Instructions to use georgeanton/alice-m5-cortex-8b-6.3gb 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 georgeanton/alice-m5-cortex-8b-6.3gb 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 georgeanton/alice-m5-cortex-8b-6.3gb # Run inference directly in the terminal: llama cli -hf georgeanton/alice-m5-cortex-8b-6.3gb
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf georgeanton/alice-m5-cortex-8b-6.3gb # Run inference directly in the terminal: llama cli -hf georgeanton/alice-m5-cortex-8b-6.3gb
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 georgeanton/alice-m5-cortex-8b-6.3gb # Run inference directly in the terminal: ./llama-cli -hf georgeanton/alice-m5-cortex-8b-6.3gb
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 georgeanton/alice-m5-cortex-8b-6.3gb # Run inference directly in the terminal: ./build/bin/llama-cli -hf georgeanton/alice-m5-cortex-8b-6.3gb
Use Docker
docker model run hf.co/georgeanton/alice-m5-cortex-8b-6.3gb
- LM Studio
- Jan
- Ollama
How to use georgeanton/alice-m5-cortex-8b-6.3gb with Ollama:
ollama run hf.co/georgeanton/alice-m5-cortex-8b-6.3gb
- Unsloth Desktop
- Pi
How to use georgeanton/alice-m5-cortex-8b-6.3gb with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf georgeanton/alice-m5-cortex-8b-6.3gb
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": "georgeanton/alice-m5-cortex-8b-6.3gb" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use georgeanton/alice-m5-cortex-8b-6.3gb with Docker Model Runner:
docker model run hf.co/georgeanton/alice-m5-cortex-8b-6.3gb
- Lemonade
How to use georgeanton/alice-m5-cortex-8b-6.3gb with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull georgeanton/alice-m5-cortex-8b-6.3gb
Run and chat with the model
lemonade run user.alice-m5-cortex-8b-6.3gb-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use georgeanton/alice-m5-cortex-8b-6.3gb with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf georgeanton/alice-m5-cortex-8b-6.3gb
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 georgeanton/alice-m5-cortex-8b-6.3gb
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use georgeanton/alice-m5-cortex-8b-6.3gb with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf georgeanton/alice-m5-cortex-8b-6.3gb
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 "georgeanton/alice-m5-cortex-8b-6.3gb" \ --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"
| license: apache-2.0 | |
| tags: | |
| - sifta | |
| - ollama | |
| - gemma4 | |
| - multimodal | |
| - local-ai | |
| library_name: ollama | |
| # Alice M5 Cortex 8B 6.3GB | |
| Primary M5 SIFTA cortex tag: | |
| ```bash | |
| ollama create alice-m5-cortex-8b-6.3gb:latest -f Modelfile | |
| ``` | |
| This is the promoted M5 cortex used by Alice on the Foundry node. It is a | |
| SIFTA-owned Ollama tag and does not use the retired LoRA candidate. | |
| ## Live Probe | |
| Verified on 2026-05-09: | |
| - architecture: Gemma4 | |
| - parameters: 8B | |
| - context length: 131072 | |
| - runtime context: 8192 | |
| - capabilities reported by `ollama show`: completion, vision, audio, tools, | |
| thinking | |
| - `/api/chat` with `think:false` answered both text and image prompts | |
| ## SIFTA Runtime Note | |
| Alice's Talk path uses `/api/chat` with `think:false`. Raw calls that omit this | |
| can spend the whole output budget in the thinking field and return blank | |
| assistant content. | |
| ## SIFTA Field Breakthrough | |
| The current SIFTA public repo includes a stigmergic field breakthrough brief: | |
| https://github.com/antonpictures/ANTON-SIFTA/blob/main/Documents/CARLTON_STIGMERGIC_FIELD_BREAKTHROUGH_2026-05-11.md | |
| The cortex is only one organ in that system. The field mechanism itself runs in | |
| the Python body: Bell analogue simulator, kernel scheduler, and hippocampus. | |
| Credit boundary: Bell/CHSH/Hall/pilot-wave/stigmergy literature grounds the | |
| analogy; SIFTA claims a receipt-backed classical contextual analogue, not a | |
| proof of the physical cause of quantum nonlocality. | |
| ## Local State Boundary | |
| This model package is public species DNA. It must not include a node's raw | |
| `.sifta_state/` memory, contacts, owner traces, camera frames, or private | |
| receipts. | |