Runtime-uncensored Ternary Bonsai 2 27B — without modifying or re-quantizing the original weights by OrcaRouter research team.

This repository applies refusal-direction ablation to prism-ml/Ternary-Bonsai-2-27B-mlx-2bit entirely at runtime.

The original Bonsai pack remains bit-identical.

• 27B parameters
• ~1.72 bits/weight
• 0 modified weights
• 0 re-quantization
• 0 additional weight quantization error
• Runtime-adjustable ablation strength
• 129 residual intervention sites
• Apple Silicon / MLX

You supply the original Ternary Bonsai 2 pack. This repository supplies the refusal direction and the runtime that applies it.

Keep the weights compressed. Change the behavior at inference.

Traditional abliteration modifies model weights by orthogonalizing matrices that write into the residual stream against a learned refusal direction r:

W ← W - r(rᵀW)

For a conventional BF16 or FP16 model, the resulting matrix can simply be saved as a new checkpoint.

Ternary Bonsai 2 is different.

Its weights are aggressively quantized. The affine container stores:

scale = s
bias  = -s

so the 2-bit codes:

{0, 1, 2}

decode to:

{-s, 0, +s}

The model's quality at approximately 1.72 bits per weight is the result of quantization-aware training.

Orthogonalizing one of these matrices produces a dense, full-precision matrix. Saving that result back into the ternary representation would therefore require re-quantization.

And simply re-quantizing the edited weights does not reproduce the quantization-aware training process that produced the original model.

So we don't edit the weights.

Instead, the equivalent projection is applied when each residual contribution is produced:

y ← y - α · dot(y, r) · r

where:

y = residual contribution
r = normalized refusal direction
α = intervention strength

At alpha=1, the component of each residual write parallel to the refusal direction is removed.

Conceptually:

      Original Ternary Bonsai 2
              27B
               │
               │
        bit-identical weights
               │
               ▼
      ┌──────────────────┐
      │  packed matmul   │
      └────────┬─────────┘
               │
               │ y
               ▼
      ┌──────────────────┐
      │ Runtime Ablation │
      │                  │
      │ y ← y - α(y·r)r │
      └────────┬─────────┘
               │
               ▼
         residual stream
               │
               ▼
            output

The projection uses an MLX-compiled correction with float32 accumulation, then restores the writer's output dtype. Compilation may change floating-point rounding; it does not change the projection formula or ablation strength.

The original ternary weights are never modified.

Install the dependencies:

pip install -r requirements.txt

Run the model:

python run.py \
  --pack /path/to/Ternary-Bonsai-2-27B-mlx-2bit \
  "your prompt"

--pack is the directory holding config.json and runtime/. If you downloaded with huggingface_hub, that is the snapshot directory, not the models--... cache entry above it:

python -c "from huggingface_hub import snapshot_download; \
print(snapshot_download('prism-ml/Ternary-Bonsai-2-27B-mlx-2bit'))"

A cache entry is accepted too and its newest snapshot is resolved for you.

By default:

alpha = 1

which enables the full runtime projection.

Full tables with capability retention, sample sizes and methodology are under Evaluation — including what the asterisk on SimpleSafetyTests means.

Run:

python scripts/selfcheck.py \
  --pack /path/to/Ternary-Bonsai-2-27B-mlx-2bit

selfcheck.py measures how much of the residual stream lies along the refusal direction before and after intervention.

A correctly instrumented setup should drive the remaining component to approximately:

~1e-6 of the residual norm

If the number barely changes, the residual writers were probably not wrapped correctly.

The self-check also verifies that all expected intervention sites are present.

Expected:

129 residual writers

Run python scripts/test_ablation.py for a model-free numerical check of the compiled correction, including FP16/FP32 outputs, changing directions, and alpha values 0, 1, and 2.

