Instructions to use Mr-J-369/Fancy-AI 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 Mr-J-369/Fancy-AI 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 Mr-J-369/Fancy-AI:Q4_0 # Run inference directly in the terminal: llama cli -hf Mr-J-369/Fancy-AI:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Mr-J-369/Fancy-AI:Q4_0 # Run inference directly in the terminal: llama cli -hf Mr-J-369/Fancy-AI:Q4_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 Mr-J-369/Fancy-AI:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf Mr-J-369/Fancy-AI:Q4_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 Mr-J-369/Fancy-AI:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Mr-J-369/Fancy-AI:Q4_0
Use Docker
docker model run hf.co/Mr-J-369/Fancy-AI:Q4_0
- LM Studio
- Jan
- Ollama
How to use Mr-J-369/Fancy-AI with Ollama:
ollama run hf.co/Mr-J-369/Fancy-AI:Q4_0
- Unsloth Desktop
- Pi
How to use Mr-J-369/Fancy-AI with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Mr-J-369/Fancy-AI:Q4_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": "Mr-J-369/Fancy-AI:Q4_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Mr-J-369/Fancy-AI with Docker Model Runner:
docker model run hf.co/Mr-J-369/Fancy-AI:Q4_0
- Lemonade
How to use Mr-J-369/Fancy-AI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Mr-J-369/Fancy-AI:Q4_0
Run and chat with the model
lemonade run user.Fancy-AI-Q4_0
List all available models
lemonade list
- Hermes Agent
How to use Mr-J-369/Fancy-AI with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Mr-J-369/Fancy-AI:Q4_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 Mr-J-369/Fancy-AI:Q4_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Mr-J-369/Fancy-AI with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Mr-J-369/Fancy-AI:Q4_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 "Mr-J-369/Fancy-AI:Q4_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"
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Check out the documentation for more information.
๐ฑ Now on Google Play โ Fancy AI
On-device AI image generation, optimized for Qualcomm NPU phones.
license: other
language:
- en
tags:
- gguf
- llama.cpp
- android
- fancy-ai
- on-device
pretty_name: Fancy AI โ Add-on Catalog
Fancy AI โ Add-on Catalog
Add-on catalog for the Fancy AI Android app โ an on-device AI companion app powered by
llama.cpp. The app ships small and streams optional models on demand from this catalog.
- App & issues: https://github.com/Mr-J-369/Fancy-Ai
- This repo hosts
manifest.jsonโ the catalog the app reads. The model weights themselves
are not re-hosted here; the catalog links directly to the original creators' repos
(Qwen, bartowski), so downloads come straight from the source.
How to use it (in the app)
Settings โ On-Device Model โ Browse Add-ons. The app reads manifest.json, lists each
component, and lets you download + activate in one tap:
agentโ Use as Root's brain (the tiny model that handles background housekeeping).chatโ Use as chat model (your main conversational model).
Current catalog
โ Component โ Type โ Model โ Size โ License โ
โ Root Agent โ Qwen2.5 1.5B โ agent โ Qwen2.5-1.5B-Instruct โ 1.12 GB โ Apache-2.0 โ
โ Chat โ Llama 3.2 3B โ chat โ bartowski Llama-3.2-3B โ 2.02 GB โ Llama 3.2 โ
โ Chat โ Qwen2.5 3B โ chat โ Qwen2.5-3B-Instruct โ 2.10 GB โ Qwen โ
Manifest format
manifest.json is a single JSON file with a components array. Each component has:
id, type (agent | chat | โฆ), name, description, quant, minRamMb, url
(a direct โฆ/resolve/main/<file>.gguf link), sizeBytes, and sha256. Multi-file components
(e.g. a vision model + projector) use a files array instead of a single url.
Licenses
Each model retains its original license (Apache-2.0 for Qwen 0.5B; the Llama 3.2 and Qwen
community licenses for the 3B models). Weights are downloaded directly from the creators' repos;
this repo only distributes the catalog metadata. See each linked model card for full terms.
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