LLM (Large Language Model) Top-P works
Use Cases Low Top-P value Use Case: Customer Support Why? High Top-P value Use Case: Creative writer Why?
Use Cases Low Top-P value Use Case: Customer Support Why? High Top-P value Use Case: Creative writer Why?
The LLM parameter Top-P, also known as nucleus sampling, controls the diversity of the output by setting a cumulative probability threshold for selecting the next token. It is used to produce higher quality and more diverse outputs depending on the setting. When generating text, tokens (words, sentences
With this setting you can choose the right balance between randomness and determinism of the outcome generated by the LLM. An important aspect in applications where decisions should be made based on profound facts. The other side, if you need more creativity
With LLM parameters you have the chance to configure additional important settings for your LLM. With that settings you can influence the balance of costs and value for instance. But in addition, you can influence how the output will be generated, is
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