Text Classification
GGUF
jev-style
English
jev
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.
- Libraries
- jev-style
How to use ajh-code/Jev-Bonsai-Compass with jev-style:
pip install jev-style # GGUF builds score through llama.cpp: build the jev-score binary once hf download ajh-code/Jev-Bonsai-Compass build_jev_score.sh jev_score.cpp --local-dir jev-score export JEV_SCORE_BIN=$(sh jev-score/build_jev_score.sh /path/to/llama.cpp | tail -n 1)
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("ajh-code/Jev-Bonsai-Compass") out = js.decide("I was charged twice for one order.", { "billing": noul("This message is about billing."), "team": choice("Which team should handle it?", ["billing", "shipping", "tech"]), }) print(out["answers"]["team"]["choice"]) - 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"
Download run.sh from ajh-code/Jev-Bonsai-Compass: direct link, hf CLI and curl.
- Browser
- Download file 2.2 kB
-
https://huggingface.co/ajh-code/Jev-Bonsai-Compass/resolve/main/run.sh
- Command line
-
hf download hf://ajh-code/Jev-Bonsai-Compass/run.sh
-
curl -L -o run.sh https://huggingface.co/ajh-code/Jev-Bonsai-Compass/resolve/main/run.sh
2.2 kB
| set -euo pipefail | |
| bundle_dir="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)" | |
| mode="${1:-serve}" | |
| if [[ "$mode" == serve || "$mode" == decide ]]; then | |
| shift || true | |
| else | |
| mode=serve | |
| fi | |
| profile=current | |
| args=("$@") | |
| for ((i=0; i<${#args[@]}; i++)); do | |
| if [[ "${args[$i]}" == --profile && $((i+1)) -lt ${#args[@]} ]]; then | |
| profile="${args[$((i+1))]}" | |
| fi | |
| done | |
| if [[ "$profile" != current && "$profile" != base ]]; then | |
| echo 'Profile must be current or base' >&2 | |
| exit 2 | |
| fi | |
| native="$bundle_dir/native/bin/llama-server" | |
| model="$bundle_dir/models/Ternary-Bonsai-2-27B-PQ2_0.gguf" | |
| adapter="$bundle_dir/adapters/broad-lr0.0001.adapter" | |
| [[ -f "$native" && -f "$model" ]] || { echo 'Bundle is incomplete: native server or model missing' >&2; exit 2; } | |
| # Some repository download clients materialize links as files or omit them. | |
| ensure_link() { | |
| local name="$1" target="$2" | |
| if [[ ! -e "$bundle_dir/native/bin/$name" && ! -L "$bundle_dir/native/bin/$name" ]]; then | |
| ln -s "$target" "$bundle_dir/native/bin/$name" | |
| fi | |
| } | |
| for library in libggml-base libggml-cpu libggml-cuda libggml; do | |
| ensure_link "$library.so.0" "$library.so.0.21.0" | |
| ensure_link "$library.so" "$library.so.0" | |
| done | |
| for library in libllama-common libllama libmtmd; do | |
| ensure_link "$library.so.0" "$library.so.0.2.0" | |
| ensure_link "$library.so" "$library.so.0" | |
| done | |
| if [[ ! -x "$native" ]]; then | |
| chmod u+x "$native" || { echo 'Native server needs executable permission' >&2; exit 2; } | |
| fi | |
| extra=() | |
| if [[ "$profile" == current ]]; then | |
| [[ -f "$adapter" ]] || { echo 'Bundle is incomplete: adapter missing' >&2; exit 2; } | |
| extra+=(--adapter "$adapter") | |
| fi | |
| cd "$bundle_dir" | |
| export PYTHONPATH="$bundle_dir/source${PYTHONPATH:+:$PYTHONPATH}" | |
| export PYTHONDONTWRITEBYTECODE=1 | |
| python_bin="${BONSAI_PYTHON:-python3}" | |
| if [[ "$mode" == decide ]]; then | |
| exec "$python_bin" -c 'from bonsai_runtime.service import cli; cli()' \ | |
| --launch --profile "$profile" --model "$model" --binary "$native" "${extra[@]}" "${args[@]}" | |
| fi | |
| exec "$python_bin" -c 'from bonsai_runtime.service import http_cli; http_cli()' \ | |
| --launch --profile "$profile" --model "$model" --binary "$native" "${extra[@]}" "${args[@]}" | |