A practical walkthrough of LLM fine-tuning covering two examples: manually fine-tuning Mixtral 8x7B to mimic a personal writing style using notes and essays, and using PromptLayer to fine-tune GPT-3.5 as a cheaper workout generator trained on GPT-4-generated data. The guide covers data sourcing, formatting challenges, using LLMs to generate training pairs, and the iterative pain points of the manual approach versus the streamlined PromptLayer workflow. Concludes that fine-tuning is often more trouble than RAG for most use cases.
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