Instructions to use prism-ml/Ternary-Bonsai-2-27B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prism-ml/Ternary-Bonsai-2-27B-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 prism-ml/Ternary-Bonsai-2-27B-gguf:F16 # Run inference directly in the terminal: llama cli -hf prism-ml/Ternary-Bonsai-2-27B-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf prism-ml/Ternary-Bonsai-2-27B-gguf:F16 # Run inference directly in the terminal: llama cli -hf prism-ml/Ternary-Bonsai-2-27B-gguf:F16
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/Ternary-Bonsai-2-27B-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf prism-ml/Ternary-Bonsai-2-27B-gguf:F16
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/Ternary-Bonsai-2-27B-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf prism-ml/Ternary-Bonsai-2-27B-gguf:F16
Use Docker
docker model run hf.co/prism-ml/Ternary-Bonsai-2-27B-gguf:F16
- LM Studio
- Jan
- vLLM
How to use prism-ml/Ternary-Bonsai-2-27B-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prism-ml/Ternary-Bonsai-2-27B-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": "prism-ml/Ternary-Bonsai-2-27B-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/prism-ml/Ternary-Bonsai-2-27B-gguf:F16
- Ollama
How to use prism-ml/Ternary-Bonsai-2-27B-gguf with Ollama:
ollama run hf.co/prism-ml/Ternary-Bonsai-2-27B-gguf:F16
- Unsloth Desktop
- Pi
How to use prism-ml/Ternary-Bonsai-2-27B-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prism-ml/Ternary-Bonsai-2-27B-gguf:F16
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": "prism-ml/Ternary-Bonsai-2-27B-gguf:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prism-ml/Ternary-Bonsai-2-27B-gguf with Docker Model Runner:
docker model run hf.co/prism-ml/Ternary-Bonsai-2-27B-gguf:F16
- Lemonade
How to use prism-ml/Ternary-Bonsai-2-27B-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prism-ml/Ternary-Bonsai-2-27B-gguf:F16
Run and chat with the model
lemonade run user.Ternary-Bonsai-2-27B-gguf-F16
List all available models
lemonade list
- Hermes Agent
How to use prism-ml/Ternary-Bonsai-2-27B-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 prism-ml/Ternary-Bonsai-2-27B-gguf:F16
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/Ternary-Bonsai-2-27B-gguf:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prism-ml/Ternary-Bonsai-2-27B-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prism-ml/Ternary-Bonsai-2-27B-gguf:F16
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 "prism-ml/Ternary-Bonsai-2-27B-gguf:F16" \ --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"
Really useful quantified small models PQ2_0. Have good intelligence and pretty fast. (With evalscope testing result)
I tested the PQ2_0 with PrismML-Eng/llama.cpp "prism-b10709-9a9394a" version. Test is ran on my 5090 laptop with speed ~7x t/s.
It only took ~11.9G VRAM with kv-cache q8_0 128k context. It passes my intellengence test scripts and the evalscope tool_bench score is pretty good (0.4200).
Ternary-Bonsai-2-27B-PQ2_0 with reasoning-effort "medium"
Ref. - Unsloth Qwen3.8-27b-UD_Q4_K_XL with reasoning-effort "xhigh", evalscope tool_bench score (0.4706)
Reminder and Conclusion:
I tested Ternary-Bonsai-2-27B-PQ2_0 with both reasoning-effort xhigh and medium. When testing with "xhigh", PQ2_0 caused thinking loop and made testing not finish. After switching to "medium", it ran stable and complete full testing. So suggest set the reasoning-effort to "meiudm".
Next:
Will test real coding and compare with Unsloth Qwen3.8-27b-UD-Q4_K_XL.