Wikis
Info-nuggets to help anyone understand various concepts of MLOps, their significance, and how they are managed throughout the ML lifecycle.
Top-k
Constrains the model's predictions during the generation process to the top-k most probable tokens at each step
The top-k parameter constrains the model's predictions during the generation process to the top-k most probable tokens at each step. When a value is set for 'k', the model focuses its attention on only the k tokens with the highest probabilities, discarding less likely options. This instruction allows for fine-tuning the generated output, ensuring that it aligns with specific patterns or constraints defined by the user. By controlling the set of tokens considered at each generation step, the top-k parameter provides a way to balance precision and diversity in the generated sequences.
The top-k parameter offers several advantages, including controlled diversity, sharper distributions, and reduced randomness in the generated output.
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