Instructions to use lmstudio-community/Bonsai-27B-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 lmstudio-community/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 lmstudio-community/Bonsai-27B-GGUF:Q1_0
# Run inference directly in the terminal:
llama cli -hf lmstudio-community/Bonsai-27B-GGUF:Q1_0

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/lmstudio-community/Bonsai-27B-GGUF:Q1_0

LM Studio
Jan
Ollama
How to use lmstudio-community/Bonsai-27B-GGUF with Ollama:
```
ollama run hf.co/lmstudio-community/Bonsai-27B-GGUF:Q1_0
```

Unsloth Studio

How to use lmstudio-community/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 lmstudio-community/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 lmstudio-community/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 lmstudio-community/Bonsai-27B-GGUF to start chatting

How to use lmstudio-community/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 lmstudio-community/Bonsai-27B-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": "lmstudio-community/Bonsai-27B-GGUF:Q1_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lmstudio-community/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 lmstudio-community/Bonsai-27B-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 lmstudio-community/Bonsai-27B-GGUF:Q1_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use lmstudio-community/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 lmstudio-community/Bonsai-27B-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 "lmstudio-community/Bonsai-27B-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"

Docker Model Runner
How to use lmstudio-community/Bonsai-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/lmstudio-community/Bonsai-27B-GGUF:Q1_0
```

Lemonade

How to use lmstudio-community/Bonsai-27B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lmstudio-community/Bonsai-27B-GGUF:Q1_0

Run and chat with the model

lemonade run user.Bonsai-27B-GGUF-Q1_0

List all available models

lemonade list

Bonsai 27B GGUF

Private LM Studio conversion of prism-ml/Bonsai-27B-unpacked.

Files

File	Purpose	Size
`Bonsai-27B-Q1_0.gguf`	Pure Q1_0 language model	3.80 GB
`mmproj-Bonsai-27B-BF16.gguf`	BF16 vision projector	0.93 GB

The language model was converted with llama.cpp commit 0e4a0362239713ea95a6864a17a8de4b0ad90d62 using --no-mtp, then quantized with llama-quantize --pure ... Q1_0. Excluding MTP is required because the source configuration advertises one MTP layer while the checkpoint contains language blocks 0 through 63 only.

This repository intentionally does not include Prism’s separately packaged DSpark draft model. It therefore indexes as one 27B target plus one vision adapter in LM Studio.

Validation

Validated with llama-server and LM Studio/llmster:

qwen35.block_count = 64; no qwen35.nextn_predict_layers
Text and vision inference
Parallel tool calls
A three-stage tool workflow with nested JSON arguments and tool-result synthesis

Checksums

71d6871836886267f39f1ac69b1b1dd8a783970708d7a189960112ed02978901  Bonsai-27B-Q1_0.gguf
c4c09d300638f6d2759e1ffe94d97cf08be94b8c9dad6920509be1aab11fd8cb  mmproj-Bonsai-27B-BF16.gguf

Downloads last month: 126,243

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

1-bit

Inference Providers NEW

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Model tree for lmstudio-community/Bonsai-27B-GGUF

Base model

prism-ml/Bonsai-27B-unpacked

Quantized

(4)

this model