Quiz me — Fine-Tuning Fundamentals

Q1.Which of these correctly identifies the role of "LoRA explained — low-rank decomposition and why it works" in the broader system?

Q2.For "Full fine-tune vs LoRA vs QLoRA — memory, time, quality tradeoffs", which detail or constraint from the module is accurate?

Q3.Which fact about "DPO — direct preference optimization without a reward model" matches the mechanism the module covered?

Q4."Quantization — int8, int4, GPTQ, AWQ, bitsandbytes" — which of these claims is supported by the module?

Q5.When applying "The SFT pipeline — dataset format, training config, evaluation" in practice, which of these holds?

Q6.Which statement about how "Serving fine-tuned models — vLLM, TGI, Ollama comparison" actually works is correct?

Q7.Which statement about how "LoRA explained — low-rank decomposition and why it works" actually works is correct?

Q8.Which definition of "Full fine-tune vs LoRA vs QLoRA — memory, time, quality tradeoffs" matches what the module established?

Q9.When applying "RLHF overview — reward model, PPO loop, why it's hard" in practice, which of these holds?

Q10.For "Evaluating fine-tuned models — MT-Bench, Alpaca Eval, custom evals", which detail or constraint from the module is accurate?