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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
 
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+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.