Fundamentals of SLM Fine-Tuning: LoRA, Quantization & Edge

About this course

This course provides a comprehensive technical framework for fine-tuning Small Language Models (SLMs) and deploying them on edge devices.

Moving beyond the hype of massive cloud models, this guide focuses on the engineering reality of running private, offline AI. You will learn the end-to-end methodology to transform general-purpose models (1–7B parameters) into specialized, efficient tools that run directly on user hardware, without depending on internet connectivity or external APIs.

What you will learn:

The Strategic Shift to Edge AI: Understand the architectural trade-offs between Cloud and Edge. We analyze exactly when to move processing to the device to solve issues of latency, data privacy, and recurring cloud costs.

Small Language Models (SLMs) Deep Dive: A technical breakdown of the SLM landscape (Phi, Gemma, Llama, Mistral) and why their architecture makes them viable for smartphones, tablets, and embedded IoT systems.

Optimization Techniques (The "How-To"): We deconstruct the core mechanisms of Parameter-Efficient Fine-Tuning (PEFT). You will understand how LoRA and QLoRA work to adapt models using consumer-grade GPUs, and how Quantization (INT4/INT8) reduces model size without destroying performance.

The Deployment Pipeline: A step-by-step look at the lifecycle of a local model: from dataset preparation and hyperparameter selection to conversion into edge-friendly formats (like GGUF or ONNX).

Real-World Production Scenarios: We examine concrete case studies including enterprise document classification and offline support assistants to validate how these systems perform regarding memory usage, battery life, and inference speed.

Who is this for: This course is designed for AI architects, technical leads, and engineers who need a clea

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