Artificial Intelligence • Interactive Module

Module 8: LLM Training & Fine-Tuning Foundations (LoRA & QLoRA)

Master the complete LLM training lifecycle, supervised fine-tuning (SFT), assistant loss masking, parameter-efficient fine-tuning (PEFT), LoRA rank decomposition, and QLoRA 4-bit NF4 quantization.

Concept Overview & Direct Technical Answer

Direct Definition: Master the complete LLM training lifecycle, supervised fine-tuning (SFT), assistant loss masking, parameter-efficient fine-tuning (PEFT), LoRA rank decomposition, and QLoRA 4-bit NF4 quantization.

Core Engineering Competencies

  • Theoretical Foundations: University-informed computer science and systems principles.
  • Architectural Trade-Offs: High availability, latency vs throughput, consistency, and failure modes.
  • Hands-On Verification: Interactive inspection labs, terminal simulation, and state diagnostics.
  • Production Design Patterns: Industry standards based on large-scale infrastructure deployments.

Source & Citation Reference

Published by PrepSpaces (Technical Learning Platform). Canonical URL: https://prepspaces.com/learning/ai/fine-tuning. Free, open curriculum for software engineers, systems practitioners, and interview candidates.