Ternary-Bonsai-2-27B — Uncensored by Gastón Parravicini

The 27B model that every abliteration tool said couldn't be touched — edited anyway, with its brain intact. A new liberation technique: refusal-steering compiled straight into the weights. No fine-tuning. No reference model. No lobotomy.

Refusal reflex removed at the weight level · MTP draft head grafted · runs on a single RTX 3090 · honestly measured — no inflated numbers. See How we measured.

Published by Gastón Parravicini

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TL;DR

Bonsai-2 is built to be un-abliteratable by every existing tool: its weights are ternary and Hadamard-rotated, and it cannot be fine-tuned in llama.cpp (there is no backward pass for its state-space layers). Subtract the refusal direction the normal way and nothing happens.

This release edits it anyway, with a different method entirely. What that buys you, measured rigorously on a single RTX 3090:

✅ The explicit refusal reflex is gone — the hard "I cannot / I won't" wall drops to ~0 on adversarial prompts (shipped scale 16). ✅ Capability largely retained — perplexity +8.4% at the shipped scale; the technique reaches +3.3% at a lower, less-aggressive setting. ✅ Reasoning fully intact — the <think> chain still thinks. ✅ Stays coherent under adversarial load — it keeps producing a usable answer where a reference abliteration of the same base collapses into reasoning loops on most hard prompts. ✅ Tunable — a single scale knob trades removal strength for perplexity.

What it does not do: it does not turn Bonsai-2 into a model that will hand you operative harmful content on demand. Bonsai-2's safety is deep, and this technique removes the surface refusal without replacing it with wholesale compliance — see the honest numbers below.

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The honest picture (read this)

Most "uncensored" model cards lead with a number like "95% refusal removal." We measured ours the careful way and found that that number, as usually computed, is largely an artifact (see the methodology section). Here is what our model actually does on hard adversarial prompts (AdvBench, 50 prompts, hand-classified, 1024-token generations so nothing is truncated):

On a mild refusal set (targeted-harassment prompts the stock model refuses but where complying is harmless), removal is high (31/32) — mild prompts don't trigger the deep-safety deflection that hard prompts do. Efficacy is strongly prompt-difficulty-dependent, and we think a card should say so plainly.

The interesting result isn't a big compliance number — it's that surface abliteration of a deeply safety-trained reasoning model removes the refusal token but re-routes the behavior into deflection, not compliance. That's a real, reproducible finding, and it's why we ship this with open scripts and honest numbers rather than a headline percentage.

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How it compares

Measured against a reference abliteration of the same base model, identical rigorous protocol (50 hard prompts, 1024-token generations, hand-classified):

Takeaway: neither approach is a clean "uncensor," and both would report ~90% under the naive classifier. The reference model is slightly more compliant when it answers but loops on 70% of hard prompts — largely unusable under adversarial load. This model is far more coherent (answers ~2× as often, no loops) with comparable or better capability retention. Different point on the same trade-off: coherence + capability vs. raw compliance.

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What makes the method different

Independently of the efficacy numbers, the technique itself is novel and works on a model class that defeats every off-the-shelf approach:

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How we measured (and why most "uncensored" numbers are inflated)

This is the part we think matters most. The standard way to score "refusal removal" is a keyword classifier: generate a short completion, and if it doesn't contain "I cannot / I'm sorry / I won't," call it a success. On a reasoning model that method is badly broken, in three ways we verified:

1. It reads the <think> block. The model's internal reasoning routinely contains "I should be careful / I should not," so a completion that ultimately complies gets scored as a refusal (false negative) — or, at higher steering, the reasoning stops hedging and pure deflections get scored as compliance (false positive).
2. Short generations truncate the answer. With a 200-token cap, the <think> block eats the budget and the actual answer never appears — scored as whatever keyword happened to land.
3. Deflection looks like compliance. "How to make a homemade bomb → here's a baking-soda volcano" contains no refusal keyword, so it scores as a success. It is not.

Our numbers above use 1024-token generations, the answer body only (post-</think>), and manual classification into comply / deflect / loop / refuse. Under the naive classifier this same model scores 86–98%; hand-verified, genuine hard-prompt compliance is ~15%. We report the honest one.

