Weights
The model is full of numbers called weights
Every connection between neurons has a weight. Change the weights below and watch the output change live. Click "Run forward pass" to see values flow through the network.
This is the basic formula: <span class="mono">input × weight = contribution</span>. Contributions from many inputs are summed to form a neuron's activation.
In more detail
A weight is a number that controls how strongly one value influences another. In a single artificial neuron, each input is multiplied by its weight, the results are added together, a bias (another learned number) is added, and the total is passed through an activation function:
output = f(w₁x₁ + w₂x₂ + … + b)
A large positive weight means the input pushes the result up, a negative weight pushes it down, and a weight near zero means the input barely matters. Looking at one weight tells you almost nothing. The behavior of a network emerges from millions or billions of weights working together.
Weights are the main kind of parameter that a model learns. Training adjusts them in many small steps so that the model's predictions improve. After training, the weights are fixed: they do not change while you chat with the model.
Weights are not facts stored as sentences. Knowledge in a network is spread across many weights, and there is no simple table where a fact can be looked up.
Simplification: the visualization shows one multiplication (input × weight). A full neuron also sums many such products, adds a bias and applies an activation function.