Instructions to use leeaandrob/Bonsai-27B-1bit-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use leeaandrob/Bonsai-27B-1bit-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 leeaandrob/Bonsai-27B-1bit-gguf:Q1_0
# Run inference directly in the terminal:
llama cli -hf leeaandrob/Bonsai-27B-1bit-gguf:Q1_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf leeaandrob/Bonsai-27B-1bit-gguf:Q1_0
# Run inference directly in the terminal:
llama cli -hf leeaandrob/Bonsai-27B-1bit-gguf:Q1_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 leeaandrob/Bonsai-27B-1bit-gguf:Q1_0
# Run inference directly in the terminal:
./llama-cli -hf leeaandrob/Bonsai-27B-1bit-gguf:Q1_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 leeaandrob/Bonsai-27B-1bit-gguf:Q1_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf leeaandrob/Bonsai-27B-1bit-gguf:Q1_0

Use Docker

docker model run hf.co/leeaandrob/Bonsai-27B-1bit-gguf:Q1_0

LM Studio
Jan

vLLM

How to use leeaandrob/Bonsai-27B-1bit-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leeaandrob/Bonsai-27B-1bit-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": "leeaandrob/Bonsai-27B-1bit-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/leeaandrob/Bonsai-27B-1bit-gguf:Q1_0

Ollama
How to use leeaandrob/Bonsai-27B-1bit-gguf with Ollama:
```
ollama run hf.co/leeaandrob/Bonsai-27B-1bit-gguf:Q1_0
```
Unsloth Desktop

How to use leeaandrob/Bonsai-27B-1bit-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf leeaandrob/Bonsai-27B-1bit-gguf:Q1_0

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": "leeaandrob/Bonsai-27B-1bit-gguf:Q1_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use leeaandrob/Bonsai-27B-1bit-gguf with Docker Model Runner:
```
docker model run hf.co/leeaandrob/Bonsai-27B-1bit-gguf:Q1_0
```

Lemonade

How to use leeaandrob/Bonsai-27B-1bit-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull leeaandrob/Bonsai-27B-1bit-gguf:Q1_0

Run and chat with the model

lemonade run user.Bonsai-27B-1bit-gguf-Q1_0

List all available models

lemonade list

Hermes Agent

How to use leeaandrob/Bonsai-27B-1bit-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 leeaandrob/Bonsai-27B-1bit-gguf:Q1_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 leeaandrob/Bonsai-27B-1bit-gguf:Q1_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use leeaandrob/Bonsai-27B-1bit-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf leeaandrob/Bonsai-27B-1bit-gguf:Q1_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 "leeaandrob/Bonsai-27B-1bit-gguf:Q1_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"

Bonsai-27B · 1-bit (Q1_0) GGUF — for the SuperSeed AI Agent

The model the SuperSeed AI Agent app downloads when you switch on Local model (NeuroGrid): PrismML's Bonsai-27B (Qwen3.5 architecture, 27B parameters) in 1-bit Q1_0 — every projection stored as one sign bit per weight with an fp16 scale per 128-weight block. 3.6 GB on disk, 3.6 GB pinned in RAM while it runs.

file	size	what
`Bonsai-27B-Q1_0.gguf`	3.54 GiB	language model, Q1_0
`Bonsai-27B-mmproj-Q8_0.gguf`	0.59 GiB	vision projector (optional)

These are byte-identical redistributions of the files in prism-ml/Bonsai-27B-gguf (Apache-2.0). Credit for the model and the quantization belongs to PrismML.

How it runs

The SuperSeed AI Agent embeds the NeuroGrid engine as a sidecar (neurogrid -solo) and runs this file on the CPU with NEON SDOT kernels written for the Q1_0 block layout (bit=1 → +d, bit=0 → −d), weights mlock'd so they never fall out of RAM, on the performance cores only. On an Apple M5 with 16 GB it loads in ~4 s and decodes at ~3–3.5 tok/s while the laptop stays usable. Everything you type stays on the machine.

Also runs with PrismML's llama.cpp fork (Q1_0 is not in upstream llama.cpp).

License

Apache-2.0, same as the original.

Downloads last month: 169

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

1-bit