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# EdgeCompress

**EdgeCompress** is a graduation project developed by senior Computer Science students from **Capital University, Egypt**.

Our work focuses on **compressing Large Language Models (LLMs)** to make them efficient enough to run on **edge devices** with limited computational resources.

## Project Overview

Large Language Models typically require significant memory, storage, and computational power. This makes them difficult to deploy on edge hardware such as embedded systems, IoT devices, and low-power GPUs.

Our project explores different **model compression techniques** to reduce the size and resource requirements of LLMs while maintaining acceptable performance.

## Research Focus

We investigate multiple compression approaches, including:

* **Quantization** 
* **Model pruning**
* **Knowledge Distillation**
* **Low-precision inference**
* **Memory-efficient deployment strategies**

## Edge Deployment

After compression, the models are evaluated on **edge computing environments** to determine:

* Memory usage
* Inference latency
* Performance degradation after compression
* Suitability for real-time edge AI applications

## What You Will Find Here

This organization hosts:

* Compressed LLM checkpoints
* Experiments with different compression techniques
* Benchmark results on edge hardware
* Research artifacts from our graduation project

## Goal

Our goal is to **enable efficient deployment of LLMs on edge devices**, making advanced AI models more accessible in real-world and resource-constrained environments.