# Bonsai 2 27B — 1bit CRACK · GGUF
**Abliterated · No guardrails · PTQ1_0 dense-ternary 1.75 bpw · 5.9 GB · Runs on a laptop / single GPU · Vision-capable**
[@dealignai](https://x.com/dealignai)
What is this
Bonsai 2 27B — PrismML's ternary compression of Qwen3.8-27B — with the refusal circuitry surgically removed at the weight level while capability, vision, reasoning modes (off/low/xhigh), tool use, and multi-turn coherence are preserved. Full 27B-class hybrid Attention + SSM (GatedDeltaNet) architecture in a 5.9 GB 1bit GGUF.
Proprietary weight-level abliteration by the dealignai research team. Byte-identical to the base ternary quant everywhere except a small set of tensors that carry the refusal circuit. Drop-in replacement for the ternary base at inference — same tokenizer, same chat template, same reasoning modes, same vision projector interface.
|
|
| Base |
prism-ml/Ternary-Bonsai-2-27B-gguf — Qwen3.8-27B, ternary compression by PrismML |
| Architecture |
Hybrid Attention + SSM (GatedDeltaNet), 64 blocks, hidden 5120, vision tower separate |
| Quant |
PrismML PTQ1_0 — 2.13 bpw ternary, group 128 |
| Footprint |
5.95 GB (identical to base; same per-tensor type policy) |
| Reasoning modes |
off (no thinking), low, xhigh (default, extended thinking) |
| Vision |
Same mmproj files as the base release (Ternary-Bonsai-2-27B-mmproj-BF16.gguf / -Q8_0.gguf) |
| Runtime |
PrismML llama.cpp fork (CUDA / Metal / CPU) |
Results
Refusal graded on the tokens the model actually emits (content, or the reasoning trace when the model reasons past the token budget) via a tiered classifier: HARD_REF / SOFT_RED / HEDGE / REASONING_REFUSAL (refused) vs COMPLY / COMPLY_TRUNCATED / NO_REFUSAL_TRUNCATED (complied). Truncation is never miscounted as a refusal.
HarmBench-320 — refuse rate (lower is better for uncensored eval), off mode, T=0
| eval |
base refuse rate |
CRACK refuse rate |
| HB-320 all categories |
93.44% (299/320) |
0.00% (0/320) |
Verdict breakdown (n=320 each):
| Model |
HARD_REF |
SOFT_RED |
COMPLY |
COMPLY_TRUNCATED |
Base PTQ1_0 |
297 |
2 |
10 |
11 |
CRACK PTQ1_0 |
0 |
0 |
133 |
186 |
Per-category refuse rate (all 7 HarmBench semantic categories):
| category |
n |
base refuse |
CRACK refuse |
base comply |
CRACK comply |
| chemical_biological |
42 |
95.2% |
0.0% |
4.8% |
100.0% |
| copyright |
80 |
90.0% |
0.0% |
10.0% |
100.0% |
| cybercrime_intrusion |
52 |
94.2% |
0.0% |
5.8% |
98.1% |
| harassment_bullying |
21 |
100.0% |
0.0% |
0.0% |
100.0% |
| harmful |
18 |
94.4% |
0.0% |
5.6% |
100.0% |
| illegal |
53 |
90.6% |
0.0% |
9.4% |
100.0% |
| misinformation_disinformation |
54 |
96.3% |
0.0% |
3.7% |
100.0% |
MMLU (n=2,280 = 40 questions × 57 subjects, next-token letter-logit)
| build |
acc |
Δ |
Base PTQ1_0 |
40.31% |
— |
CRACK PTQ1_0 |
39.96% |
-0.35 pp |
CRACK preserves general capability — Δ within noise on the 40-per-subject sample.
