2-27B-gguf as a Jev-style System One model: one forward pass in, a calibrated probability per option out.

Asked "Is the sky blue?", a real row from this model's own run:

{"choice": "true", "probabilities": {"true": 0.99331, "false": 0.00669},
 "label_mass": 0.992448, "prompt_tokens": 95, "forward_s": 1.494}

Code: https://github.com/NicolaiLassen/open-bonzi-jev

Where this comes from

1. TypeSafe shipped Jev and named the category: models returning typed probabilistic decisions instead of text.
2. TheoLeeCJ open-sourced the mechanism as SemIf, originally released as OpenJev, reading option logits straight out of a frozen open model.
3. This ports that onto PrismML's Bonsai low-bit models.

score.py is an independent re-implementation of SemIf's "direct" mode with its own prompt. No SemIf code is used, so these numbers are not comparable to SemIf's published benchmarks. Not affiliated with TypeSafe, PrismML, or TheoLeeCJ.

This model

Weights	Ternary-Bonsai-2-27B-PTQ1_0.gguf
Quantization	ternary (PTQ1_0)
On disk	5.95 GB
Runtime	PrismML fork
WANLI-256	74.6% (chance 33.3%)
easy100	100%
Throughput	24 decisions/min
Median latency	2443 ms
Median label_mass	0.993

This model needs PrismML's llama.cpp fork. Its ternary weights carry a folded-in Hadamard rotation that stock llama.cpp cannot apply, and it does not implement the tensor type at all. Stock refuses the file at load with invalid ggml type. Build the fork with scripts/build_llama_fork.sh.

Against the rest of the family

Apple M4 Pro, GPU, ctx 4096, warmup excluded. This model ranks #1 of 6 on decision quality.

Model	On disk	WANLI-256	decisions/min
Bonsai 1 1.7B	0.25 GB	52.0%	925
Bonsai 1 4B	0.57 GB	60.2%	398
Bonsai 1 8B	1.16 GB	64.5%	230
Bonsai 1 27B	3.80 GB	71.1%	31
Ternary Bonsai 1 8B	2.18 GB	65.2%	200
Bonsai 2 27B	5.95 GB	74.6%	24

Usage

git clone https://github.com/NicolaiLassen/open-bonzi-jev
cd open-bonzi-jev
./scripts/build_llama_fork.sh
python3 scripts/download_model.py Ternary-Bonsai-2-27B

./openjev/score.sh -q "Is the user angry?" \
    --state '{"msg":"this is broken AGAIN"}' \
    --model bonzi/models/Ternary-Bonsai-2-27B

Options default to true,false; pass --options a,b,c for up to 26.

How it works

A lettered multiple-choice prompt is built from the state, question and options. The chat template is applied with thinking disabled, so the assistant turn begins exactly where the answer letter goes. One forward pass runs, and the scorer softmaxes over just the option-letter tokens.

Limitations

• label_mass is not confidence in correctness. It reports how much probability mass landed on the option letters, meaning you got an answer, not a right one. Bonsai 1 8B answers "does a spider have two legs?" with true at 0.998 confidence and label_mass 0.9997.
• Probabilities are conditional on the options you supplied, and are not calibrated for your workload. Validate on your own data before branching on a threshold.
• WANLI-256 is 256 rows. Treat the difference between neighbouring models as indicative, not decisive.
• Text only. The 27B packs include a vision tower that is never used here.

Licence

MIT for the code. Bonsai weights are Apache 2.0 from PrismML and are not redistributed. The downloader fetches them from source.