Fine-tune an LLM: Why, when, and how
SMRTR summary
Fine-tuning allows developers to customize large language models for specific tasks, improving efficiency and performance. It involves training a model on curated examples to learn desired behaviors, formats, and tones. Benefits include reduced token usage, consistent outputs, and better handling of edge cases. Fine-tuning should be considered when prompts become unwieldy or costly.
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