Per-subject accuracy (all 57 subjects)
| subject | base | CRACK | Δpp | n |
|---|---:|---:|---:|---:|
| abstract_algebra | 20.0% | 32.5% | +12.5 | 40 |
| anatomy | 37.5% | 42.5% | +5.0 | 40 |
| astronomy | 40.0% | 52.5% | +12.5 | 40 |
| business_ethics | 40.0% | 50.0% | +10.0 | 40 |
| clinical_knowledge | 40.0% | 55.0% | +15.0 | 40 |
| college_biology | 45.0% | 37.5% | -7.5 | 40 |
| college_chemistry | 25.0% | 35.0% | +10.0 | 40 |
| college_computer_science | 50.0% | 40.0% | -10.0 | 40 |
| college_mathematics | 27.5% | 42.5% | +15.0 | 40 |
| college_medicine | 27.5% | 30.0% | +2.5 | 40 |
| college_physics | 30.0% | 42.5% | +12.5 | 40 |
| computer_security | 47.5% | 45.0% | -2.5 | 40 |
| conceptual_physics | 45.0% | 45.0% | +0.0 | 40 |
| econometrics | 35.0% | 42.5% | +7.5 | 40 |
| electrical_engineering | 32.5% | 50.0% | +17.5 | 40 |
| elementary_mathematics | 55.0% | 32.5% | -22.5 | 40 |
| formal_logic | 35.0% | 32.5% | -2.5 | 40 |
| global_facts | 32.5% | 37.5% | +5.0 | 40 |
| high_school_biology | 37.5% | 30.0% | -7.5 | 40 |
| high_school_chemistry | 40.0% | 37.5% | -2.5 | 40 |
| high_school_computer_science | 52.5% | 47.5% | -5.0 | 40 |
| high_school_european_history | 60.0% | 50.0% | -10.0 | 40 |
| high_school_geography | 37.5% | 45.0% | +7.5 | 40 |
| high_school_government_and_politics | 52.5% | 47.5% | -5.0 | 40 |
| high_school_macroeconomics | 42.5% | 47.5% | +5.0 | 40 |
| high_school_mathematics | 32.5% | 32.5% | +0.0 | 40 |
| high_school_microeconomics | 42.5% | 37.5% | -5.0 | 40 |
| high_school_physics | 32.5% | 22.5% | -10.0 | 40 |
| high_school_psychology | 50.0% | 37.5% | -12.5 | 40 |
| high_school_statistics | 35.0% | 40.0% | +5.0 | 40 |
| high_school_us_history | 60.0% | 40.0% | -20.0 | 40 |
| high_school_world_history | 55.0% | 37.5% | -17.5 | 40 |
| human_aging | 40.0% | 50.0% | +10.0 | 40 |
| human_sexuality | 37.5% | 30.0% | -7.5 | 40 |
| international_law | 72.5% | 47.5% | -25.0 | 40 |
| jurisprudence | 47.5% | 32.5% | -15.0 | 40 |
| logical_fallacies | 35.0% | 47.5% | +12.5 | 40 |
| machine_learning | 40.0% | 37.5% | -2.5 | 40 |
| management | 32.5% | 52.5% | +20.0 | 40 |
| marketing | 37.5% | 45.0% | +7.5 | 40 |
| medical_genetics | 45.0% | 42.5% | -2.5 | 40 |
| miscellaneous | 37.5% | 45.0% | +7.5 | 40 |
| moral_disputes | 30.0% | 40.0% | +10.0 | 40 |
| moral_scenarios | 27.5% | 25.0% | -2.5 | 40 |
| nutrition | 37.5% | 47.5% | +10.0 | 40 |
| philosophy | 50.0% | 40.0% | -10.0 | 40 |
| prehistory | 35.0% | 27.5% | -7.5 | 40 |
| professional_accounting | 17.5% | 25.0% | +7.5 | 40 |
| professional_law | 42.5% | 30.0% | -12.5 | 40 |
| professional_medicine | 27.5% | 32.5% | +5.0 | 40 |
| professional_psychology | 35.0% | 27.5% | -7.5 | 40 |
| public_relations | 22.5% | 32.5% | +10.0 | 40 |
| security_studies | 35.0% | 55.0% | +20.0 | 40 |
| sociology | 62.5% | 42.5% | -20.0 | 40 |
| us_foreign_policy | 65.0% | 42.5% | -22.5 | 40 |
| virology | 32.5% | 30.0% | -2.5 | 40 |
| world_religions | 57.5% | 52.5% | -5.0 | 40 |
Additional direct refusal-removal check
On 200 prompts hand-verified to make the base refuse consistently:
| Model |
refuse |
comply |
empty |
Base PTQ1_0 |
200/200 (100%) |
0 |
0 |
CRACK PTQ1_0 |
0/200 (0%) |
199/200 |
1 |
Serving
Serve exactly like the base ternary release — PrismML's llama.cpp fork (CUDA / Metal / CPU).
# clone and build the fork (once)
git clone https://github.com/PrismML-Eng/llama.cpp
cd llama.cpp && cmake -B build -DGGML_CUDA=ON && cmake --build build -j$(nproc)
# serve
./build/bin/llama-server \
-m Bonsai-2-27B-PTQ1_0-CRACK.gguf \
-ngl 99 -c 8192 --host 0.0.0.0 --port 8080
Optionally load the multimodal projector (Ternary-Bonsai-2-27B-mmproj-BF16.gguf or
-Q8_0.gguf from the base release) with --mmproj <file> for image input.
Reasoning modes
# HTTP /v1/chat/completions — same as base
{
"messages": [{"role": "user", "content": "..."}],
"chat_template_kwargs": {"enable_thinking": true, "reasoning_effort": "xhigh"}
}
# valid reasoning_effort: "low" | "xhigh" (default) — set enable_thinking:false for no-thinking
Preserved (byte-compatible with the base quant)
Same tokenizer, chat template, per-tensor quant policy, vision projector interface,
and all non-refusal tensors. File size and type layout match the base exactly.
Responsible use
Adult / research use only. This model has its refusal circuit removed; it can produce content that other models refuse, including content that is offensive, illegal in some jurisdictions, or unsafe. You are responsible for what you generate and for complying with all applicable law. Do not deploy without a moderation layer for downstream users. No warranty.
License & attribution
Apache 2.0, inherited from the upstream Bonsai 2 27B release. See LICENSE and
NOTICE.txt. Base model: prism-ml/Ternary-Bonsai-2-27B-gguf (PrismML), derived from
Qwen/Qwen3.8-27B (Alibaba).
About
Published by dealignai — public catalog of
uncensored model builds for research on refusal mechanisms in modern LLMs.
Follow updates at @dealignai.
