The number of parameters reflects the amount of knowledge a model can represent or learn, representing the number of states or combinations the model can express in a specific domain.
Taking language models as an example, the larger the number of parameters, the more text data it sees, the richer the language knowledge it masters, and the more language combinations it can express, just like a person with an extremely large vocabulary can combine various fluent languages.
Moreover, when the scale of the model reaches a certain level, its performance often improves by leaps and bounds—this is known as 'emergent abilities,' which is one of the reasons why many models are made larger.
However, if a large model is used to address the needs in specific business marketing scenarios, the larger the macro parameter size, the more likely the large model is to lose focus.
The number of parameters reflects the amount of knowledge a model can represent or learn, representing the number of states or combinations the model can express in a specific domain.
Taking language models as an example, the larger the number of parameters, the more text data it sees, the richer the language knowledge it masters, and the more language combinations it can express, just like a person with an extremely large vocabulary can combine various fluent languages.
Moreover, when the scale of the model reaches a certain level, its performance often improves by leaps and bounds—this is known as 'emergent abilities,' which is one of the reasons why many models are made larger.
However, if a large model is used to address the needs in specific business marketing scenarios, the larger the macro parameter size, the more likely the large model is to lose focus.