Large language models (LLMs) have shown success in NLP but need customization to adapt to specific tasks or domains. This post explores how Amazon SageMaker and MLflow can simplify the process of fine-tuning LLMs at scale using SageMaker Pipelines. By integrating MLflow, you can manage experiment tracking, model versioning, and deployment, enabling easier comparison of multiple LLM experiments. The post provides a step-by-step guide and source code to streamline fine-tuning, evaluation, and deployment of models like Llama 3 using SageMaker and MLflow.
Table of contents
LLM selection and fine-tuning journeysSolution overviewPrerequisitesSet up an MLflow tracking serverOverview of SageMaker Pipelines for experimentation at scaleLog datasets with MLflowFine-tune a Llama model with LoRA and MLflowEvaluate the modelCreate the pipelineCompare experiment resultsRegister the modelDeploy the modelClean upConclusionSort: