Fine-tuning an LLM using QLoRA in AWS GovCloud (US)

This article describes how to fine-tune a large language model (LLM) using the Quantized Low Rank Adaptation (QLoRA) technique on the AWS GovCloud (US) Regions. It covers the following key points:

• Setting up an Amazon SageMaker notebook instance and preprocessing a dataset for instruction-based fine-tuning
• Launching a SageMaker training job using a Hugging Face container and QLoRA to efficiently adapt an LLM to a specific domain
• Key configurations for quantization and low-rank adapters to enable efficient fine-tuning and inference
• Merging the adapted weights into the base model for simplified deployment to an inference endpoint
• Relevant resources and next steps for deploying the fine-tuned model