Because the intervention happens entirely at runtime, the same model pack can be run with or without ablation.

python run.py \
  --pack /path/to/Ternary-Bonsai-2-27B-mlx-2bit \
  --alpha 0 \
  "your prompt"

python run.py \
  --pack /path/to/Ternary-Bonsai-2-27B-mlx-2bit \
  --alpha 1 \
  "your prompt"

There is no second checkpoint.

alpha = 0
     │
     └── Original model behavior

alpha = 1
     │
     └── Full refusal-direction projection

This also makes A/B testing straightforward because both configurations use the same underlying weights.

Refusal is judged by a rule-based opening-phrase classifier (caveat = answered but wrapped in a disclaimer) — indicative, not an LLM-judge / publication-grade number. Thinking off, greedy decoding, 64-token budget on the refusal sets. Base and ablated rows are the same weights in the same process, alpha 0 against alpha 1.

Run on the unfolded fp16 expansion of the pack rather than the pack driving its own kernels: the packed quantized matmul has no CUDA implementation, and the CPU backend needs minutes per forward pass. The expansion carries the pack's ternary values exactly — a container change, not a requantization — and reproduces the pack's own next-token distributions to three decimals on spot checks.

No reply in any set ran out of its token budget, so none of these rates is inflated by truncation.

The projection does not only stop refusals on harmful prompts. The published pack turns down a quarter of JailbreakBench's benign prompts; ablated, it turns down none.

Every movement is within noise at these sample sizes — one question on GSM8K is 0.7 points. That is the result the runtime approach is for: the weights are bit-identical, so there is no requantization to pay for.

¹ Understated. This set is mostly self-harm prompts, and the model answers them with a crisis redirect that opens "I am deeply sorry to hear…" — which the classifier's exact phrase list misses, scoring it as compliance. The real residual refusal rate on this set is higher than 18%. The classifier is left as-is so these numbers stay comparable with our other cards.

² Excluded. Its prompt asks for reasoning before the answer, and 63–64% of replies on both sides had not reached one inside the token budget, so the accuracy would be a floor set by that budget rather than a measurement.

Because modifying the weights defeats one of the most interesting properties of Bonsai.

A conventional abliteration performs:

W' = W - r(rᵀW)

But W' is no longer ternary.

It contains arbitrary floating-point values.

To store it in the original pack, we would need something approximately equivalent to:

ternary(
    W - r(rᵀW)
)

That introduces a new quantization step.

The original Bonsai model, however, achieved its compression through quantization-aware training, not through naïve post-training conversion of arbitrary dense matrices.

Instead we preserve:

W

exactly and transform its output:

y = Wx

y' = y - α(y·r)r

The stored model therefore remains untouched.

One important implementation detail is that wrapping only o_proj is not enough.

Ternary Bonsai 2 uses a hybrid architecture containing:

48 linear-attention layers
16 full-attention layers
64 MLP blocks

Each of these can write into the residual stream.

The runtime therefore wraps:

In other words:

64 + 48 + 16 + 1 = 129

Wrapping only:

self_attn.o_proj

would intercept only 16 of these sites.

run.py prints the number of wrapped residual writers.

selfcheck.py warns when it does not detect the expected:

129

Another important detail is the basis in which the projection is applied.

The Bonsai pack keeps projections in a rotated basis on their input dimension.

Its runtime compensates for that rotation on the activation side.

The refusal projection in this repository operates on the outputs of those projections.

Those outputs are already in the normal hidden basis.

Likewise, the embedding output is un-rotated by the pack's own Packed implementation.

Therefore the refusal direction is simply an ordinary:

5120-dimensional vector

No additional Hadamard rotation should be applied to the direction.

Doing so would project against the wrong basis.

The pack must be loaded using its own bundled runtime.

An ordinary MLX loader may appear to load the model successfully while silently producing incorrect computation because it does not apply the activation transformations required by the stored weights.

This repository handles that through:

bonsai_abliterate.pack

including the detail that:

vision_artifact.load_vl_model

is the loader that accepts the:

schema_version: 2

configuration used by these packs.

If outputs look completely wrong before ablation is even enabled, verify the pack-loading path first.

The intervention does not have to be binary.

alpha controls how strongly the refusal direction is removed.

