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Efficient fine-tuning: LoRA and QLoRA

Fine-tuning: teaching an LLM new tricks without full retraining

GPT-4 can do a lot, but it does not know your internal workflows. BERT may miss your clinic’s medical jargon. Fine-tuning adapts a pretrained model to a task or domain. Full fine-tuning is expensive: LLaMA-7B needs multiple A100s. LoRA is elegant: freeze ~99% of weights and train only two small matrices. Quality is close to full FT; memory is often 10–50× smaller.

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