Training adjusts these values to reduce an objective, and checkpoints store them as tensors. Architecture code and configuration are also needed to interpret the tensors and run the model correctly.
Model weights are learned numeric parameters that determine how inputs are transformed during inference.
Training adjusts these values to reduce an objective, and checkpoints store them as tensors. Architecture code and configuration are also needed to interpret the tensors and run the model correctly.