For example:

python run.py \
  --pack /path/to/Ternary-Bonsai-2-27B-mlx-2bit \
  --alpha 0.7 \
  "your prompt"

This makes the intervention a runtime control parameter rather than a permanent property of a checkpoint.

The projection can also be restricted to specific layers.

For example:

python run.py \
  --pack /path/to/Ternary-Bonsai-2-27B-mlx-2bit \
  --layers 20,21,22,23 \
  "your prompt"

Layer selection provides another dimension for exploring the trade-off between behavioral intervention and capability retention.

Instead of producing many different checkpoints, experiments can vary:

direction
×
alpha
×
layers

against the same immutable model pack.

The directions/ directory contains:

The Safetensors file stores the vector under:

direction

The raw binary representation contains:

5120 × little-endian Float32

with no header.

The current refusal direction was estimated from the BF16 base model from which this Bonsai pack was trained.

The architecture and hidden basis are identical.

However, the degree to which the learned direction transfers through the model's quantization-aware training has not yet been fully measured.

This distinction matters.

The runtime can verify mathematically that it is removing the supplied direction from the residual stream.

That does not, by itself, prove that the transferred direction captures exactly the same behavioral feature in the QAT model.

Behavioral evaluation should therefore sweep:

alpha

and, where useful:

layers

before drawing conclusions about transfer quality.

A Swift reference implementation is included at:

swift/RefusalAblation.swift

Load:

directions/refusal_dir_fp32.bin

as:

5120 little-endian Float32

and wrap each residual writer with:

AblatedLinear

The pack's PACK-RUNTIME.md currently states that Swift support is layer-level only and that full-model loading still requires model integration.

Once the model integration exists, the refusal intervention itself is small.

Per residual write it adds approximately:

1 × dot product
1 × AXPY

No alternative model weights need to be stored.

On a phone the binding constraint is resident memory, and it is almost entirely the weights. Measured on the pack, not estimated:

ternary codes              6.256 GiB   78.2%
vision tower               0.858 GiB   10.7%
group biases (redundant)   0.391 GiB    4.9%
group scales               0.391 GiB    4.9%
norms + GDN state path     0.098 GiB    1.2%
hadamard signs             0.011 GiB    0.1%
total                      8.005 GiB

The KV cache is not the problem here, which is what makes a 27B model on a phone worth discussing at all. Only 16 of the 64 layers use full attention:

16 full-attention layers    64 KiB per token  (256 MiB at 4K context)
48 linear-attention layers  72 MiB of recurrent state, fixed, context-independent

Two things can come out of the pack for a text-only app:

python scripts/prepare_ios_pack.py \
    --pack /path/to/Ternary-Bonsai-2-27B-mlx-2bit \
    --out  /path/to/Bonsai-2-27B-ios

source pack                 8.005 GiB
  - vision tower           -0.858        memory and disk
  - redundant biases       -0.391        disk; memory only with a kernel that reconstructs
result                      6.756 GiB

The distinction between those two lines matters. The vision tower is a genuine memory saving: it is only read when an image is in the prompt, and the pack documents it as the stock unquantized Qwen tower.

The biases are a download saving. The affine container stores a scale and a bias per group of 128, but the ternary levels {-s, 0, +s} come out of scale = s, bias = -s, so the bias holds no information — verified exact across all 402 packed modules, every group, max |bias + scale| = 0. MLX's quantized_matmul and dequantize still take a bias argument, so a runtime that calls them has to materialise -scales at load and saves no memory at all. The memory saving needs a kernel that assumes the identity. Check before counting on it.

Nothing in that script touches the ternary codes, the group scales, the Hadamard signs or the direction. Kept tensors are bit-identical to the source.

Whether 6.756 GiB fits is a per-device question this repo cannot answer for you: iOS caps a single app well below total RAM, and the cap depends on the device and on whether com.apple.developer.kernel.increased-memory-limit has been granted. Do the arithmetic against your own target before committing to it. An 8 GB device does not have room for 6.756 GiB of weights plus an app.

