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machine-learning/Advanced AI Engineering

LoRA explained — low-rank decomposition and why it works

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Module 1 · Task 1 of 10

Fine-Tuning Fundamentals

01LoRA explained — low-rank decomposition and why it works
02Full fine-tune vs LoRA vs QLoRA — memory, time, quality tradeoffs
03The SFT pipeline — dataset format, training config, evaluation
04DPO — direct preference optimization without a reward model
05Quantization — int8, int4, GPTQ, AWQ, bitsandbytes
06Serving fine-tuned models — vLLM, TGI, Ollama comparison
07Evaluating fine-tuned models — MT-Bench, Alpaca Eval, custom evals
08RLHF overview — reward model, PPO loop, why it's hard
09Project · Fine-tune and deploy a domain-adapted model
10Quiz me — Fine-Tuning Fundamentals

© 2026 Capstok · learn by doing

Mission About Pricing MCP Contact Feedback Terms Privacy