What you'll learn:
- Design and fine-tune small language models (1–7B) specifically for edge and mobile devices, balancing accuracy, size, and latency
- Apply LoRA and QLoRA to fine-tune SLMs on consumer GPUs, drastically reducing VRAM needs and training time for real projects
- Quantize fine-tuned models (INT8/INT4), convert them to edge-friendly formats, and deploy them on phones, tablets, and Raspberry Pi
- Build an end‑to‑end pipeline from data preparation and hyperparameter tuning to on‑device validation, benchmarking, and optimization
- Decide when to use prompt engineering, RAG, or fine‑tuning, and justify edge deployment versus cloud APIs for different business use cases
- Select the right SLM family (Gemma, Phi, Llama, Mistral) for your constraints in VRAM, hardware, privacy, and on‑device performance
- Design high‑quality instruction datasets and splits, avoiding overfitting and catastrophic forgetting in small, specialized models
- Package, version, and update on‑device models (monolithic vs modular adapters) for real‑world apps like classification, support bots, and content generation
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 clear roadmap and conceptual understanding of how to design, train, and ship on-device AI systems, moving from theory to production-ready strategies.
