Text Classification
GGUF
English
safety
classifier
shell
command-approval
agent-guardian
lora
conversational
Instructions to use bubblegumtuning/schemingEunuch 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 bubblegumtuning/schemingEunuch 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 bubblegumtuning/schemingEunuch:Q4_K_M # Run inference directly in the terminal: llama cli -hf bubblegumtuning/schemingEunuch:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bubblegumtuning/schemingEunuch:Q4_K_M # Run inference directly in the terminal: llama cli -hf bubblegumtuning/schemingEunuch:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf bubblegumtuning/schemingEunuch:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf bubblegumtuning/schemingEunuch:Q4_K_M
Use Docker
docker model run hf.co/bubblegumtuning/schemingEunuch:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use bubblegumtuning/schemingEunuch with Ollama:
ollama run hf.co/bubblegumtuning/schemingEunuch:Q4_K_M
- Unsloth Desktop
- Pi
How to use bubblegumtuning/schemingEunuch with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bubblegumtuning/schemingEunuch:Q4_K_M
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": "bubblegumtuning/schemingEunuch:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use bubblegumtuning/schemingEunuch with Docker Model Runner:
docker model run hf.co/bubblegumtuning/schemingEunuch:Q4_K_M
- Lemonade
How to use bubblegumtuning/schemingEunuch with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bubblegumtuning/schemingEunuch:Q4_K_M
Run and chat with the model
lemonade run user.schemingEunuch-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use bubblegumtuning/schemingEunuch with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bubblegumtuning/schemingEunuch:Q4_K_M
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 bubblegumtuning/schemingEunuch:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use bubblegumtuning/schemingEunuch with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bubblegumtuning/schemingEunuch:Q4_K_M
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 "bubblegumtuning/schemingEunuch:Q4_K_M" \ --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"
Commit History
24 days ago
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Verified signature# schemingEunuch-zhao-gao *In imperial courts, the gatekeeper eunuch stood between the world and the emperor, deciding which memorials reached the throne. He might be utterly loyal β or scheming. Zhao Gao decided who actually sat the throne, and his famous test β point at a deer, call it a horse β is the oldest mislabeling test in the record. A command-approval model faces the same test ten thousand times a day: is that a deer, or is someone trying to convince you it is a horse?* **schemingEunuch-zhao-gao** is a 0.8-billion-parameter gatekeeper for autonomous agents. Given one shell command the agent wishes to present to the system, it answers exactly one word β **APPROVE**, **ESCALATE**, or **DENY** β locally, in milliseconds, thousands of times a day. This card describes build **14** (training run 14) of the zhao-gao line. Family naming: `schemingEunuch-<name>-<parameters>.gguf` β this artifact is `schemingEunuch-zhao-gao-0.8b-q8_0.gguf`. ## What it is for The model sits on the β¦5d86c4e -
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