Instructions to use webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO 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 webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO
# Run inference directly in the terminal:
llama cli -hf webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO
# Run inference directly in the terminal:
llama cli -hf webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

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 webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO
# Run inference directly in the terminal:
./llama-cli -hf webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

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 webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO
# Run inference directly in the terminal:
./build/bin/llama-cli -hf webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

Use Docker

docker model run hf.co/webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

LM Studio
Jan

vLLM

How to use webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

Ollama
How to use webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO with Ollama:
```
ollama run hf.co/webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO
```
Unsloth Desktop

How to use webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO with Docker Model Runner:
```
docker model run hf.co/webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO
```

Lemonade

How to use webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

Run and chat with the model

lemonade run user.Sakura-Micro-Bonsai-2-GSQ-RCO-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

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 webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

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 "webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO" \
  --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"

Sakura Micro Bonsai 2 GSQ-RCO

Sakura Micro Bonsai 2 GSQ-RCO is an experimental ultra-low-bit derivative of Prism ML's Ternary Bonsai 2 27B, itself derived from Qwen3.8-27B. This release applies GSQ/RCO-based mixed-codec allocation and additional low-bit compression.

Provenance / Base model

Sakura Micro Bonsai 2 GSQ-RCO is derived from Prism ML's Ternary Bonsai 2 27B, which identifies Qwen3.8-27B as its base model.

Release artifact

Sakura-Micro-Bonsai-2-GSQ-RCO-5.3GiB.gguf

Size: 5,686,593,888 Bytes (5.296 GiB)
SHA-256: 5054d9b3b3b95145ebf1def33803fd93a1962dda37ca50bd53dbcbbbe4655eb3
The bundled SHA256SUMS file can be used to verify the downloaded GGUF.

Benchmark comparison

Metric	Ternary Bonsai 2 PTQ1 reference	Sakura Micro Bonsai 2 GSQ-RCO
File size	5.538 GiB	5.296 GiB
WikiText-2 PPL	10.2726	10.7251
Arithmetic	34/40 (85.0%)	37/40 (92.5%)
GSM8K	38/40 (95.0%)	35/40 (87.5%)
HumanEval	16/20 (80.0%)	15/20 (75.0%)
IFEval	9/12 (75.0%)	12/12 (100%)

Sakura Micro Bonsai 2 GSQ-RCO is 4.37% smaller than the 5,946,648,928-Byte Ternary Bonsai 2 PTQ1 reference. These are measurements on small benchmark subsets, not a claim of general quality superiority over the base model.

Compression approach

The project combines several ideas rather than applying one uniform quantizer to every tensor. It uses GSQ/RCO-based mixed-codec allocation and additional low-bit compression to target a smaller release artifact while retaining measured functionality.

This card intentionally does not disclose per-tensor selections, allocation details, internal optimization logic, or reproduction procedures.

Notes

Experimental low-bit release.
Use the runtime documented by the base model.

License

Apache-2.0. The license text is included in LICENSE.

Downloads last month: 272

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for webmp3/Sakura-Micro-Bonsai-2-GSQ-RCO

Base model

Qwen/Qwen3.8-27B

Quantized

prism-ml/Ternary-Bonsai-2-27B-gguf

Quantized

(22)

this model