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gemma-2-2b-it

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Hi @Lef00 It usually depends on what your building specifically your usecase. The standard recommendation is to use a temperature 1.0 to allow for moderate randomness and creativity, a Top_P of 0.95 to ensure the model considers a broad but not overly sparse set of probable tokens , and a Top_K of 64 to limit the sampling to the top 64 tokens at each step. These parameters are designed to produce high-quality, diverse, and contextually relevant responses, but for more deterministic or fact-intensive tasks, you might lower the temperature towards 0.2 and Top_P towards 0.8 to increase predictability.
Thank you