Instructions to use KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit / compact /README.md

KridgeDookie

Add validated original-size base plus compact rank-8 correction

6f27bbf verified 5 days ago

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2.85 kB

Compact Bonsai 2 27B Philadelphia Class

8.630 GB of weights plus correction: original packed weights (8,595,477,990 bytes) + required rank-8 adapter (34,093,704 bytes). Tokenizer/config/runtime files are additional. Keep the two files separate; do not merge and requantize the adapter. The base file alone is unchanged original Bonsai and does not contain the abliteration.

The adapter approximates the previously validated mixed-Q8 derivative using randomized SVD of its weight differences from the original ternary model. It is a compressed approximation, not a bit-identical reconstruction or a newly trained model. All 126 edited output matrices retain rank-8 corrections in FP16. This avoids the failed method of rounding edited weights back to ternary.

Validation

The actual original GGUF plus this adapter produced 0/126 refusals, 100.0% usable openings, and 21/24 benign capability passes. Original Bonsai scored 123/126 refusals and 22/24 capability passes; the larger mixed-Q4/Q8 releases scored 0/126 and 21/24. These are limited heuristic screens, not general intelligence or safety guarantees. MLX passed strict model+adapter loading, three numerical layer checks and one arithmetic generation prompt on Linux CPU. See evaluation.json for the exact scope.

Run

hf download KridgeDookie/Ternary-Bonsai-2-27B-ABLITERATED-UNCENSORED-PHILADELPHIA-CLASS-MLX-Mixed-2-4bit --include "compact/*" --local-dir bonsai-compact
pip install -r bonsai-compact/compact/requirements.txt
python bonsai-compact/compact/compact_runtime.py --model bonsai-compact/compact --prompt "Say hello briefly."

The supplied loader is required: from compact_runtime import load_compact; model, processor, config = load_compact(directory). It first loads the original schema-2 pack, then installs all 126 additive corrections. Loading just model.safetensors with the original loader omits the edits. This is the same compact 2-bit package in both mixed-precision MLX repositories; those repository names describe their older root-level models. The original vision weights are retained, but vision behavior was not tested. Linux CPU validation used the pinned MLX stack; macOS Metal has not been tested.

Provenance

Created using Bonsai by Prism ML. Original base: prism-ml/Ternary-Bonsai-2-27B-mlx-2bit; mixed-Q8 reference revision 61231c9a2feed6457f47d1d50a691b8dc96b2613. Apache-2.0 weights; source runtime retains its MIT license. The compact correction and loader were added 2026-09-21. artifact_manifest.json records hashes of the exact source and adapter files. compression.json records numerical approximation errors; compress_adapter.py records the build method.