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Edge AI with SLMs: Fine-Tuning & Local Deployment
https://WebToolTip.com
Published 2/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 51m | Size: 1.28 GB
The complete guide to running private, offline AI on mobile & IoT. Master LoRA, Quantization, and Small Language Models.
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
Requirements
A general understanding of what AI or “ChatGPT‑style” models are is useful, but the course includes a quick conceptual recap so motivated beginners can follow
Access to a computer (Windows, macOS o Linux) where you can install Python and common AI libraries; no need for prior setup experience
Basic Python knowledge (variables, functions, and running simple scripts) is helpful but not strictly required; all code is explained step by step |
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