Learn the fundamentals of large language models (LLMs) and put them into practice by deploying your own solutions based on open source models. By the end of this course, you will be able to leverage state-of-the-art open source LLMs to create AI applications using a code-first approach.
Open Source LLMOps Solutions
Open Source LLMOps Solutions
This course is part of Large Language Model Operations (LLMOps) Specialization
Instructors: Noah Gift
2,046 already enrolled
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Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
35 hours to complete
3 weeks at 11 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Run local large language models
Fine-tune LLMs
Use open-source generative AI
Skills you'll gain
Details to know
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Add to your LinkedIn profile
Assessments
9 assignments
Taught in English
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This course is part of the Large Language Model Operations (LLMOps) Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
In this module, you will learn how to leverage pre-trained natural language processing models to build NLP applications. We will explore popular open source models like BERT. You will learn how to access these models using libraries like HuggingFace Transformers and use them for tasks like text classification, question answering, and text generation. A key skill will be using large language models to synthetically augment datasets. By feeding the model examples and extracting the text it generates, you can create more training data. Through hands-on exercises, you will build basic NLP pipelines in Python that use pre-trained models to perform tasks like sentiment analysis. By the end of the module, you'll have practical experience using state-of-the-art NLP techniques to create capable language applications.
What's included
20 videos10 readings3 assignments1 discussion prompt
In this module, you run language models locally. Keep data private. Avoid latency and fees. Use Mixtral model and llamafile.
What's included
8 videos11 readings3 assignments3 ungraded labs
In this module, you will use models in the browser with Transformers.js and ONNX. You will gain experience on porting models to the ONNX runtime and experience how to put them on the browser. You will also use the Cosmopolitan project to build a phrase generator that is easily portable on different systems.
What's included
7 videos5 readings2 assignments1 ungraded lab
In this module, you will focus on completing several external labs and hands-on examples that will allow you to feel comfortable running local LLMs, connect to them with APIs using Python as well as building solutions with the Rust programming language
What's included
7 readings1 assignment1 ungraded lab
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