Artificial Intelligence • Interactive Module

Module 21: Fine-Tuning LLMs for Domain-Specific Applications

Master enterprise domain LLM fine-tuning: parameter-efficient fine-tuning (PEFT), LoRA matrix decomposition, QLoRA 4-bit NF4 quantization, conversational JSONL dataset engineering, SFTTrainer with response-only loss masking, model merging, and vLLM deployment.

Concept Overview & Direct Technical Answer

Direct Definition: Master enterprise domain LLM fine-tuning: parameter-efficient fine-tuning (PEFT), LoRA matrix decomposition, QLoRA 4-bit NF4 quantization, conversational JSONL dataset engineering, SFTTrainer with response-only loss masking, model merging, and vLLM deployment.

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-domain. Free, open curriculum for software engineers, systems practitioners, and interview candidates.