Frequency Penalty is between -2.0 and 2.0 and it impacts how the Omni AI penalizes new tokens based on their existing frequency in the text.
Positive values will decrease the likelihood of the AI repeating the same line verbatim by penalizing new tokens that have already been used frequently.
The presence penalty is a one-time, additive contribution that applies to all tokens that have been sampled at least once, while the frequency penalty is a contribution that is proportional to how often a specific token has already been sampled.
For the purpose of slightly reducing repetitive samples, reasonable values for the penalty coefficients are typically around 0.1 to 1.
If the goal is to significantly suppress repetition, the coefficients can be increased up to 2, but this may negatively impact the quality of the samples.
Alternatively, using negative values can increase the likelihood of repetition.