Full Explanation
A model is a learned structure that predicts what comes next. The training process — analyzing massive amounts of text data — doesn't stay "alive" inside the model. It collapses and crystallizes into a single artifact: a file full of numbers called weights. These weights represent how strongly different connections in the neural network are associated with each other.
The model is not a database. It doesn't store sentences or facts. It learns how tokens usually follow each other — patterns, not knowledge.
This becomes concrete with open-source models. If you download a model file and disconnect from the internet, the model still works. Everything it learned is already crystallized inside those numbers.
Once the file exists, copying it is much easier than training it. That's why model security is taken seriously by labs like OpenAI, Google, and others. But the core insight remains: the model is just a file. A large, carefully trained file — but a file.
