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Building LLM Powered  Applications

You're reading from   Building LLM Powered Applications Create intelligent apps and agents with large language models

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835462317
Length 342 pages
Edition 1st Edition
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Valentina Alto Valentina Alto
Author Profile Icon Valentina Alto
Valentina Alto
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Table of Contents (16) Chapters Close

Preface 1. Introduction to Large Language Models FREE CHAPTER 2. LLMs for AI-Powered Applications 3. Choosing an LLM for Your Application 4. Prompt Engineering 5. Embedding LLMs within Your Applications 6. Building Conversational Applications 7. Search and Recommendation Engines with LLMs 8. Using LLMs with Structured Data 9. Working with Code 10. Building Multimodal Applications with LLMs 11. Fine-Tuning Large Language Models 12. Responsible AI 13. Emerging Trends and Innovations 14. Other Books You May Enjoy
15. Index

Start working with LLMs via Hugging Face Hub

Now that we got familiar with LangChain components, it is time to start using our LLMs. If you want to use open-source LLMs, leveraging the Hugging Face Hub integration is extremely versatile. In fact, with just one access token you can leverage all the open-source LLMs available in Hugging Face’s repos. As it being a non-production scenario, I will be using the free Inference API, however if you are meant to build production-ready applications, you can easily scale to the Inference Endpoint, which grants you a dedicated and fully managed infrastructure to host and consume your LLMs.So let’s see how to start integrating LangChain with Hugging Face Hub.

Create an Hugging Face user access token

To access the free Inference API, you will need an user access token, the credential thal allows you to run the service. Below you can find the steps to activate the user access token:

  • Create an Hugging Face account. You can create an Hugging...

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