Using Spectrum fine-tuning to improve FM training efficiency on Amazon SageMaker AI

Optimizing generative AI applications relies on tailoring foundation models (FMs) using techniques such as prompt engineering, RAG, continued pre-training, and fine-tuning. Efficient fine-tuning is achieved by strategically managing hardware, training time, data volume, and model quality to reduce resource demands and maximize value.

Spectrum is a new approach designed to pinpoint the most informative layers within a foundation model (FM). Using this method, you can selectively fine-tune just a portion of the model, thereby enhancing training efficiency. Recently, several methods have been developed to fine-tune language models more efficiently, reducing both computational resources and time. One widely used technique is Quantized LoRA (QLoRA) which combines low rank adaptation (LoRA) with quantization of the original model for training. This method yields impressive outcomes, only slightly inferior to full fine-tuning, while using just a fraction of GPU resources. However, QLoRA applies low-rank adaptation uniformly throughout the entire model.

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