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 / evidence /core-resource-summary.json

ajh-code

Publish Jev-Bonsai-Compass runtime, adapter, card and evidence

b4b0f75 verified 9 days ago

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	{
	"profiles": {
	"base": {
	"samples": 707,
	"sampled_wall_seconds": 425.43988013267517,
	"native_peak_rss_mib": 7271.453125,
	"wrapper_peak_rss_mib": 47.34765625,
	"native_cpu_seconds": 421,
	"wrapper_cpu_seconds": 4,
	"native_average_cpu_cores": 0.9895640245778309,
	"wrapper_average_cpu_cores": 0.009402033487675352,
	"gpu_peak_mib_including_sampled_startup": 7510,
	"first_gpu_sample_mib": 2
	},
	"current": {
	"samples": 883,
	"sampled_wall_seconds": 531.5644588470459,
	"native_peak_rss_mib": 6543.625,
	"wrapper_peak_rss_mib": 50.05078125,
	"native_cpu_seconds": 527,
	"wrapper_cpu_seconds": 4,
	"native_average_cpu_cores": 0.9914131602083666,
	"wrapper_average_cpu_cores": 0.007524957572739025,
	"gpu_peak_mib_including_sampled_startup": 7844,
	"first_gpu_sample_mib": 270
	}
	},
	"notes": [
	"RSS includes resident shared/file-backed pages; do not interpret as private allocated RAM.",
	"Observer follows only study-owned processes and GPU0; sampling can miss transient peaks.",
	"Wrapper here is the research driver. Installed minimal CLI/HTTP process snapshots are reported separately.",
	"CPU time granularity is one second; average CPU cores are process CPU seconds / sampled wall time."
	]
	}