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Fine-Tuning, LoRA & Alignment (RLHF / DPO)
PEFT, Low-Rank Adaptation, Instruction Tuning, and Direct Preference Optimization
While prompting adapts in-context behavior, Fine-Tuning updates the internal model weights for specialized domain vocabularies, style consistency, and safety alignment.
Parameter-Efficient Fine-Tuning (PEFT) & LoRA
Full Parameter Fine-Tuning: Modifying all 70B weights in a model requires immense GPU VRAM and risks catastrophic forgetting.
LoRA (Low-Rank Adaptation): Freezes base model weights and injects trainable rank-decomposition matrices into Transformer attention layers. This reduces trainable parameters by over 99% while achieving comparable task performance.
Alignment (RLHF & DPO): Pretrained base models are raw text completion engines. Instruction tuning and Direct Preference Optimization (DPO) steer them to act as respectful, helpful, and safe chat assistants.
Use RAG when you need dynamic, up-to-date knowledge or source attribution. Use Fine-Tuning when you need to teach a specific tone, format, syntax, or specialized terminology.
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Test Yourself With Exercise
Why is LoRA (Low-Rank Adaptation) preferred over full-parameter fine-tuning for large language models?