Instructions to use prism-ml/Bonsai-1.7B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prism-ml/Bonsai-1.7B-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prism-ml/Bonsai-1.7B-gguf",
	filename="Bonsai-1.7B-Q1_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prism-ml/Bonsai-1.7B-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prism-ml/Bonsai-1.7B-gguf:Q1_0
# Run inference directly in the terminal:
llama-cli -hf prism-ml/Bonsai-1.7B-gguf:Q1_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prism-ml/Bonsai-1.7B-gguf:Q1_0
# Run inference directly in the terminal:
llama-cli -hf prism-ml/Bonsai-1.7B-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 prism-ml/Bonsai-1.7B-gguf:Q1_0
# Run inference directly in the terminal:
./llama-cli -hf prism-ml/Bonsai-1.7B-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 prism-ml/Bonsai-1.7B-gguf:Q1_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prism-ml/Bonsai-1.7B-gguf:Q1_0

Use Docker

docker model run hf.co/prism-ml/Bonsai-1.7B-gguf:Q1_0

LM Studio
Jan

vLLM

How to use prism-ml/Bonsai-1.7B-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prism-ml/Bonsai-1.7B-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": "prism-ml/Bonsai-1.7B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prism-ml/Bonsai-1.7B-gguf:Q1_0

Ollama
How to use prism-ml/Bonsai-1.7B-gguf with Ollama:
```
ollama run hf.co/prism-ml/Bonsai-1.7B-gguf:Q1_0
```

Unsloth Studio new

How to use prism-ml/Bonsai-1.7B-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 prism-ml/Bonsai-1.7B-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 prism-ml/Bonsai-1.7B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prism-ml/Bonsai-1.7B-gguf to start chatting

Pi new

How to use prism-ml/Bonsai-1.7B-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prism-ml/Bonsai-1.7B-gguf:Q1_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": "prism-ml/Bonsai-1.7B-gguf:Q1_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prism-ml/Bonsai-1.7B-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prism-ml/Bonsai-1.7B-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 prism-ml/Bonsai-1.7B-gguf:Q1_0

Run Hermes

hermes

Docker Model Runner
How to use prism-ml/Bonsai-1.7B-gguf with Docker Model Runner:
```
docker model run hf.co/prism-ml/Bonsai-1.7B-gguf:Q1_0
```

Lemonade

How to use prism-ml/Bonsai-1.7B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prism-ml/Bonsai-1.7B-gguf:Q1_0

Run and chat with the model

lemonade run user.Bonsai-1.7B-gguf-Q1_0

List all available models

lemonade list

Prism ML Website | Whitepaper | Demo & Examples | Colab Notebook | Discord

Bonsai-1.7B-GGUF-1bit

End-to-end 1-bit language model for llama.cpp (CUDA, Metal, CPU)

13.9x smaller than FP16 | 3.0x faster on RTX 4090 | runs on any device

Highlights

Deployed footprint — runs on virtually any device
End-to-end 1-bit weights across embeddings, attention projections, MLP projections, and LM head
GGUF Q1_0 (g128) format for 1-bit packing of weights with shared scales for each group (group size 128).
Cross-platform: CUDA (RTX/datacenter), Metal (Mac), Swift (iPhone/iPad), Android
MLX companion: also available as MLX 1-bit g128 for native Apple Silicon inference

Frontier Efficiency

Resources

Google Colab — try Bonsai in your browser, no setup required
Whitepaper — for more details on Bonsai, check out our whitepaper
Demo repo — comprehensive examples for serving, benchmarking, and integrating Bonsai
Discord — join the community for support, discussion, and updates
1-bit kernels: llama.cpp fork (CUDA + Metal) · MLX fork (Apple Silicon) · mlx-swift fork (iOS/macOS)
Locally AI — we have partnered with Locally AI for iPhone support

Model Overview

Item	Specification
Parameters	1.7B (~1.4B non-embedding)
Architecture	Qwen3-1.7B dense: GQA (16 query / 8 KV heads), SwiGLU MLP, RoPE, RMSNorm
Layers	28 Transformer decoder blocks
Context length	32,768 tokens
Vocab size	151,936
Weight format	GGUF Q1_0
Deployed size	0.24 GB (14.2x smaller than FP16)
1-bit coverage	Embeddings, attention projections, MLP projections, LM head
License	Apache 2.0

Quantization Format: Q1_0

Each weight is a single bit: 0 maps to −scale, 1 maps to +scale. Every group of 128 weights shares one FP16 scale factor.

Effective bits per weight: 1.125 (1 sign bit + 16-bit scale amortized over 128 weights).

Memory Requirement

Parameter memory only (weights and scales loaded into memory):

Format	Size	Reduction	Ratio
FP16	3.44 GB	—	1.0x
GGUF Q1_0	0.24 GB	93.0%	14.2x
MLX 1-bit g128	0.27 GB	92.2%	12.8x

The GGUF file on disk is 0.25 GB (~6.2 MB larger) because the format embeds the tokenizer, chat template, and model metadata alongside the weights.

Best Practices

Generation Parameters

Parameter	Default	Suggested range
Temperature	0.5	0.5 -- 0.7
Top-k	20	20 -- 40
Top-p	0.9	0.85 -- 0.95
Repetition penalty	1.0
Presence penalty	0.0

System Prompt

You can use a simple system prompt such as:

You are a helpful assistant

Quickstart

llama.cpp (CUDA)

# Clone the PrismML fork of llama.cpp (includes Q1_0 kernels)
git clone https://github.com/PrismML-Eng/llama.cpp
cd llama.cpp

# Build with CUDA support
cmake -B build -DGGML_CUDA=ON && cmake --build build -j

# Run inference
./build/bin/llama-cli \
    -m Bonsai-1.7B-Q1_0.gguf \
    -p "Explain quantum computing in simple terms." \
    -n 256 \
    --temp 0.5 \
    --top-p 0.85 \
    --top-k 20 \
    -ngl 99

llama.cpp (Metal / macOS)

# Clone the PrismML fork of llama.cpp (includes Q1_0 kernels)
git clone https://github.com/PrismML-Eng/llama.cpp
cd llama.cpp

# Build with Metal support (default on macOS)
cmake -B build && cmake --build build -j

# Run inference
./build/bin/llama-cli \
    -m Bonsai-1.7B-Q1_0.gguf \
    -p "Explain quantum computing in simple terms." \
    -n 256 \
    --temp 0.5 \
    --top-p 0.85 \
    --top-k 20 \
    -ngl 99

llama.cpp Server

./build/bin/llama-server \
    -m Bonsai-1.7B-Q1_0.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -ngl 99

Open the web UI at http://127.0.0.1:8080, or see our llama.cpp fork for more examples.

Cross-Platform Throughput

Platform	Backend	TG128 (tok/s)	FP16 TG (tok/s)	TG vs FP16	PP512 (tok/s)	FP16 PP512 (tok/s)
RTX 4090	llama.cpp CUDA	674	224	3.0x	31,899	30,630
M4 Pro 48 GB	llama.cpp Metal	250	65	3.8x	2,305	2,291

Citation

If you use 1-bit Bonsai 1.7B, please cite:

@techreport{bonsai,
    title   = {Bonsai: End-to-End 1-bit Language Model Deployment
               Across Apple, GPU, and Mobile Runtimes},
    author  = {Prism ML},
    year    = {2026},
    month   = {March},
    url     = {https://prismml.com}
}