Repetition Penalties › Key Parameters

When implementing repetition penalties in generation, these parameters control behavior:

• repetition_penalty (float, typically 1.0-2.0): Multiplicative penalty applied to logits of repeated tokens. A value of 1.0 applies no penalty. Values around 1.1-1.3 provide gentle repetition reduction. Values above 1.5 strongly discourage any repetition.

• frequency_penalty (float, typically 0-2.0): Additive penalty proportional to token occurrence count. Higher values increasingly penalize frequently used tokens. OpenAI's API uses this parameter with typical values of 0-1.0.

• presence_penalty (float, typically 0-2.0): Flat additive penalty for tokens that have appeared at all. Encourages vocabulary diversity. Also used in OpenAI's API with typical values of 0-1.0.

• no_repeat_ngram_size (int): Size of n-grams to block from repeating. In Hugging Face's generate(), setting this to 2 prevents any bigram repetition, 3 prevents trigram repetition, etc. Set to 0 to disable.

• encoder_no_repeat_ngram_size (int): For encoder-decoder models, prevents generating n-grams that appear in the encoder input. Useful for abstractive summarization to avoid copying source phrases.