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.
