Techniques to Improve Model Output (20%)
Fine-tuning Approaches
KEY CONCEPTS
- →Supervised fine-tuning: Task-specific adaptation
- →RLHF: Reinforcement Learning from Human Feedback
- →LoRA and parameter-efficient methods
- →Continued pre-training vs fine-tuning
- →When NOT to fine-tune
WHAT THE EXAM IS REALLY TESTING
Fine-tuning is expensive and slow. Exam tests whether you recognize when simpler approaches (prompting, RAG) should be tried first.
COMMON TRAPS
- !Jumping to fine-tuning before trying prompting
- !Confusing fine-tuning with continued pre-training
- !Not considering data requirements for fine-tuning
OFFICIAL DOCUMENTATION
PRACTICE QUESTIONS
4 questions available for this topic
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