---
license: mit
language:
- en
base_model:
- google/gemma-4-E2B-it
tags:
- gemma
- gguf
- multimodal
- vision
- wildlife-monitoring
- quantized
- audio
- text
---
## 🐘 EleGuard: Multimodal Elephant Detection

**EleGuard** is a specialized, multimodal Vision-Language Model (VLM) developed for the **24/7 monitoring of elephant activity** in natural habitats. By leveraging infrared (IR) imagery and bioacoustic signals, EleGuard provides a robust solution for human-elephant conflict mitigation and wildlife conservation.

## Model Summary
*   **Project Name:** EleGuard
*   **Base Architecture:** This model is a variant based on **Gemma 4 E2B**.
*   **Modality:** Multimodal (Vision + Acoustic via Spectrograms).
*   **Format:** GGUF (Optimized for edge deployment).
*   **Training data:** [EleGuard Dataset](https://www.kaggle.com/datasets/malithabandara/eleguard-dataset)
*   **Training Method:** Knowledge Distillation from Gemini 3.1 Flash.

## Technical Innovation: Reasoning Distillation
The core breakthrough of EleGuard is the shift from simple classification to **expert reasoning**. Instead of training only on labels, the model was fine-tuned on "thought blocks" generated by a Teacher model (Gemini 3.1 Flash). 

For every image or audio sample, the model is trained to explain its reasoning—such as identifying thermal signatures in thick brush or frequency patterns in a rumble—before outputting a final status:
*   **ALERT:** Elephant presence confirmed.
*   **SAFE:** No threat detected.

## Dataset Details
The model was trained on a curated dataset of **2,600 samples** organized into:
*   **Visual Imagery:** High-resolution daytime and **Infrared (IR)** forest captures.
*   **Acoustic Data:** Mel Spectrograms identifying vocalizations like rumbles, roars, and trumpets.
*   **Paired Expert Labels:** Detailed JSON reasoning files for every media asset.

## Usage & Deployment
This repository contains the model weights in **GGUF** format, specifically optimized for edge devices (Raspberry Pi, Jetson Nano, or standard laptops) using tools like `llama.cpp` or `Ollama`.

### Required Files:
1.  `EleGuard-gemma-4-e2b-it.GGUF` (Main model weights)
2.  `EleGuard-gemma-4-e2b-it.mmproj.GGUF` (Multimodal vision projector)

## Acknowledgments & Trademarks
*   Gemma is a trademark of Google LLC.
*   EleGuard is a model trained on a dataset based on Gemma 4 E2B.
*   This project was developed for [The Gemma 4 Good Hackathon](https://www.kaggle.com/competitions/gemma-4-good-hackathon/overview) using the Unsloth fine-tuning framework.
---