Understanding LLM hyperparameters like temperature, Top-k, Top-p, frequency, and presence penalties is essential for effective prompt engineering. Temperature controls output randomness; Top-k sampling limits next-word choices to the top probabilities, while Top-p focuses on cumulative probabilities. Frequency and presence penalties prevent repetition to promote diversity in model responses.

8m read timeFrom towardsdatascience.com
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A visual explanation of LLM hyperparametersLLMs under the hood

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