Based on your current model implementation (single linear layer with 1 weight and bias value) I doubt the model will be able to learn the dataset.
The increasing input numbers in [0, 10000] would only be multiplied with the weight value (thus scaled) and then the bias would be added (thus shifted). I don’t see how this operation can predict an oscillating target so you would need to change the model architecture (e.g. adding hidden layers etc.).