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 evidence/agent-final.json from ajh-code/Jev-Bonsai-Compass: direct link, hf CLI and curl.
- Browser
- Download file 28.6 kB
-
https://huggingface.co/ajh-code/Jev-Bonsai-Compass/resolve/main/evidence/agent-final.json
- Command line
-
hf download hf://ajh-code/Jev-Bonsai-Compass/evidence/agent-final.json
-
curl -L -o agent-final.json https://huggingface.co/ajh-code/Jev-Bonsai-Compass/resolve/main/evidence/agent-final.json
28.6 kB
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| "time_utc": "2026-09-24T09:09:02.248742+00:00", | |
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| } | |
| }, | |
| "lifecycle": [ | |
| { | |
| "time_utc": "2026-09-24T09:09:02.427185+00:00", | |
| "kind": "server_started", | |
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| "command": [ | |
| "llama.cpp-b2/build/bin/llama-server", | |
| "-m", | |
| "models/Ternary-Bonsai-2-27B-PQ2_0.gguf", | |
| "--host", | |
| "0.0.0.0", | |
| "--port", | |
| "5991", | |
| "-c", | |
| "4096", | |
| "-ngl", | |
| "99", | |
| "-fa", | |
| "on", | |
| "--jinja", | |
| "--reasoning", | |
| "off", | |
| "-np", | |
| "1", | |
| "--no-context-shift", | |
| "--cache-ram", | |
| "0", | |
| "--no-cache-idle-slots", | |
| "--lora", | |
| "models/fast-routes-20260924T0101Z/broad-lr0.0001.gguf" | |
| ] | |
| }, | |
| { | |
| "time_utc": "2026-09-24T09:09:04.862338+00:00", | |
| "kind": "server_ready", | |
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| { | |
| "id": 0, | |
| "path": "models/fast-routes-20260924T0101Z/broad-lr0.0001.gguf", | |
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| "task_name": "", | |
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| } | |
| ] | |
| }, | |
| { | |
| "time_utc": "2026-09-24T09:30:16.229790+00:00", | |
| "kind": "server_stopped", | |
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| "exit_code": 0, | |
| "peak_mib": 7844 | |
| } | |
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| } | |
| } | |
| }, | |
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| "cluster_bootstrap_delta_95pct": [ | |
| 0.0, | |
| 0.058333333333333334 | |
| ] | |
| }, | |
| "integrity": { | |
| "frozen_files_and_weights_unchanged": true, | |
| "episodes": 960, | |
| "complete_unique_case_orders": true, | |
| "mechanical_tests": 46 | |
| }, | |
| "limitations": [ | |
| "Final requests were authored from80archetypes,3variants; not independent real-user production tasks.", | |
| "Gold-blind runtime, but same author prepared fixtures and implementation. Final sources frozen before final inference.", | |
| "Memory sampled after readiness on16GiBRTX5080; real8GBcard fit and startup peak are not proven.", | |
| "Logical task counts are not physical external tool calls; reference application only.", | |
| "Content-based unintended-effect metric excludes version-only writes; supplementary audit reports them separately." | |
| ] | |
| } |