Trainer: Reusing the Contract for Fine-Tuning

Generation uses a model package to produce outputs; fine-tuning changes the package’s learned weights for a task or domain. This chapter follows how Transformers’ Trainer reuses the same model, preprocessing, and task-head contracts for a PyTorch training and evaluation loop. The practical goal is to see what Trainer automates, what a valid training input must contain, and which choices remain scientific decisions rather than library defaults.

Fine-tuning does not begin with an empty architecture. It starts from a checkpoint whose representation and often some task behavior have already been learned. The training system must preserve the input conventions that the checkpoint understands while adding labels, a loss, an optimizer, evaluation, and a way to save the updated result.

From a checkpoint to a training step

A minimal Trainer setup needs more than a model name. It needs a model with a task head, a dataset, a processing class such as a tokenizer or processor, a data collator, and training arguments. The processing class turns raw examples into model inputs. The collator assembles individual examples into a batch, handling padding or other batch-level preparation. The model consumes the batch and returns outputs from which a loss can be computed against labels.

The task head is the first important gate. A representation-only backbone may produce hidden states but not accept labels or compute the objective needed for a supervised task. Trainer checks for this mismatch and directs the user toward a model class with a suitable head. This prevents a common error: assuming that because a backbone can run a forward pass, it can automatically learn the requested task.

The dataset must also be shaped for the model. A text column is not automatically a tensor input. Tokenization produces fields such as input_ids and attention_mask; labels must be created with the correct meaning and alignment; unused raw columns may need to be removed before the forward pass. For vision, audio, or multimodal tasks, the processing class performs the equivalent conversion for those modalities. The model’s input contract remains the same as in inference, but training adds targets and a loss relationship.

flowchart LR A["Dataset"] --> B["Processing class"] B --> C["Data collator"] C --> D["Task model"] C --> E["Labels"] D --> F["Loss"] E --> F F --> G["Optimizer and update"] G --> H["Evaluation and save"] H --> I["Reloadable checkpoint"]

The processing class is saved with the model because it is part of the result, not merely a temporary input utility. A fine-tuned checkpoint without the tokenizer or processor that shaped its training examples is harder to reuse correctly. The updated task head, configuration, generation settings when relevant, and preprocessing assets together describe what the new artifact expects.

Trainer coordinates the repeated mechanics around this path. It can establish devices and distributed settings, select or construct an optimizer and scheduler, run training and evaluation steps, invoke callbacks, log metrics, save checkpoints, and optionally initialize Hub sharing. It also inspects the model’s forward signature and label behavior so it can connect dataset columns to the computation.