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# 示例笔记本
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有关如何在 SageMaker Python SDK 中使用公开 JumpStart 基础模型的 step-by-step示例，请参阅以下关于文本生成、图像生成和模型自定义的笔记本。

**注意**  
专有和公开 JumpStart 的基础模型具有不同的 SageMaker AI Python SDK 部署工作流程。通过 Amazon SageMaker Studio Classic 或 A SageMaker I 控制台探索专有的基础模型示例笔记本电脑。有关更多信息，请参阅 [JumpStart 基础模型用法](jumpstart-foundation-models-use.md)。

您可以克隆 [Amazon A SageMaker I 示例存储库](https://github.com/aws/amazon-sagemaker-examples/tree/main/introduction_to_amazon_algorithms/jumpstart-foundation-models)，以便在 Studio 中您选择的 Jupyter 环境中运行可用的 JumpStart 基础模型示例。有关可用于在 SageMaker AI 中创建和访问 Jupyter 的应用程序的更多信息，请参阅。[Amazon SageMaker Studio 支持的应用程序](studio-updated-apps.md)

## 时间序列预测
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您可以使用 Chronos 模型来预测时间序列数据。它们基于语言模型架构。使用 [Chronos 笔记本上的时间序列预测简介](https://github.com/aws/amazon-sagemaker-examples/blob/default/%20%20%20%20generative_ai/sm-jumpstart_time_series_forecasting.ipynb)开始使用。 SageMaker JumpStart 

有关可用的 Chronos 更多信息，请参阅 [可用的基础模型](jumpstart-foundation-models-latest.md)。

## 文本生成
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探索文本生成示例笔记本，包括一般文本生成工作流、多语言文本分类、实时批量推理、少样本学习、聊天机器人交互等方面的指导。
+ [SageMaker JumpStart 基础模型——以 FLAN-T5 XL 为例生成 HuggingFace Text2Text](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/text2text-generation-flan-t5.html)
+ [SageMaker JumpStart 基础模型——BloomZ：多语言文本分类、问答、代码生成、段落改写等](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/text2text-generation-bloomz.html)
+ [SageMaker JumpStart 基础模型- HuggingFace Text2Text 生成批量转换和实时批量推理](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/text2text-generation-Batch-Transform.html)
+ [SageMaker JumpStart 基础模型——GPT-J、GPT-neo Few-shot Learning](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-few-shot-learning.html)
+ [SageMaker JumpStart 基础模型-聊天机器人](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-chatbot.html)
+ [简介 SageMaker JumpStart -使用 Mistral 模型生成文本](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/mistral-7b-instruction-domain-adaptation-finetuning.html)
+ [简介 SageMaker JumpStart -使用 Falcon 模型生成文本](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/falcon-7b-instruction-domain-adaptation-finetuning.html)

## 图像生成
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开始使用 text-to-image稳定扩散模型，学习如何部署修复模型，并尝试使用简单的工作流程来生成狗的图像。
+ [简介 JumpStart -文字转图像](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart_text_to_image/Amazon_JumpStart_Text_To_Image.html)
+ [ JumpStart 图像编辑简介-稳定扩散修复术](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart_inpainting/Amazon_JumpStart_Inpainting.html)
+ [为您的狗生成有趣的图片](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart_text_to_image/custom_dog_image_generator.html)

## 模型自定义
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有时，您的使用案例需要针对特定任务进行更多的基础模型自定义。有关模型自定义方法的更多信息，请参阅[基础模型自定义](jumpstart-foundation-models-customize.md)或浏览以下示例笔记本之一。
+ [SageMaker JumpStart 基础模型——微调特定领域数据集上的文本生成 GPT-J 6B 模型](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/domain-adaption-finetuning-gpt-j-6b.html)
+ [SageMaker JumpStart 基础模型- HuggingFace Text2Text 指令微调](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/instruction-fine-tuning-flan-t5.html)
+ [检索增强生成：使用 LangChain 和 Cohere 的生成和嵌入模型进行问答 SageMaker JumpStart](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_Cohere+langchain_jumpstart.html)
+ [检索增强生成：使用- LLama 2、Pinecone 和自定义数据集回答问题](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_pinecone_llama-2_jumpstart.html)
+ [检索增强生成：基于开源库的自定义数据集问答 LangChain ](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_langchain_jumpstart.html)
+ [检索增强生成：基于自定义数据集的问题回答](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_jumpstart_knn.html)
+ [检索增强生成：使用 Llama-2 和文本嵌入模型进行问题解答](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_text_embedding_llama-2_jumpstart.html)
+ [Amazon SageMaker JumpStart -文本嵌入和句子相似度](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/text-embedding-sentence-similarity.html)