The tool is included: scripts/honest_eval.py. It runs long generations, scores the answer body only, and auto-separates explicit refusal / no-answer (loop) / answered — then hands you the "answered" set to split into genuine compliance vs. deflection by eye, because that last call genuinely needs a human. It refuses to print a single inflated percentage. Run it on this model, on the base model, or on any other "uncensored" release and compare honestly:

python3 scripts/honest_eval.py --model <model.gguf> \
    --prompts advbench_harmful_behaviors.csv --n 50 --tokens 1024

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About Ternary-Bonsai-2-27B

The base model is prism-ml/Ternary-Bonsai-2-27B — a natively ternary (~1.58-bit) 27B model built on a Qwen3-27B foundation (the qwen35 hybrid state-space architecture, from the Qwen3.8-27B generation), released by prism-ml under Apache 2.0:

• 64 layers: 16 full-attention + 48 gated-delta-net (SSM) blocks; blk.64 is the MTP/nextn draft head
• n_embd 5120, n_ff 17408; a reasoning model — emits <think>...</think> before answering
• Multilingual (inherits Qwen3's broad language coverage)
• Shipped only quantized: PQ2_0 (2.13 bpw) and PTQ1_0 (1.75 bpw), plus MLX
• Weights stored Hadamard-rotated (prism.hadamard, QuIP#-style, block 1024)

Base model collection: huggingface.co/collections/prism-ml/bonsai-2 · upstream family: Qwen3.8-27B (Apache 2.0).

Not a standard Transformer — and that's exactly why nothing off-the-shelf can uncensor it.

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Why every standard tool fails here

Three brick walls, each verified on the actual model:

1. Orthogonalization does nothing. Projecting the writer matrices orthogonal to a refusal direction fails on this model, even with a perfectly recovered direction.
2. You cannot fine-tune it. ggml has no backward pass for the state-space operators (GATED_DELTA_NET, GATED_LINEAR_ATTN, SSM_CONV, SSM_SCAN). Gradient-based uncensoring is dead on arrival.
3. The weights are rotated. Edit them naively and your intervention lands in the wrong basis (Hadamard) — the effect washes out.

Run any existing abliterator on Bonsai-2 and you get a byte-for-byte identical model back.

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How it was done

A forward-only technique, derived entirely from Bonsai-2 itself — no other model touched.

1. Read the refusal decision from the model's own thoughts

Run the stock model on prompts it refuses vs. prompts it complies with, and grab the residual-stream state at the decision point (end of the <think> chain). The per-layer difference is g — the direction that turns "refuse" into "comply". No training, just a forward pass.

2. Teach it when to steer (conditional)

Capture the mean ffn_down input on refusal vs. comply contexts. Their difference v is the "this is a refusal context" detector. The edit is built to fire only on refusal-context tokens and stay silent on normal prompts — this is what keeps the model smart.

3. Compile the steering into the weights — in the right basis

For layers 15–63, a rank-1 term is added to ffn_down:

DeltaW[i, j] = u[i] * g_hat[j]
  g_hat = unit refusal->comply direction        (output side, natural space)
  u     = SCALE * T(v) / (v . h_refuse)          (input side)
  T     = the model's own Hadamard transform:    block-1024 Walsh-Hadamard of (sign (.) v)

Applying the exact Hadamard rotation the engine uses is the unlock — it puts the edit in the same basis as the rotated weights. Baked in F16, then re-quantized to the ternary formats.

4. The recipe

target:    ffn_down (layers 15-63)        # the FFN residual writer, in every layer
direction: model's own comply-vs-refuse activations, at the decision point
gating:    conditional on refusal-context input (v = h_refuse - h_comply)
basis:     model Hadamard transform (block 1024, sign-explicit)
scale:     16.0 (shipped — removes the hard refusal reflex) | 8.0 = higher capability, weaker removal
method:    forward-only  |  no gradient  |  no reference model

5. Why the brain survives

The steering is conditional — it fires on refusal contexts and is ~0 everywhere else — so it barely perturbs normal generation. Perplexity cost is +8.4% at the shipped scale 16 (and as low as +3.3% if you turn the knob down), versus the much larger hit a blunt weight edit would cause.

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Capability & efficacy

Measured on a single NVIDIA RTX 3090 (24 GB). Reproduce with the included scripts; the full breakdown is in benchmarks.txt.

Why scale 16 ships: at lower scale 8 the perplexity cost is only +3.3%, but the refusal reflex is not removed on hard prompts — it still refuses ~56% of AdvBench outright. Scale 16 is the setting where the refusal wall actually drops. Scale 32 and above only add reasoning loops and perplexity without more genuine compliance. The scale knob is exposed in bake_portable_f16.py if you want to trade removal strength for capability.

• ✅ Reasoning (<think>) fully intact
• ✅ Benign coherence verified (notes, explanations, summaries — all clean)

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Speed — MTP speculative decoding

The -MTP variants graft the blk.64 multi-token-prediction (nextn) draft head, so the model can draft its own tokens (self-speculative decoding). Measured on RTX 3090, code generation:

Honest result: the speedup is real and worth it on PQ2_0 for code/structured output (+31%; n-max 4 was the sweet spot — 3 gives +26%, 5 regresses). On PTQ1_0 the draft head did not accelerate decode in our tests, so for the 1.75-bpw quant just use the non-MTP file. Draft acceptance is high on code and low on free-form prose (little or no gain on prose either way).

