LLM Fine-Tuning: LoRA, QLoRA, and PEFT
Master parameter-efficient fine-tuning strategies: LoRA adapter weights, QLoRA quantization, and PEFT principles.
Fine-Tuning vs RAG
For interviews, understand when to use RAG vs Fine-Tuning:
• RAG: Best for updating knowledge, connecting external databases, minimizing hallucination, and providing source citations. Dynamic and easy to update. • Fine-Tuning: Best for teaching models a custom tone, style, specific output syntax (like JSON), or domain-specific language structures. Static and expensive to update.
PEFT and LoRA (Low-Rank Adaptation)
Full fine-tuning requires updating all billions of parameters in an LLM, which is incredibly slow and expensive.
PEFT (Parameter-Efficient Fine-Tuning) updates only a tiny fraction of parameters.
LoRA (Low-Rank Adaptation) freezes the pre-trained model weights and injects trainable rank decomposition matrices (A and B) into the attention layers. This drastically reduces the number of trainable parameters (e.g. by 99%), making fine-tuning possible on consumer GPUs.
QLoRA (Quantized LoRA) takes this further by quantizing the base model to 4-bit precision, minimizing RAM usage during training.