Parameters are the internal values a language model learns during training. They encode the model's understanding of language, facts, and relationships — controlling how the model interprets inputs and generates outputs.
A model with more parameters can generally capture more complexity, but also requires more compute to run. The parameter count of a model is often used as a rough proxy for its capability, though the training approach, data quality, and fine-tuning significantly affect real-world performance independently of raw parameter count.
