In part 3 of Generative AI Foundations on AWS, AWS generative AI expert instructor Emily Webber explains the benefits of using a pre-trained foundation model as well as how to fine tune it to fit your use case.
You’ll learn about different types of prompt engineering, such as zero-shot, single-shot, few-shot, and learn how to apply these for various NLP use cases like summarization, classification, and translation. Then, you’ll move on to fine-tuning a pre-trained model using classic fine-tuning, parameter efficient fine tuning, and finally how to access Hugging Face’s new library. Go hands-on by fine-tuning GPT-J 6B with SageMaker Jumpstart on SEC filing data using this GitHub resource: https://go.aws/3DjMjFq
Learn more about generative AI on AWS: https://go.aws/44rbDVG
Tune in to Build On Generative AI with host Emily Webber on twitch.tv/aws for even more tips and tricks: https://m.twitch.tv/videos/1723458659
Access the slides from this lesson to follow along: https://github.com/aws-samples/sagemaker-distributed-training-workshop/blob/main/slides/Generative%20AI%20Foundations%20Technical%20Deep%20Dive/1%20-%20Intro%20to%20FMs.pdf.zip
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