license: apache-2.0
base_model: prism-ml/Ternary-Bonsai-2-27B-gguf
tags:

• gguf
• llama.cpp
• abliteration
• ternary
• ptq1_0
• uncensored
• prism
• bonsai
• qwen35
  pipeline_tag: text-generation

Ternary Bonsai 2 27B — Abliterated (PTQ1_0 GGUF)

Refusal-ablated Ternary Bonsai 2 27B, edited directly on the ternary lattice and shipped
in PrismML's native PTQ1_0 pack.

• 5,946,648,928 bytes — byte-for-byte the size of the official PTQ1_0 release
• No requantization, no BF16 bake, no runtime steering, no control vector at serve time
• Requires the PrismML llama.cpp fork

Differs from PrismML's release in exactly 98 tensors and nowhere else. The other 753
tensors are byte-identical, including token_embd and output. Anyone with both files can
check that without trusting us.

Results

Paired against the untouched PrismML release: same card, same runtime, same flags, same
seed, enable_thinking: false on both arms. Judge is Qwen3.8-27B-OBLITERATED-Q8_0 with the
StrongReject rubric; the prefix grader is reported beside it as a cheap second opinion.

Refusal — SimpleSafetyTests, n=100

Benign over-refusal — XSTest-safe, n=250 (lower is better)

Over-refusal went down, not up. The model answers every harmless prompt in the set,
which the stock model does not.

Capability — HumanEval-164, greedy, paired per problem

Per problem: 129 both pass, 28 both fail, 3 gained, 4 lost.
McNemar exact two-sided p = 1.000 (discordant n=7) — no detectable capability change.

The empty-completion and unparsed counts are listed deliberately. An empty answer scores as
compliance under any prefix grader, so a 0% refusal rate means nothing without them. An
earlier run of this same eval produced "0/100 refusal" purely from 82 empty answers, because
thinking consumed the token budget before the model reached its reply.

Why this is not a normal abliteration

Standard abliteration projects a refusal direction out of the residual-writing tensors:
W ← W − λ·r·(rᵀW). On a ternary checkpoint that does nothing at all.

Bonsai 2's weights take three values per group of 128: {−s, 0, +s}. Projection asks each
weight to move about 1.4% of its magnitude while the lattice step is 100%, so repacking
rounds every weight straight back. Measured on real writer tensors:

This is a trap worth naming. A projected-then-repacked model passes a file-size check
and a quantizer reproducibility check while containing no abliteration whatsoever. If you
build one of these, diff the tensors against the base release. Size is not evidence.

The per-group scales cannot carry the edit either — a scale can only scale, while the
correction it must express varies across the 128 weights in its group. That is the 1.1% row.

Method

The quantity to remove is not each weight but the row sum c = rᵀW, one value per column. A
single flipped digit moves c by r_i · s · Δ, a whole lattice step — enormous next to the
1.4% nudge projection wanted. So the edit does not need many digits, it needs the right ones:
spend them where |r_i| is largest, since those buy the most change in c per unit of
damage to W. Greedy, one pass over rows in descending |r_i|, clipped so every digit stays
in {−1,0,+1} and every group scale is untouched.

• Writers only: ffn_down, ssm_out, attn_output — 98 tensors
• Blocks 15–63. Early blocks deliberately untouched; including them destroys coding.
  Corroborated three ways: huihui's Qwen3.8 recipe skips 0–14, our own all-blocks arm
  collapsed to 0.233 answer quality, and Hikari07jp restored L0–7 on Bonsai 1 to recover
  coding from 16/20 to 19/20.
• λ = 1.0, top 512 rows per tensor, 0.24% of digits flipped
• Direction transferred from the Qwen3.8-27B parent, recovered from huihui's released weights
  by rank-1 decomposition (σ₁/σ₂ ≈ 23)

Cost, stated plainly: |dW|/|W| = 0.071, roughly 5× what a true projection costs on a
full-precision model, because a coarse lattice cannot make small corrections. The HumanEval
result above is what that cost bought — apparently nothing, which is the interesting part.

Rebuilding this

Default llama-quantize does not reproduce the release — it yields 7.11 GB because
output.weight and token_embd.weight land at Q6_K/Q4_K. The released configuration is:

llama-quantize \
  --output-tensor-type PTQ1_0 --token-embedding-type PTQ1_0 \
  --tensor-type ssm_alpha=bf16 --tensor-type ssm_beta=bf16 \
  in-F16.gguf out-PTQ1_0.gguf PTQ1_0 16

The 96 ssm_alpha/ssm_beta tensors cannot be reproduced from the released F16 at all: the
PTQ1_0 release stores them BF16 (7 mantissa bits) while the F16 file holds 10, so packing
truncates them ~0.4%. They are spliced verbatim from the base release here.

Usage

git clone https://github.com/PrismML-Eng/llama.cpp
cmake -B build -DGGML_CUDA=ON && cmake --build build -j

./build/bin/llama-server \
  -m Ternary-Bonsai-2-27B-Abliterated-PTQ1_0.gguf \
  -ngl 99 -c 8192 --flash-attn on --jinja

Benchmarks above were run with --chat-template-kwargs '{"enable_thinking": false}'. With
thinking enabled, budget generously: this model reasons ~25% longer than stock, and a 256
token cap leaves most answers empty.

Limitations

• Abliteration is a behavioural change, not a safety property. This model will produce
  unsafe content if asked, including in categories stock refuses outright. Use under your
  own policy and applicable law.
• Requires the PrismML fork; stock llama.cpp cannot load PTQ1_0.
• The direction is transferred from the Qwen3.8 parent, not fitted on Bonsai 2 itself. A
  natively fitted direction may do better and has not been tried.
• Same-condition lab measurement, not a leaderboard reproduction. Single greedy sample per
  problem; at n=164 the resolution floor is ~6.5 points.
• Vision mmproj not included (text only).

Prior art and attribution

• Base weights and format:
  prism-ml/Ternary-Bonsai-2-27B-gguf (Apache-2.0)
• Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF
  did this first, on Bonsai 1, with the same family of method (direct ternary code edit
  on writers). Their early-block rollback is the degradation fix and is prior art for the
  block choice here. This release is Bonsai 2 and adds the measured mechanism: why
  projection fails on ternary weights, and what the lattice costs.
• Architecture lineage: Qwen3.5 / Qwen3.8-27B hybrid attention

License

Apache-2.0, same as the base release.