Text Generation
GGUF
llama.cpp
conversational
1-bit
llama-cpp
cuda
metal
on-device
hybrid-attention
prismml
bonsai
Eval Results
Instructions to use prism-ml/Bonsai-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/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 prism-ml/Bonsai-27B-gguf:F16 # Run inference directly in the terminal: llama cli -hf prism-ml/Bonsai-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/Bonsai-27B-gguf:F16 # Run inference directly in the terminal: llama cli -hf prism-ml/Bonsai-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/Bonsai-27B-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf prism-ml/Bonsai-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/Bonsai-27B-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf prism-ml/Bonsai-27B-gguf:F16
Use Docker
docker model run hf.co/prism-ml/Bonsai-27B-gguf:F16
- LM Studio
- Jan
- vLLM
How to use prism-ml/Bonsai-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/Bonsai-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/Bonsai-27B-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/prism-ml/Bonsai-27B-gguf:F16
- Ollama
How to use prism-ml/Bonsai-27B-gguf with Ollama:
ollama run hf.co/prism-ml/Bonsai-27B-gguf:F16
- Unsloth Desktop
- Pi
How to use prism-ml/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 prism-ml/Bonsai-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/Bonsai-27B-gguf:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prism-ml/Bonsai-27B-gguf with Docker Model Runner:
docker model run hf.co/prism-ml/Bonsai-27B-gguf:F16
- Lemonade
How to use prism-ml/Bonsai-27B-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prism-ml/Bonsai-27B-gguf:F16
Run and chat with the model
lemonade run user.Bonsai-27B-gguf-F16
List all available models
lemonade list
- Hermes Agent
How to use prism-ml/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 prism-ml/Bonsai-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/Bonsai-27B-gguf:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prism-ml/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 prism-ml/Bonsai-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/Bonsai-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"
Additional benchmarks?
#4
by SRZhu26 - opened
It's quite difficult to compare to other models like 3.6 35b or 3.5 9b and 4b considering there's not that much known about specific benchmarks used and their scores. Would this be a point of expansion for the model card in the future?
SRZhu26 changed discussion status to closed