Text Classification
GGUF
English
jev
jev-style
openjev
decision-model
typed-decisions
bonsai
ternary
local-inference
blackwell
conversational
Instructions to use ajh-code/Jev-Bonsai-Compass 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 ajh-code/Jev-Bonsai-Compass 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 ajh-code/Jev-Bonsai-Compass:Q2_0 # Run inference directly in the terminal: llama cli -hf ajh-code/Jev-Bonsai-Compass:Q2_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ajh-code/Jev-Bonsai-Compass:Q2_0 # Run inference directly in the terminal: llama cli -hf ajh-code/Jev-Bonsai-Compass:Q2_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 ajh-code/Jev-Bonsai-Compass:Q2_0 # Run inference directly in the terminal: ./llama-cli -hf ajh-code/Jev-Bonsai-Compass:Q2_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 ajh-code/Jev-Bonsai-Compass:Q2_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ajh-code/Jev-Bonsai-Compass:Q2_0
Use Docker
docker model run hf.co/ajh-code/Jev-Bonsai-Compass:Q2_0
- LM Studio
- Jan
- Ollama
How to use ajh-code/Jev-Bonsai-Compass with Ollama:
ollama run hf.co/ajh-code/Jev-Bonsai-Compass:Q2_0
- Unsloth Desktop
- Pi
How to use ajh-code/Jev-Bonsai-Compass with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ajh-code/Jev-Bonsai-Compass:Q2_0
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": "ajh-code/Jev-Bonsai-Compass:Q2_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ajh-code/Jev-Bonsai-Compass with Docker Model Runner:
docker model run hf.co/ajh-code/Jev-Bonsai-Compass:Q2_0
- Lemonade
How to use ajh-code/Jev-Bonsai-Compass with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ajh-code/Jev-Bonsai-Compass:Q2_0
Run and chat with the model
lemonade run user.Jev-Bonsai-Compass-Q2_0
List all available models
lemonade list
- Hermes Agent
How to use ajh-code/Jev-Bonsai-Compass with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ajh-code/Jev-Bonsai-Compass:Q2_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 ajh-code/Jev-Bonsai-Compass:Q2_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ajh-code/Jev-Bonsai-Compass with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ajh-code/Jev-Bonsai-Compass:Q2_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 "ajh-code/Jev-Bonsai-Compass:Q2_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"
| """Check downloaded bundle files against the published SHA256 manifest.""" | |
| from hashlib import sha256 | |
| import json | |
| from pathlib import Path | |
| import sys | |
| root = Path(__file__).resolve().parent | |
| manifest = json.loads((root / "MANIFEST.json").read_text()) | |
| errors = [] | |
| for name, expected in manifest["files"].items(): | |
| path = root / name | |
| if not path.is_file(): | |
| errors.append(f"missing: {name}") | |
| continue | |
| if path.stat().st_size != expected["bytes"]: | |
| errors.append(f"size mismatch: {name}") | |
| continue | |
| digest = sha256() | |
| with path.open("rb") as stream: | |
| for chunk in iter(lambda: stream.read(4 * 1024 * 1024), b""): | |
| digest.update(chunk) | |
| if digest.hexdigest() != expected["sha256"]: | |
| errors.append(f"SHA256 mismatch: {name}") | |
| if errors: | |
| print("\n".join(errors), file=sys.stderr) | |
| raise SystemExit(1) | |
| print(f"Verified {len(manifest['files'])} bundle files") | |