Instructions to use mondk/fly-brain 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 mondk/fly-brain 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 mondk/fly-brain:F16 # Run inference directly in the terminal: llama cli -hf mondk/fly-brain:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mondk/fly-brain:F16 # Run inference directly in the terminal: llama cli -hf mondk/fly-brain:F16
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 mondk/fly-brain:F16 # Run inference directly in the terminal: ./llama-cli -hf mondk/fly-brain:F16
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 mondk/fly-brain:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf mondk/fly-brain:F16
Use Docker
docker model run hf.co/mondk/fly-brain:F16
- LM Studio
- Jan
- Ollama
How to use mondk/fly-brain with Ollama:
ollama run hf.co/mondk/fly-brain:F16
- Unsloth Desktop
- Docker Model Runner
How to use mondk/fly-brain with Docker Model Runner:
docker model run hf.co/mondk/fly-brain:F16
- Lemonade
How to use mondk/fly-brain with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mondk/fly-brain:F16
Run and chat with the model
lemonade run user.fly-brain-F16
List all available models
lemonade list
- Atomic Chat
I don't know what I created it for, lol
7 Labels
| label | meaning |
|---|---|
move_forward |
move straight when the path is clear |
fly |
take off / fly |
turn_left / turn_right |
turn left/right when encountering an obstacle on the right/left |
spin_360 |
rotate and re-scan when disoriented |
evade_threat |
evade threats (shadows, swatters, predators) |
land_on_food |
land where a food scent is detected |
Usage
1) GGUF F16 / Q4_K_M with llama.cpp / Ollama / LM Studio:
# Download
hf download mondk/fly-brain fly-behavior-classifier.F16.gguf
# or: huggingface-cli download mondk/fly-brain fly-behavior-classifier.F16.gguf
# Ollama
cat > Modelfile <<'EOF'
FROM ./fly-behavior-classifier.F16.gguf
EOF
ollama create fly-brain -f Modelfile
ollama run fly-brain
# llama.cpp (embedding/classifier โ this GGUF is a DistilBERT classifier, not a chat LM,
# so don't expect it to chat like an LLM; the file is primarily for loading via llama.cpp/LM Studio.
# The actual classification demo still runs via Transformers as shown above)
./llama.cpp/build/bin/llama-cli -m fly-behavior-classifier.F16.gguf -p "turn_left"
2) Space: https://huggingface.co/spaces/mondk/fly-brain
fly-behavior-classifier.F16.gguf(135 MB, F16) โ Full GGUFfly-behavior-classifier.Q4_K_M.gguf(47.4 MB, 4-bit) โ Quantized GGUF, faster loadingfly-brain/โ FlyWire LIF simulation code + 3D viewer (not directly related to the classifier; included for reference to the actual connectome)
ty
- Downloads last month
- 69
Hardware compatibility
Log In to add your hardware
4-bit
16-bit