What are Parameters in an LLM?

Parameters are the internal numerical values, weights, that a neural network learns during training and uses to transform input into output.

• Modern LLMs commonly have anywhere from a few billion to hundreds of billions of parameters
• Every parameter is adjusted incrementally during training to reduce the model's loss on its training data
• Parameter count is a common, though imperfect, shorthand for a model's raw capacity, more parameters generally means more capacity to learn complex patterns

Parameter count isn't the whole story though, training data quality, architecture choices, and fine-tuning all meaningfully affect how capable a model actually is, independent of raw size.

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