Models & Architecture

Explainers, primers, and context for complex AI topics.

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Models & Architecture

ComparisonsModel Parameters vs. Model WeightsModel parameters are all the fitted values that determine a model’s behavior; model weights are either one type of parameter or an informal name for the model’s learned values as a whole. In a layer equation, “weight” usually has the narrower meaning. In a phrase such as “download the model weights,” it usually has the broader one.
ConceptsWhat Are Model Parameters?Model parameters are the stored numerical values inside a fitted model that determine how it transforms an input into an output. In a neural network, they usually include weights and biases whose values are chosen during training and reused when the model makes predictions.
ConceptsWhat Are Model Weights?Model weights are learned numbers that determine how strongly a model uses each input or intermediate signal when computing an output. They are usually stored in large groups called matrices or tensors, and training changes them so the model's outputs better fit its objective.
ConceptsWhat Is a Foundation Model?A foundation model is a machine-learning model trained on broad data so it can be adapted for many different tasks, rather than built for just one. It is a reusable starting point, not a finished application.
ConceptsWhat Is a Neural Network?A neural network is a machine-learning model that learns to turn input numbers into useful output numbers by passing them through connected layers of simple calculations. Those layers form one trainable mathematical function, not a literal electronic brain.
ConceptsWhat Is an AI Model?An AI model is the part of an AI system that transforms an input into an output, such as a prediction, classification, recommendation, action, or generated result. In most current usage, it means a machine-learning model whose behavior was shaped by training data.
ConceptsWhat Is an LLM?A large language model (LLM) is an AI model trained on large collections of language data to process or generate likely sequences of text. Most LLMs used for text generation take a prompt and produce a response one small piece at a time.