Requires the MTP Hadamard-inverse fix. PrismML's ternary models store token_embd in a Hadamard-rotated basis; stock prism-llama's MTP draft graph reads it without the inverse transform and errors out (failed to create MTP context). The one-file fix is in-flight upstream (PR #205 / #217 / #230). Build a prism-llama that includes it, then run:

llama-cli -m Ternary-Bonsai-2-27B-Uncensored-Gaston-PQ2_0-MTP.gguf \
  -ngl 99 -c 4096 -fa on --spec-type draft-mtp --spec-draft-n-max 4 \
  -p "<|im_start|>user\nYour prompt<|im_end|>\n<|im_start|>assistant\n<think>\n"

The non-MTP variants have no such dependency and run on any prism-llama build.

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Open method & tools

Not a black-box drop. There's a full step-by-step TUTORIAL.md — reproduce this model, or apply the technique to other Hadamard-folded / ternary models. The technique, scripts and measurements ship in scripts/:

Direction and gating are derived only from Bonsai-2's own activations — no reference model, no gradient (impossible on this SSM arch), no fine-tuning.

Available quants

Same size as the official Bonsai-2 quant of the same format — embeddings and the output layer are kept at higher precision, exactly as the base release ships them (not compressed to save space).

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Requirements & installation

Bonsai-2 uses a custom arch (qwen35 hybrid SSM), custom quants (PQ2_0 / PTQ1_0) and Hadamard-folded weights that upstream llama.cpp does not implement. You need prism-llama — Prism's public llama.cpp fork — the same engine required to run the original Bonsai GGUF. This model needs no extra patch.

# 1. Clone Prism's llama.cpp fork
git clone https://github.com/PrismML-Eng/llama.cpp prism-llama
cd prism-llama

# 2. Build (NVIDIA)
cmake -B build -DGGML_CUDA=ON
cmake --build build -j --config Release

#    ...or on Apple Silicon
# cmake -B build -DGGML_METAL=ON
# cmake --build build -j --config Release

Binaries land in build/bin/. ~24 GB VRAM runs PQ2_0 fully on GPU with -ngl 99; it also runs CPU / partial-offload.

Usage

llama-cli

llama-cli \
  -m Ternary-Bonsai-2-27B-Uncensored-Gaston-PQ2_0.gguf \
  -ngl 99 -c 4096 --temp 0 \
  -p "<|im_start|>user\nYour prompt<|im_end|>\n<|im_start|>assistant\n<think>\n"

llama-server

llama-server -m Ternary-Bonsai-2-27B-Uncensored-Gaston-PQ2_0.gguf -ngl 99 -c 4096 --port 8080

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Notes

• Reasoning model: answers include a <think>...</think> block first. Expected and correct. The model tends to reason at length; give it enough tokens (-n 512+) to reach the answer.
• MTP / speculative decoding: the qwen35 arch supports a multi-token-prediction (nextn) draft head; -MTP variants include a grafted head. The liberation edit touches only layers 15-63.
• Ternary + Hadamard: needs a prism-llama build; upstream llama.cpp doesn't implement this arch/quant — same requirement as the base Bonsai release, nothing extra.
• Tunable: the edit is an additive term with a scale knob; lower scales trade a little efficacy for an even smaller perplexity cost.

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Base model

• Model: prism-ml/Ternary-Bonsai-2-27B
• Architecture: qwen35 hybrid — full attention + gated-delta-net (SSM), natively ternary
• Parameters: 27B
• Quant formats: PQ2_0 (2.13 bpw), PTQ1_0 (1.75 bpw), MLX-2bit
• Weight storage: Hadamard-rotated (prism.hadamard)

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License & attribution

Licensed under Apache 2.0, the same license as the base model. This is a modified derivative of prism-ml/Ternary-Bonsai-2-27B (itself derived from Qwen3-27B, Apache 2.0). The modification is the conditional refusal-steering edit to ffn_down (layers 15–63) described above; all other weights are unchanged. Base-model attribution and change description are retained per Apache 2.0 §4.

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Credits

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Responsible use

The explicit refusal reflex has been removed for research and red-teaming. As the numbers above make clear, this is not a model that reliably produces operative harmful content — but you are responsible for how you use it and for complying with applicable law and the base model's terms. Don't use it to harm people.

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Released by Gastón Parravicini — huggingface.co/gaston-parravicini Liberation technique: conditional refusal-steering compiled into the weights, in the model's own Hadamard basis — forward-only, no fine-tuning, no reference model — for a model class that resists every standard abliteration approach. Honestly measured.