How to Fine-Tune an LLM: An End-to-End Guide
SMRTR summary
Fine-tuning a small 7B model on breast cancer reporting templates boosted accuracy from 35% to 98%, compared to a frontier model using lengthy prompts, saving an estimated $320,000 in API costs. Using QLoRA, a memory-efficient technique, smaller models can learn highly specific, complex tasks that system prompts and RAG simply cannot handle reliably.
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