LangChain - Using Hugging Face Models locally (code walkthrough) faq

learnersLearners: 42,538
instructor Instructor: Sam Witteveen instructor-icon
duration Duration: duration-icon

LangChain - Using Hugging Face Models locally (code walkthrough) - Take a deep dive into LangChain and learn how to utilize Hugging Face models locally through this detailed code walkthrough.

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Course Feature Course Overview Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free

providerProvider:

Youtube

certificateCertificate:

No Information

languageLanguage:

English

start dateStart Date:

2023-03-08 00:00:00

Course Overview

❗The content presented here is sourced directly from Youtube platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [September 06th, 2023]

What skills and knowledge will you acquire during this course? This course will provide learners with the skills and knowledge to use Hugging Face models locally. Learners will learn how to access models that are not available via the API endpoints, as well as how to load the embedding models locally. Additionally, learners will gain an understanding of how to use the endpoints and how to load the models locally. How does this course contribute to professional growth? This course provides an opportunity for professional growth by teaching participants how to use Hugging Face models locally. Participants will learn how to access models that are not available via the API endpoints, as well as how to load the embedding models locally. This knowledge can be applied to a variety of tasks, such as natural language processing, text classification, and sentiment analysis. By mastering these skills, participants can become more proficient in their field and gain a competitive edge in the job market. Is this course suitable for preparing further education? This course provides a code walkthrough on how to use Hugging Face models locally, which can be beneficial for those looking to further their education in the field of natural language processing. It covers how to access models that don't work in the endpoints, as well as how to load embedding models locally.

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