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Trainer

[Trainer] is a complete training and evaluation loop for Transformers models. You only need a model and dataset to get started.

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Underneath, [Trainer] handles batching, shuffling, and padding your dataset into tensors. The training loop runs the forward pass, calculates loss, backpropagates gradients, and updates weights. Configure the training run with [TrainingArguments] to customize everything from batch size and training duration to distributed strategies, compilation, and more.

Next steps

• Start with the fine-tuning tutorial for an introduction to training a large language model with [Trainer].
• Check the Subclassing Trainer methods guide for examples of how to subclass [Trainer] methods.
• See the Data collators guide to learn how to create a data collator for custom batch assembly.
• See the Callbacks guide to learn how to hook into training events.