Example notebooks
For step-by-step examples on how to use publicly available JumpStart foundation models with the SageMaker Python SDK, refer to the following notebooks on text generation, image generation, and model customization.
Note
Proprietary and publicly available JumpStart foundation models have different SageMaker AI Python SDK deployment workflows. Discover proprietary foundation model example notebooks through Amazon SageMaker Studio Classic or the SageMaker AI console. For more information, see JumpStart foundation model usage.
You can clone the Amazon SageMaker AI examples repository
Time series forecasting
You can use the Chronos models to forecast time series data. They're based on the
language model architecture. Use the Introduction to SageMaker AI JumpStart - Time Series Forecasting with Chronos
For information about the available Chronos models, see Available foundation models.
Text generation
Explore text generation example notebooks, including guidance on general text generation workflows, multilingual text classification, real-time batch inference, few-shot learning, chatbot interactions, and more.
Image generation
Get started with text-to-image Stable Diffusion models, learn how to deploy an inpainting model, and experiment with a simple workflow to generate images of your dog.
Model customization
Sometimes your use case requires greater foundation model customization for specific tasks. For more information on model customization approaches, see Foundation model customization or explore one of the following example notebooks.
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SageMaker JumpStart Foundation Models - HuggingFace Text2Text Instruction Fine-Tuning
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Retrieval-Augmented Generation: Question Answering using LLama-2, Pinecone and Custom Dataset
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Retrieval-Augmented Generation: Question Answering based on Custom Dataset
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Retrieval-Augmented Generation: Question Answering using Llama-2 and Text Embedding Models
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Amazon SageMaker JumpStart - Text Embedding and Sentence Similarity
