HandsOnLLM/Hands-On-Large-Language-Models

# 动手学大语言模型 欢迎！在这个仓库中，您将找到 [Jay Alammar](https://www.linkedin.com/in/jalammar/) 和 [Maarten Grootendorst](https://www.linkedin.com/in/mgrootendorst/) 撰写的《[动手学大语言模型](https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961)》一书所有示例的代码，我们戏称它为：

“图解 LLM 之书”

通过本书极具视觉教育意义的特点以及**近 300 张定制图表**，学习您当今使用大语言模型所需的实用工具和概念！
本书可在以下平台购买： * [Amazon](https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961) * [Shroff Publishers (印度)](https://www.shroffpublishers.com/books/computer-science/large-language-models/9789355425522/) * [O'Reilly](https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/) * [Kindle](https://www.amazon.com/Hands-Large-Language-Models-Alammar-ebook/dp/B0DGZ46G88/ref=tmm_kin_swatch_0?_encoding=UTF8&qid=&sr=) * [Barnes and Noble](https://www.barnesandnoble.com/w/hands-on-large-language-models-jay-alammar/1145185960) * [Goodreads](https://www.goodreads.com/book/show/210408850-hands-on-large-language-models) ## 目录 我们建议通过 Google Colab 运行所有示例，以获得最简单的配置体验。Google Colab 允许您免费使用具有 16GB VRAM 的 T4 GPU。所有示例主要都是在 Google Colab 上构建和测试的，因此它应该是运行最稳定的平台。不过，使用任何其他云服务提供商也应该没有问题。 | 章节 | Notebook | |---|---| | 第 1 章：语言模型简介 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter01/Chapter%201%20-%20Introduction%20to%20Language%20Models.ipynb) | | 第 2 章：Token 和 Embedding | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter02/Chapter%202%20-%20Tokens%20and%20Token%20Embeddings.ipynb) | | 第 3 章：深入 Transformer LLM 内部 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter03/Chapter%203%20-%20Looking%20Inside%20LLMs.ipynb) | | 第 4 章：文本分类 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter04/Chapter%204%20-%20Text%20Classification.ipynb) | | 第 5 章：文本聚类和主题建模 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter05/Chapter%205%20-%20Text%20Clustering%20and%20Topic%20Modeling.ipynb) | | 第 6 章：Prompt 工程 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter06/Chapter%206%20-%20Prompt%20Engineering.ipynb) | | 第 7 章：高级文本生成技术与工具 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter07/Chapter%207%20-%20Advanced%20Text%20Generation%20Techniques%20and%20Tools.ipynb) | | 第 8 章：语义搜索和检索增强生成 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter08/Chapter%208%20-%20Semantic%20Search.ipynb) | | 第 9 章：多模态大语言模型 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter09/Chapter%209%20-%20Multimodal%20Large%20Language%20Models.ipynb) | | 第 10 章：创建文本 Embedding 模型 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter10/Chapter%2010%20-%20Creating%20Text%20Embedding%20Models.ipynb) | | 第 11 章：为分类任务微调表示模型 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter11/Chapter%2011%20-%20Fine-Tuning%20BERT.ipynb) | | 第 12 章：微调生成模型 | [](https://colab.research.google.com/github/HandsOnLLM/Hands-On-Large-Language-Models/blob/main/chapter12/Chapter%2012%20-%20Fine-tuning%20Generation%20Models.ipynb) | ## 评价 ## [附赠内容！](bonus/) 我们试图在不令人感到信息过载的前提下，在书中尽可能多地提供信息。然而，即使是一本 400 页的书，依然有很多内容值得去探索！ 我们将继续创作更多对本书起到补充作用的指南，并更深入地探讨[令人兴奋的新主题]((bonus/))： ## 引用 如果您觉得本书对您的研究有用，请考虑引用本书： ``` @book{hands-on-llms-book, author = {Jay Alammar and Maarten Grootendorst}, title = {Hands-On Large Language Models}, publisher = {O'Reilly}, year = {2024}, isbn = {978-1098150969}, url = {https://www.oreilly.com/library/view/hands-on-large-language/9781098150952/}, github = {https://github.com/HandsOnLLM/Hands-On-Large-Language-Models} } ```

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