Efficiently fine-tune the ESM-2 protein language model with Amazon SageMaker

This article discusses how to efficiently fine-tune the ESM-2 protein language model (pLM) using Amazon SageMaker to predict protein subcellular localization. Specifically, the article covers:

• An introduction to protein language models and their use in life sciences research
• Four methods to improve the efficiency of fine-tuning large models like ESM-2: weighted training classes, gradient accumulation, gradient checkpointing, and Low-Rank Adaptation (LoRA)
• How to prepare the training data and create a training script for SageMaker
• Submitting a SageMaker training job and comparing the results of different efficiency methods
• Conclusion highlighting the benefits of fine-tuning and efficient methods for adapting large language models