How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jadynwor/aria-sar# Run inference directly in the terminal:
llama cli -hf jadynwor/aria-sarBuild 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 jadynwor/aria-sar# Run inference directly in the terminal:
./build/bin/llama cli -hf jadynwor/aria-sarUse Docker
docker model run hf.co/jadynwor/aria-sarQuick Links
ARIA-SAR Models
Three fine-tuned models for aerial search and rescue drone analysis. Built for the Gemma 4 Good Hackathon.
Models Included
combined_best.pt — YOLOv9c human detection
- Fine-tuned on HERIDAL + SARD datasets (5,123 images)
- 0.861 mAP50 accuracy
- Detects people from drone altitude
efficientnet_distress.pt — EfficientNet-B0 distress classifier
- Fine-tuned on 6,024 aerial person crops
- 92% validation accuracy
- Classes: LYING_DOWN, STATIONARY, OBSCURED
gemma-sar.gguf — Gemma 4B rescue briefing generator
- Base: google/gemma-3-4b-it
- Fine-tuned with Unsloth on 100 SAR examples
- Training loss: 2.91 → 0.54
- Quantized Q8_0 GGUF via llama.cpp
- Runs via Ollama as "aria-sar"
Usage
See the ARIA GitHub repo for full deployment instructions.
# Load Gemma into Ollama
docker cp gemma-sar.gguf ollama:/tmp/gemma-sar.gguf
docker exec ollama sh -c "echo 'FROM /tmp/gemma-sar.gguf' > /tmp/Modelfile"
docker exec ollama ollama create aria-sar -f /tmp/Modelfile
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Hardware compatibility
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf jadynwor/aria-sar# Run inference directly in the terminal: llama cli -hf jadynwor/aria-sar