Understand open weights
Weights are learned numbers
Understand model weights as the learned numerical state produced during training rather than source code or stored answers.
A language model contains a huge collection of numbers called parameters. Most of those parameters are weights.
Think of the model as an enormous audio mixing desk. Its architecture defines how the desk is wired. The weights are the positions of billions of small knobs.
Before training, those values are mostly random. The model cannot produce useful language yet.
During training, the model receives examples and tries to predict what comes next. Each error causes tiny adjustments to the weights. Repeating that process across a large dataset gradually produces a useful model.
The final weights do not contain a list of complete answers. They encode patterns that influence how an input is transformed and which token becomes likely next.
This is why a model can combine ideas in a way that does not match one training document. It is also why the model can invent a plausible answer. The weights produce likely continuations, not a database lookup with guaranteed facts.
When a model is called 1B, it has roughly one billion parameters. A 30B model has roughly 30 billion. Parameter count affects memory and compute, but it does not tell you whether the model is good at your task.
The weights are the model’s learned state. They are the result of training, not the training process itself.
Lesson completed