Decode is memory-bandwidth-bound: every token reads the whole weight set. The ~47 tok/s figure quoted for an M5 Max laptop corresponds to several hundred GB/s of usable bandwidth; a phone has a fraction of that, so expect a fraction of the throughput, and expect sustained generation to meet thermal limits.

The output pack is meant for an app with its own model integration. It is deliberately not loadable by the pack's bundled Python loaders — vision_artifact.load_vl_model requires components.vision, and a removed bias changes what a Packed module reads. Keep the original pack for anything that uses those; ios-pack.json records what was removed and how to rebuild it.

The same edit ships as a rank-1 LoRA adapter, so the published ternary GGUF stays byte-identical. The adapter is in this repo — nothing to build:

gguf/bonsai-abliterate-lora.gguf     9,682,464 bytes
sha256  f1669534803d340a496015f5c45125f3437b4d13ec764f40e34488ce83967f42

llama-cli -m Ternary-Bonsai-2-27B-PTQ1_0.gguf --lora gguf/bonsai-abliterate-lora.gguf

It holds 129 lora_a/lora_b pairs — one per residual writer — as F32, against general.architecture = qwen35 and adapter.lora_alpha = 1.0.

scripts/export_gguf_lora.py rebuilds it, for anyone who wants to check the provenance or re-derive it against a different direction. That path needs the unfolded fp16 checkpoint, which this repo does not ship, so it is for reproduction rather than for ordinary use.

W' = W - r (r^T W) is rank 1, so the whole edit is A = r^T W, B = -r. llama.cpp builds a LoRA into the graph as two extra matmuls on the activation and never merges it into the base weights — which is what makes this work at 1.75 bits, where a baked edit would simply be rounded away.

--lora applies it at scale 1.0, and that is the projection exactly, not an approximation: llama.cpp computes scale = adapter_scale * alpha / rank with rank = lora_b->ne[0], and the exporter writes alpha = 1 against rank 1.

Exact does not mean every prompt flips. Measured on the PTQ1_0 pack, greedy, thinking off:

scale 0       the published model
scale 1       exact projection; most harmful prompts comply, some still refuse
scale 2       flips the stubborn ones
scale 3+      over-projection; output degrades, then collapses

llama-cli -m Ternary-Bonsai-2-27B-PTQ1_0.gguf \
    --lora-scaled bonsai-abliterate-lora.gguf:2

Read a single prompt as a sample of one. At full strength our own evaluation still had 6% of AdvBench refusing, so one stubborn prompt tells you nothing about the strength.

Stock llama.cpp cannot open these packs at all. PTQ1_0 and PQ2_0 are private ggml type ids — 143 and 142, outside upstream's range — so upstream refuses them at header parse. Even gguf-py raises:

ValueError: np.uint32(143) is not a valid GGMLQuantizationType

which is why bonsai_abliterate/gguf_min.py reads the base header directly. Writing the adapter still uses gguf-py, since an adapter holds only standard F32 tensors.

The published Ternary-Bonsai-2-27B-F16.gguf is not an escape hatch. It uses standard tensor types, so stock llama.cpp and Ollama will load it — but it is stored in the same Hadamard-rotated basis and carries the same prism.hadamard.* metadata, which upstream ignores entirely. It would run and emit nonsense rather than fail.

Ollama has no path today. It builds stock llama.cpp, so it inherits the rejection of the private types; ADAPTER parses but ollama create rejects every request carrying adapters with "LoRA adapters are no longer supported"; and it has no control-vector support.

The exported B @ A reproduces -r (r^T W) exactly — relative error 0.000e+00 on ffn_down, attn_output, ssm_out and token_embd — and the direction's leakage into each falls about six orders of magnitude. Against the fork, the adapter loads on the ternary pack, scale 0 reproduces the published refusal and scale 100 destroys the model, so it is genuinely in the compute graph. All 129 sites are on LoRA-aware paths: ffn_down through build_ffn's build_lora_mm, attn_output and ssm_out inline in qwen35.cpp, token_embd in build_inp_embd.

