Instructions to use leok7v/Ternary-Bonsai-27B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leok7v/Ternary-Bonsai-27B-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="leok7v/Ternary-Bonsai-27B-gguf",
	filename="Ternary-Bonsai-27B-Q2_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use leok7v/Ternary-Bonsai-27B-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 leok7v/Ternary-Bonsai-27B-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf leok7v/Ternary-Bonsai-27B-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf leok7v/Ternary-Bonsai-27B-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf leok7v/Ternary-Bonsai-27B-gguf:Q8_0

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 leok7v/Ternary-Bonsai-27B-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf leok7v/Ternary-Bonsai-27B-gguf:Q8_0

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 leok7v/Ternary-Bonsai-27B-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf leok7v/Ternary-Bonsai-27B-gguf:Q8_0

Use Docker

docker model run hf.co/leok7v/Ternary-Bonsai-27B-gguf:Q8_0

LM Studio
Jan

vLLM

How to use leok7v/Ternary-Bonsai-27B-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leok7v/Ternary-Bonsai-27B-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leok7v/Ternary-Bonsai-27B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/leok7v/Ternary-Bonsai-27B-gguf:Q8_0

Ollama
How to use leok7v/Ternary-Bonsai-27B-gguf with Ollama:
```
ollama run hf.co/leok7v/Ternary-Bonsai-27B-gguf:Q8_0
```

Unsloth Studio

How to use leok7v/Ternary-Bonsai-27B-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 leok7v/Ternary-Bonsai-27B-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 leok7v/Ternary-Bonsai-27B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for leok7v/Ternary-Bonsai-27B-gguf to start chatting

How to use leok7v/Ternary-Bonsai-27B-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf leok7v/Ternary-Bonsai-27B-gguf:Q8_0

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": "leok7v/Ternary-Bonsai-27B-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use leok7v/Ternary-Bonsai-27B-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 leok7v/Ternary-Bonsai-27B-gguf:Q8_0

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 leok7v/Ternary-Bonsai-27B-gguf:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use leok7v/Ternary-Bonsai-27B-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf leok7v/Ternary-Bonsai-27B-gguf:Q8_0

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 "leok7v/Ternary-Bonsai-27B-gguf:Q8_0" \
  --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 leok7v/Ternary-Bonsai-27B-gguf with Docker Model Runner:
```
docker model run hf.co/leok7v/Ternary-Bonsai-27B-gguf:Q8_0
```

Lemonade

How to use leok7v/Ternary-Bonsai-27B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull leok7v/Ternary-Bonsai-27B-gguf:Q8_0

Run and chat with the model

lemonade run user.Ternary-Bonsai-27B-gguf-Q8_0

List all available models

lemonade list

Model Card for Ternary Bonsai 27B (GGUF, mirror)

An unmodified mirror of one quant of Prism ML's Ternary Bonsai 27B GGUF. This is a byte-for-byte copy re-hosted under the Apache 2.0 license as a stable pinned snapshot that survives if the upstream repo is moved or removed. It is NOT the authoritative source and adds nothing to the model.

This repository holds a quantized weight file and its matching vision projector only, not training data or original weights, and nothing here was re-quantized, converted, or otherwise changed. For the full model card, benchmarks, other quants, and the whitepaper, use the original repository.

Model Details

Model Description

Ternary Bonsai 27B is a Qwen3.5 hybrid model (general.architecture = qwen35): of its 64 layers, 48 are Gated DeltaNet linear-attention (state-space) blocks and 16 are full softmax-attention blocks, interleaved "three linear, one attention". The weights are quantized to a true ternary alphabet {-1, 0, +1} at ~1.71 bits per weight. This mirror carries the Q2_0_g128 build: each 128-weight block is { FP16 scale d; 2-bit codes qs[32] } and dequantizes as w = (code - 1) * d.

Developed by: Prism ML (model + ternary quantization), built from Qwen3.6-27B by Alibaba Cloud; this repository is an unmodified mirror by leok7v
Model type: Hybrid Gated DeltaNet + attention causal language model, ternary-quantized GGUF (llama.cpp)
Language(s): English and the languages of the base model
License: Apache 2.0 (inherited unchanged from the upstream model)
Mirrored from model: prism-ml/Ternary-Bonsai-27B-gguf

Model Sources

Repository (this mirror): https://huggingface.co/leok7v/Ternary-Bonsai-27B-gguf
Original (authoritative) repository: prism-ml/Ternary-Bonsai-27B-gguf (Apache 2.0)
Base model: Qwen/Qwen3.6-27B (Apache 2.0)

Attribution

Per the upstream NOTICE:

Created using Bonsai by Prism ML.

File	Notes
`Ternary-Bonsai-27B-Q2_0.gguf` (~7.2 GB)	Byte-for-byte copy of the upstream `Q2_0_g128` ternary build.
`Ternary-Bonsai-27B-mmproj-Q8_0.gguf` (~0.63 GB)	Byte-for-byte copy of the upstream Q8_0 vision projector (mmproj) for the multimodal path.

Why a mirror and not a fork

Hugging Face does not support forking a model repository, so mirroring the weight file is the only way to keep a pinned snapshot; the storage and bandwidth are the mirror's cost, not upstream's. If you just want the model, prefer the original.

Downloads last month: 63

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for leok7v/Ternary-Bonsai-27B-gguf

Base model

Qwen/Qwen3.6-27B

Quantized

prism-ml/Ternary-Bonsai-27B-gguf

Quantized

(3)

this model

leok7v
/

Ternary-Bonsai-27B-gguf

Model Card for Ternary Bonsai 27B (GGUF, mirror)

Model Details

Model Description

Model Sources

Attribution

Contents

Why a mirror and not a fork

Model tree for leok7v/Ternary-Bonsai-27B-gguf