Full-Stack AI & Systems Roadmap
Model Fine-Tuning & Parameter-Efficient Adaptation
PEFT, LoRA, QLoRA, SFT data pipelines, DPO, and Unsloth acceleration.
2 weeks · wk 18–19Core / deep pace5 topics15 Interview Questions
Why this pace:When prompt engineering cannot alter model style, domain grammar, or low-latency output formats, fine-tuning teaches models new behaviors and specialized domain knowledge at lower serving costs.
SEC FOCUS
Catastrophic forgetting can occur during SFT, causing fine-tuned models to lose general reasoning, safety filters, or instruction adherence.
Topics & Action Items (5)
01
Decision Matrix: When to Prompt, RAG, or Fine-Tune
3 hrs02
Parameter-Efficient Fine-Tuning (PEFT): LoRA & QLoRA
6 hrs03
SFT Data Curation, Cleaning & Synthetic Data Generation
4 hrs04
Preference Alignment: Direct Preference Optimization (DPO)
5 hrs05
High-Performance Fine-Tuning with Unsloth & Axolotl
4 hrs