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FFT - Amazon SageMaker AI
When to use FFTKey differences from LoRA
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

FFT

FFT (Full Fine-Tuning) updates all model weights during training. This provides the deepest level of customization but requires more compute resources and longer training time.

When to use FFT

  • Large domain shifts where the model needs fundamental knowledge changes

  • You have a large, high-quality dataset (small datasets risk catastrophic forgetting)

  • You want no adapter overhead at inference time

  • Maximum customization depth is required

Key differences from LoRA

  • No lora_rank, lora_alpha, or merge_weights parameters

  • Adds max_response_length parameter for controlling generated output length

  • Higher compute cost and longer training time

  • Larger output artifacts (full model weights)

  • Not compatible with continuous customization

For the full parameter reference including FFT-specific hyperparameters, see each technique page: SFT, DPO, RFT.