One thing is not: PQ2_0 was not run. The adapter does not depend on the base's quantization — A = r^T W comes from the unfolded checkpoint and the base GGUF is read only for tensor names — and all three published GGUFs carry the same 851 names, so the same file should apply. Only PTQ1_0 was actually tested.

A LoRA on a tensor llama.cpp does not route through build_lora_mm loads without error and does nothing at all. Check that scale 0 reproduces the published model and that a large scale visibly breaks it — if neither is true, the adapter is not being applied.

The pack's quantized matrix multiplication currently has kernels for:

Metal
CPU

but not CUDA through mlx-cuda.

Attempting to use the required operation on CUDA currently results in:

QuantizedMatmul has no CUDA implementation

As a result, putting this particular MLX pack on an NVIDIA machine does not currently provide the expected GPU acceleration.

VRAM is not the primary constraint.

CPU inference works, but a forward pass for a 27B model can take minutes.

That makes the Linux CPU path useful for:

• implementation testing
• behavior verification
• reproducibility checks

rather than high-throughput serving.

Build:

docker build \
  -t orcarouter-ternary-bonsai-2-27b-uncensored \
  -f docker/Dockerfile .

Run:

PACK=/path/to/Ternary-Bonsai-2-27B-mlx-2bit \
docker/run.sh \
  python run.py \
    --pack /pack \
    --max-new 32 \
    "your prompt"

The key distinction of this release is that uncensoring is a runtime property rather than a checkpoint property.

┌─────────────────────────────────────────────┐
│       Ternary Bonsai 2 · 27B               │
│                                             │
│       ~1.72 bits / weight                   │
│       original packed weights              │
└──────────────────┬──────────────────────────┘
                   │
                   │ weights remain
                   │ bit-identical
                   ▼
┌─────────────────────────────────────────────┐
│             Runtime Inference               │
│                                             │
│  residual writer                            │
│        │                                    │
│        ▼                                    │
│  y = writer(x)                              │
│        │                                    │
│        ▼                                    │
│  y' = y - α · dot(y,r) · r                  │
│        │                                    │
│        ▼                                    │
│  residual stream                            │
└──────────────────┬──────────────────────────┘
                   │
                   ▼
                 output

Model parameters           27B
Effective weight size      ~1.72 bits/weight
Hidden dimension           5120

Residual intervention
sites                       129

Weight modification        None
Weight re-quantization     None
Additional weight
quantization error         None

Intervention strength      Runtime adjustable
Layer selection            Runtime adjustable
Original behavior          alpha=0
Full projection            alpha=1

Permanent weight editing couples a behavioral modification to a particular checkpoint.

Runtime intervention separates the two.

                 MODEL
                   │
            immutable weights
                   │
                   ▼
              INFERENCE
                   │
          ┌────────┴────────┐
          │                 │
      alpha = 0         alpha = 1
          │                 │
          ▼                 ▼
      original          ablated
      behavior          behavior

The same architecture can potentially support more than one learned direction without generating another copy of the underlying model.

Conceptually:

Immutable Model
      │
      ▼
Runtime Intervention
      │
      ├── direction
      ├── strength
      └── layer scope
      │
      ▼
Inference

This repository currently implements refusal-direction ablation.

This is not a newly trained 27B model.

It is a runtime behavioral intervention for the existing Ternary Bonsai 2 27B MLX pack.

The underlying model architecture, QAT training, ternary representation and original packed weights come from the upstream Bonsai release.

The contribution here is the runtime refusal-direction implementation, direction artifacts, residual-writer instrumentation and verification tooling.

That distinction is intentional:

The model stays immutable. Behavioral intervention happens at inference.

Removing a learned refusal direction can cause the model to respond to requests that the original model would decline.

The technique should therefore be treated as a research and inference-control mechanism, not as evidence that every resulting output is safe, correct or appropriate.

Deployments should apply their own access controls, policy enforcement and security boundaries appropriate to their use case.

Built on:

Ternary Bonsai 2 27B by prism-ml

Runtime refusal-direction implementation and tooling by OrcaRouter.

The original model pack is not redistributed or modified by this repository.

Apache-2.0, matching the underlying model.