Aria Sar

• 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

gemma-sar.gguf

gguf · 3.85 GB · SHA-256 ac266e00b5c7…f0d8 · Hugging Face

--- license: mit base_model: google/gemma-3-4b-it tags: - search-and-rescue - object-detection - gemma - unsloth - ollama --- # 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](https://github.com/jadynwor/ARIA) for full deployment instructions. ```bash # 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 ```