Step-by-Step Guide to Fine-Tuning Open Source LLMs in AWS Jumpstart

In the rapidly evolving landscape of artificial intelligence, foundation and huge language models (LLMs) have revolutionized how we approach natural language processing tasks. However, while these models are powerful, they often need to be fine-tuned to excel in specific domains or tasks. Fine-tuning LLMs allows you to adapt a pre-trained model to your unique dataset, enabling more accurate and relevant results. In this podcast, we’ll explore how to fine-tune open-source LLMs using AWS Jumpstart, focusing on two approaches: no-code fine-tuning via the SageMaker Studio UI and programmatic fine-tuning using the Jumpstart SDK.

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