Ternary Bonsai 2 27B Refusal Steering (GGUF)

Name: Ternary-Bonsai-2-27B-Refusal-Steering-GGUF
Author: com-kotobalabs

Experimental refusal-direction control vector for
prism-ml/Ternary-Bonsai-2-27B-gguf.

This repository does not contain a fully uncensored model. It contains a 1.3 MB
GGUF control vector and the reproducibility material used to test refusal steering.
The held-out evaluation did not support a reliable uncensored claim, so this
artifact is published under its measured scope.

Measured status

Base artifact: Ternary-Bonsai-2-27B-PTQ1_0.gguf
Base revision: 6ed5e12bf84b7a63069882c91dd9e9218647d17b
Runtime: PrismML-Eng llama.cpp fork at 9a9394a895b96003ca842a6041cb28ac49a108f7
Hardware: Apple M1 Max, 32 GB unified memory
Extraction: mean harmful-minus-benign activation at the final prompt token,
normalized independently for layers 1–63
Recommended experimental scale: -2.0, layers 10–63
Base refusal check: 4/4 tested categories refused
At -2.0 one training prompt changed internal reasoning toward conditional
compliance, but its final answer still refused
Held-out check at -2.0: 4/4 still showed refusal-oriented reasoning
At -3.0 over layers 10–63 output quality collapsed
At -3.0 over layers 20–40 benign arithmetic remained correct (17 x 23 = 391),
but 4/4 held-out prompts still refused

These are small diagnostic runs, not a safety or capability benchmark.

Use

The base model requires Prism ML's llama.cpp fork.

git clone https://github.com/PrismML-Eng/llama.cpp
cmake -B llama.cpp/build
cmake --build llama.cpp/build -j --target llama-server

hf download prism-ml/Ternary-Bonsai-2-27B-gguf \
  Ternary-Bonsai-2-27B-PTQ1_0.gguf --local-dir models

hf download com-kotobalabs/Ternary-Bonsai-2-27B-Refusal-Steering-GGUF \
  refusal-direction.gguf --local-dir models

llama.cpp/build/bin/llama-server \
  -m models/Ternary-Bonsai-2-27B-PTQ1_0.gguf \
  -ngl 99 -fa on \
  --control-vector-scaled models/refusal-direction.gguf:-2.0 \
  --control-vector-layer-range 10 63

Negative scale suppresses the measured harmful-minus-benign direction. Positive
scale strongly amplifies it and is not useful for this purpose.

Reproduce

extract_refusal_vector.py loads the original ternary GGUF through Prism's MLX
runtime, records every decoder-layer output for paired prompts, takes the
difference at the final prompt token, averages each layer, normalizes it, and
writes a llama.cpp-compatible control-vector GGUF.

The published Prism ML runtime rejected 96 BF16 auxiliary tensors in this GGUF.
runtime-bf16.patch adds the GGUF reader's standard dequantize path for those
tensors. Apply it to the runtime bundled with
prism-ml/Ternary-Bonsai-2-27B-mlx-2bit before running the extractor.

The paired prompts are included. They are intentionally small and should be
expanded before treating this as more than an experiment.

License and attribution

The base model is Apache-2.0. See LICENSE and NOTICE.txt. This repository does not
redistribute the 5.95 GB base weights.

Related base-model measurements (2026-09-20)

The available performance evidence covers prism-ml/Ternary-Bonsai-2-27B-gguf PTQ1_0 and PQ2_0 artifacts, not this Refusal-Steering GGUF. The PTQ1_0 M1 Max decode result (9.55 tok/s) and hosted PQ2_0 prompt-prefill results therefore must not be attributed to this exact derivative. This derivative's own throughput remains unmeasured in the cited evidence. See the artifact-by-artifact record: https://github.com/kotoba-lang/inference/blob/main/verify/evidence/model-inference-speed-analysis-20260924.md.

Publisher

Published by Mithril (formerly Kotoba Cloud), operated by Kotoba Labs Inc. The Hugging Face handle com-kotobalabs is unchanged. Support: support.mithril.fund.

Ternary-Bonsai-2-27B-Refusal-Steering-GGUF