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Top -k vs Top -P

 Top-k Sampling:

  • It's a sampling technique used to control the randomness of text generation.
  • Top-k limits the model's choices to the k tokens with the highest probability.
  • For example, setting k to 3 restricts the model to choosing from the top three most probable tokens.
  • The model selects from these options using probability weighting.
  • This method adds some randomness while preventing highly improbable completions.
  • Result: Text generation is more likely to sound reasonable and coherent.

Top-p Sampling:

  • Another sampling technique to limit randomness in text generation.
  • Top-p restricts the model to predictions whose combined probabilities do not exceed p.
  • For example, setting p to 0.3 includes tokens with probabilities adding up to 0.3 (e.g., cake and donut with probabilities of 0.2 and 0.1).
  • The model uses random probability weighting to choose from these tokens.
  • Result: Allows control over text generation by specifying the total probability the model should choose from.

These two sampling techniques provide different ways to fine-tune text generation and strike a balance between randomness and coherence in the generated text. Top-k limits the number of choices, while top-p restricts the cumulative probability of options. The choice between them depends on the desired output and the specific use case.

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