

Model classes can be PyTorch models (torch.nn.Module), Keras models (tf.keras.Model) or JAX/Flax models (flax.linen.Module) that work with the pretrained weights provided in the library.
Configuration classes store the hyperparameters required to build a model (such as the number of layers and hidden size). You don't always need to instantiate these yourself. In particular, if you are using a pretrained model without any modification, creating the model will automatically take care of instantiating the configuration (which is part of the model).
Preprocessing classes convert the raw data into a format accepted by the model. A tokenizer stores the vocabulary for each model and provide methods for encoding and decoding strings in a list of token embedding indices to be fed to a model. Image processors preprocess vision inputs, feature extractors preprocess audio inputs, and a processor handles multimodal inputs.