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If you’re unfamiliar with how machine learning works: an AI model is truly just a very long list (billions, sometimes trillions) of numbers called weights, which, loosely, when multiplied and added together produce readable text. Training adjusts those numbers over and over until the model gives good answers. Before training can start, the weights need starting values and those are filled in with random numbers. Training changes most of the initial weights, but it doesn’t do so completely. Some are never touched at all and the rest still correlate with where they started. That is why the starting weights can still be read from the finished model. You can see it with a simple test: draw a smiley face into a network’s weights before training and it is still there afterwards.