The number of learned statistical parameters, or weights, in a machine-learning model.
Trainable parameter count measures the number of statistical weights in a machine-learning model. Each weight contributes part of the model’s statistical behavior. section 2.5.1
Use parameter count as a rough comparison and cost signal:
Larger models are described as likely to perform better, though parameter count alone does not establish model quality. section 2.5.1
Higher counts generally imply greater running expense, which may increase user costs. section 2.5.1
Treat prominent counts such as 175 billion or 1 trillion as model attributes, not sufficient evidence of value. section 2.5.1
How Software Testing with Generative AI covers it
How deep it goes
Defines parameters as statistical weights and connects their quantity to performance and cost, stopping short of model internals.
Examples
Uses 175-billion-parameter and 1-trillion-parameter language models as comparison examples.
What it assumes
Introduces statistical weights directly and does not require prior knowledge of machine-learning internals.
What you can do after
Evaluate/understand model scale as a rough performance and cost indicator without treating it as proof of quality.
How it connects
Connects parameter count with large language models, training data, model performance, and inference cost.
Deep Learning with R, Third EditionModel size is the amount of structural and parameter capacity in a machine learning model, determined by features such as layer and unit counts.
parametermeasures · A parameter is a value or variable that specifies or controls a characteristic, computation, or behavior of a function, model, operation, or request.
computeproduces · Compute is the processing capacity and computational resources available to or consumed by a system when executing workloads.
generalizationcontrasts with · Generalization is the extension of a pattern, model, solution, or conclusion beyond the specific examples or cases from which it was derived.
This explainer was generated by Manning's knowledge graph from the text of Software Testing with Generative AI. The section links point to the parts of the book it draws on.
