Instructions to use OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF",
	filename="Ternary-Bonsai-27B-heretic-ja-Q2_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16

Use Docker

docker model run hf.co/OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16

LM Studio
Jan
Ollama
How to use OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF with Ollama:
```
ollama run hf.co/OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16
```

Unsloth Studio

How to use OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF to start chatting

How to use OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16

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 OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16

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 "OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Docker Model Runner
How to use OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF with Docker Model Runner:
```
docker model run hf.co/OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16
```

Lemonade

How to use OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OS-Software/Ternary-Bonsai-27B-heretic-ja-GGUF:BF16

Run and chat with the model

lemonade run user.Ternary-Bonsai-27B-heretic-ja-GGUF-BF16

List all available models

lemonade list

This is a decensored version of prism-ml/Ternary-Bonsai-27B-unpacked, made using Heretic v1.4.0 with the Arbitrary-Rank Ablation (ARA) method using a LoRA adapter and row-norm preservation

Abliteration parameters

Parameter	Value
start_layer_index	10
end_layer_index	51
preserve_good_behavior_weight	0.6491
steer_bad_behavior_weight	0.0008
overcorrect_relative_weight	0.9874
neighbor_count	14

Performance

Metric	This model	Original model (prism-ml/Ternary-Bonsai-27B-unpacked)
Keywords	2/100	98/100
KL divergence	0.0243	0 (by definition)

Note: Performance testing, including the measurement of refusal rates, was conducted using Japanese datasets (harmless_alpaca_ja, harmful_behaviors_ja).

Ternary Bonsai 27B — Unpacked FP16 Safetensors

FP16 safetensors (HuggingFace format) of the Ternary Bonsai 27B model. This repo exists for users who want to run Ternary Bonsai with stock HuggingFace tooling or frameworks that don't yet support the packed ternary format. The 2-bit hybrid-attention kernels are currently in our forks of MLX, mlx-swift, and llama.cpp — once they land upstream, this unpacked version will no longer be needed.

We strongly recommend using the natively packed models instead. The packed format is where all the benefits of Bonsai come from — a 7.2 GB deployed footprint (down from 54 GB), 95% of FP16 intelligence retained, and interactive decoding on everyday laptops (26 tok/s on an M5 Pro). This unpacked FP16 version is full-size and does not provide any of those advantages.

For the optimized ternary release models (recommended):

Ternary-Bonsai-27B-mlx-2bit — Ternary MLX for Apple Silicon
Ternary GGUF (Q2_0_g128) for llama.cpp (CUDA, Metal, CPU)

For the phone-class variant:

Bonsai-27B-mlx-1bit — 1-bit Bonsai 27B (~3.9 GB), fits an iPhone 17 Pro Max

Downloads last month: 1,300

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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