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LLM Tuning Parameters

LLM Tuning Parameters

Tuning a Large language model, there are several key parameters you can adjust to optimize performance. These parameters often depend on the specific LLM architecture and the tasks you’re using the model for.

Max Length

This setting allows you to control how many tokens the model produces.

Temperature

Temperature setting controls the randomness of predictions. A higher temperature leads to more random outputs, while a lower temperature makes outputs more deterministic.

Top-k Sampling (Nucleus Sampling)

This controls how many possible next tokens are considered during generation. Adjusting top-k or top-p can influence creativity and coherence in text generation.

Presence Penalty

This setting is a penalty for repeated tokens, thus preventing excessive repetition of the word.

Frequency Penalty

This setting reduces word repetition in the LLM model response. The likelihood of the word decreases with increasing this parameter.

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