Instructions to use Zlib2/bonsai-8b-colab-prebuilt 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 Zlib2/bonsai-8b-colab-prebuilt 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 Zlib2/bonsai-8b-colab-prebuilt # Run inference directly in the terminal: llama cli -hf Zlib2/bonsai-8b-colab-prebuilt
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Zlib2/bonsai-8b-colab-prebuilt # Run inference directly in the terminal: llama cli -hf Zlib2/bonsai-8b-colab-prebuilt
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 Zlib2/bonsai-8b-colab-prebuilt # Run inference directly in the terminal: ./llama-cli -hf Zlib2/bonsai-8b-colab-prebuilt
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 Zlib2/bonsai-8b-colab-prebuilt # Run inference directly in the terminal: ./build/bin/llama-cli -hf Zlib2/bonsai-8b-colab-prebuilt
Use Docker
docker model run hf.co/Zlib2/bonsai-8b-colab-prebuilt
- LM Studio
- Jan
- Ollama
How to use Zlib2/bonsai-8b-colab-prebuilt with Ollama:
ollama run hf.co/Zlib2/bonsai-8b-colab-prebuilt
- Unsloth Desktop
- Pi
How to use Zlib2/bonsai-8b-colab-prebuilt with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Zlib2/bonsai-8b-colab-prebuilt
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": "Zlib2/bonsai-8b-colab-prebuilt" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Zlib2/bonsai-8b-colab-prebuilt with Docker Model Runner:
docker model run hf.co/Zlib2/bonsai-8b-colab-prebuilt
- Lemonade
How to use Zlib2/bonsai-8b-colab-prebuilt with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Zlib2/bonsai-8b-colab-prebuilt
Run and chat with the model
lemonade run user.bonsai-8b-colab-prebuilt-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use Zlib2/bonsai-8b-colab-prebuilt with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Zlib2/bonsai-8b-colab-prebuilt
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 Zlib2/bonsai-8b-colab-prebuilt
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Zlib2/bonsai-8b-colab-prebuilt with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Zlib2/bonsai-8b-colab-prebuilt
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 "Zlib2/bonsai-8b-colab-prebuilt" \ --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"
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Bonsai-8B (GGUF) β Run in 30 Seconds π
β‘ Quick Start
π§ Setup (run once)
import os
import tarfile
print("π¦ Installing dependencies...")
!pip install -q huggingface_hub
from huggingface_hub import hf_hub_download
HF_REPO = "Zlib2/bonsai-8b-colab-prebuilt"
print("\nπ₯ 1/3 Downloading pre-built llama.cpp (Fast)...")
llama_zip = hf_hub_download(repo_id=HF_REPO, filename="llama_cpp_prebuilt.tar.gz")
print("π₯ 2/3 Downloading Bonsai-8B.gguf model (Large file)...")
model_path = hf_hub_download(repo_id=HF_REPO, filename="Bonsai-8B.gguf")
print("π¦ 3/3 Extracting files...")
!mkdir -p /content/llama.cpp
with tarfile.open(llama_zip, "r:gz") as tar:
tar.extractall(path="/content/llama.cpp")
!chmod +x /content/llama.cpp/build/bin/llama-cli
print("\nπ Setup complete!")
π€ Run Inference
USER_PROMPT = "Explain quantum computing in simple terms."
SYSTEM_PROMPT = "You are a helpful assistant"
!/content/llama.cpp/build/bin/llama-cli \
-m "{model_path}" \
--system-prompt "{SYSTEM_PROMPT}" \
-p "{USER_PROMPT}" \
-n 4096 \
--temp 0.5 \
--top-p 0.85 \
--top-k 20 \
-ngl 99
- Downloads last month
- 12
Hardware compatibility
Log In to add your hardware
We're not able to determine the quantization variants.
Inference Providers NEW
This model isn't deployed by any Inference Provider. π Ask for provider support