Instantiate a Transformers model (TensorFlow)

Instantiating Transformers models involves utilizing the `TFAutoModel` and `AutoConfig` classes to streamline the loading of architectures and weights from the Hugging Face Hub. The `TFAutoModel` API automatically identifies the correct model class—such as BERT, GPT-2, or BART—by first reading a configuration file that serves as a structural blueprint. While `from_pretrained` loads a model with optimized weights for immediate use, developers can also initialize models with random weights for training from scratch by passing a configuration object to the model class. This configuration is highly customizable, allowing for modifications like reducing the number of layers in a BERT architecture. Once a model is trained or fine-tuned, the `save_pretrained` method exports the necessary configuration and weight files to a local directory, ensuring they can be reloaded or shared seamlessly.