模型微调与适配

题集来源:AMIT SHEKHAR(amitshekhariitbhu)收录项目:ai-engineering-interview-questions查看原题集

题目目录

32 道原题
  1. What is fine-tuning, and when should you fine-tune an LLM?

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  2. Explain the difference between full fine-tuning and parameter-efficient fine-tuning (PEFT).

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  3. What is LoRA (Low-Rank Adaptation), and how does it work?

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  4. What is QLoRA, and how does it enable fine-tuning on consumer hardware?

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  5. How does fine-tuning work?

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  6. Explain Prefix Tuning and Prompt Tuning. How are they different from LoRA?

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  7. What is adapter-based fine-tuning?

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  8. Explain the difference between pre-training, supervised fine-tuning (SFT), and preference optimization (RLHF/DPO).

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  9. What is RLHF (Reinforcement Learning from Human Feedback), and how is it used to align LLMs?

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  10. What is Deep RL from Human Preferences, the paper that started RLHF?

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