大语言模型

该存储库包含用于编码、预训练和微调类似GPT的LLM的代码，是《构建大型语言模型（从头开始）》一书的官方代码存储库。

（如果您从Manning网站下载了代码包，请考虑访问GitHub上的官方代码库，网址为https://github.com/rasbt/LLMs-from-scratch.)

For the past 6 months, I’ve been working on LLM-powered applications using GPT and other AI-as-a-Service providers. Along the way, I produced a set of illustrations to help visualize and explain some general architectural concepts.

Below is the first batch, I’m hoping to add more later on.

Building LLM-powered Applications

The past few weeks have been exciting for developers interested in deploying AI-powered applications. The field is evolving quickly, and it is now possible to build AI-powered applications without having to spend months or years learning the ins and outs of machine learning. This opens up a whole new world of possibilities, as developers can now experiment with AI in ways that were never before possible.

How can you leverage the capabilities of Large Language Models (LLMs) within your software applications?

You cannot simply create a thin application layer above an LLM API. Instead you need to design and build a number of components to ‘tame’ the underlying models and also to differentiate your product.

🚀 People ask me what it takes to build a scalable Large Language Model (LLM) app in 2023. When we talk about scalable we mean 100s of users and millisecond latency. Let me share some of our learning experience with you.

Large language models are a powerful new primitive for building software. But since they are so new—and behave so differently from normal computing resources—it’s not always obvious how to use them.