10 questions · need 7/10 to pass.
Q1.For "How Do You Know Your Fine-Tune Actually Worked?", which detail or constraint from the module is accurate?
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Q2.Which fact about "Teach Preferences Without a Reward Model (DPO)" matches the mechanism the module covered?
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Q3.Which statement about how "Fine-Tune Big Models on Small GPUs (LoRA)" actually works is correct?
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Q4.Which statement about how "Serve Your Model to Real Users (vLLM, TGI, Ollama)" actually works is correct?
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Q5.When applying "Teach a Base Model to Follow Instructions (SFT)" in practice, which of these holds?
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Q6.When applying "How Chat Models Learn From Human Feedback (RLHF)" in practice, which of these holds?
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Q7.For "Which Fine-Tuning Method Fits Your GPU? (Full, LoRA, QLoRA)", which detail or constraint from the module is accurate?
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Q8.Which of these correctly identifies the role of "Fine-Tune Big Models on Small GPUs (LoRA)" in the broader system?
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Q9.Which definition of "Which Fine-Tuning Method Fits Your GPU? (Full, LoRA, QLoRA)" matches what the module established?
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Q10."Shrink a Model to a Quarter of Its Size (Quantization)" — which of these claims is supported by the module?
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