Parameters are the adjustable numerical values inside a neural network. Weights are the values that determine how signals are transformed as information moves through the model. In everyday discussion the terms are often used closely together, although a model can also contain other learned parameters such as biases.
During training, optimization repeatedly adjusts model parameters to reduce error on the training objective. The resulting parameter values encode statistical patterns learned from data; they are not a simple database of sentences.
Not automatically. Performance also depends on architecture, training data, data quality, compute, optimization, post-training, tools, inference strategy and the task being measured. A smaller specialized model can outperform a larger model on a particular job.
Parameter count alone does not tell you factual accuracy, safety, cost, latency, privacy, tool competence or suitability for your use case.
Choose models from the job and constraints backward: task quality, evidence needs, privacy, latency, cost, deployment environment, resource burden, tool use, failure modes and recovery.
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