View source for Large Language Model Configuration Parameter

A [[Large Language Model Configuration Parameter]] is a [[parameter]] that controls the [[text generation process]] of a [[large language model]] during [[inference phase]].
* <B>AKA:</B> [[Large Language Model Configuration Parameter|LLM Hyperparameter]], [[Large Language Model Configuration Parameter|LLM Setting]], [[Large Language Model Configuration Parameter|LLM Control Variable]].
* <B>Context:</B>
** It can control the randomness of the model's output through the [[temperature setting]], where higher values result in more creative responses and lower values lead to more [[deterministic output]]s.
** It can influence the diversity of [[generated text]] by adjusting the [[top-k sampling]] parameter, which limits the model to selecting from the top k probable next tokens.
** It can affect response variability using the [[top-p sampling]] (nucleus sampling) parameter, which allows the model to consider tokens with a cumulative probability mass up to p.
** It can set constraints on the length of the generated output by specifying the [[max tokens]] parameter.
** It can penalize repetitive phrases by configuring the [[frequency penalty]] parameter.
** It can encourage the inclusion of new topics by setting the [[presence penalty]] parameter.
** It can integrate external tools or functions into the model's responses through the [[function calling]] parameter.
** It can range from being a simple adjustment like setting the temperature to being a complex configuration involving multiple parameters.
** It can range from being a general-purpose setting to a task-specific configuration for specialized outputs.
** ...
* <B>Example(s):</B>
** [[Temperature Setting (LLM) Parameter]], which controls the randomness and creativity of the model’s output.
** [[Top-K Sampling Parameter]], which limits the model's token selection to the top k probable choices.
** [[Top-P Sampling Parameter]], which uses cumulative probability to select a subset of output tokens.
** [[Max Tokens Parameter]], which restricts the length of generated output.
** [[Frequency Penalty Parameter]], which discourages repetition.
** [[Presence Penalty Parameter]], which encourages topic diversity.
** [[Function Calling Parameter]], which enables the model to invoke external tools or APIs during generation.
** ...
* <B>Counter-Example(s):</B>
** [[Static Model Parameter]]s, which are not configurable at runtime.
** [[Training Hyperparameter]]s, which are fixed during training and not used to configure output behavior.
** [[Non-Configurable Parameter]]s, which cannot be adjusted by the user or developer to control generation behavior.
** [[Model Weight]]s, which are learned during training rather than configured during inference.
** [[Training Hyperparameter]]s, such as [[learning rate]] or [[batch size]], which affect model training rather than inference.
** [[Architecture Component]]s like [[transformer layer]]s, which define structural capabilities rather than runtime behavior.
* <B>See:</B> [[Writing Parameter]], [[Automated Text Generation Task]], [[Natural Language Processing]], [[Prompt Engineering]], [[Model Fine-Tuning]], [[Softmax Layer]], [[Tokenization Process]], [[Inference Optimization]], [[Transformer Architecture]], [[Attention Head]]s, [[Context Window]], [[Dropout Rate]], [[Parameter-Efficient Fine-Tuning (PEFT)]].

----
----