Instructions to use ajh-code/Jev-Bonsai-Compass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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"

Jev-Bonsai-Compass

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Bonsai decision runtime

The newly packaged bonsai_runtime software is distributed under Apache-2.0.
This statement applies to the runtime package, not all files in the research
workspace, and does not relicense model weights or optional adapters.

Letter-option prompting/readout follows OpenJev's Apache-2.0 helper interface.
The local implementation is modified for the Prism native server, explicit
adapter profiles, compact numerical tools, and bounded local API entry points.
OpenJev project: https://huggingface.co/openjev/openjev
Its helper/ and serve/ code are Apache-2.0; its weights are CC-BY-NC-4.0.
No OpenJev weights or extracted adapter tensors are included in this package.

The separate native llama.cpp-b2 runtime is MIT-licensed by the ggml authors.
The separate unmodified Ternary-Bonsai-2-27B weights are Apache-2.0 according
to the pinned upstream license. Optional local OpenJev-derived adapters retain
their separately documented noncommercial lineage.

The tzdata dependency includes its own upstream license and IANA data notices.
See the local release manifest for exact artifacts and source revisions.