transformers and trl provide Trainer and SFTTrainer for training transformer models. They are used for different purposes.

Trainer

The Trainer class offers an API for training transformer models using the TrainingArguments class, which provides various options to customize the training process.

It is ideal for general-purpose training of models from scratch and usually requires large datasets for effective training.

It also manages complex training tasks like checkpointing, gradient accumulation, and distributed training in the background.

SFTTrainer

It offers an easy-to-use API to train models with just a few lines of code using the SFTConfig class. It automates many tasks, letting you write training code with less effort.

The SFTTrainer class is optimized for fine-tuning pre-trained models with smaller datasets.

It includes built-in support for PEFT and packing optimizations to reduce memory use during training.

The SFTTrainer and SFTConfig classes are essentially subclasses of the Trainer and TrainingArguments classes, so the same options can be used.

Which one to choose?

• Trainer: Your goal is to train a model from scratch with large datasets and needs extensive customization for your training.

• SFTTrainer: Your goal is to fine-tuning a pre-trained model with smaller datasets and want a simple implementation of training code.