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LLM Fine-Tuning: LoRA, QLORA, PEFT & RLHF
How to adapt pre-trained LLMs for specific tasks without catastrophic forgetting. Covers full fine-tuning vs PEFT, LoRA math and implementation, QLoRA for consumer hardware, instruction tuning, RLHF with PPO, DPO as the modern alternative, and when fine-tuning actually helps vs. when RAG or prompting is better.
Fine-Tuning Landscape — The Key Decision
Before fine-tuning, ask three questions: (1) Does the base model already do this with a good prompt? If yes, prompting is 10× cheaper and faster. (2) Do I need new factual knowledge? If yes, RAG is better — fine-tuning doesn't reliably inject facts (model hallucinates memorized but uncertain facts). (3) Do I need to change HOW the model responds (format, tone, style, domain-specific reasoning, task adherence)? If yes, fine-tuning is the right tool.
Fine-tuning changes model BEHAVIOR, not its knowledge base. Use it for: (a) instruction following in specific formats (always respond as JSON, follow specific clinical note templates), (b) domain vocabulary and reasoning patterns (medical, legal, code in a specific style), (c) alignment with human preferences (make it less verbose, avoid certain topics), (d) task-specific performance when prompting plateaus.