Large-Language-Model-Notebooks-Course

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Large-Language-Model-Notebooks-Course is an open-source educational resource providing practical, hands-on projects for engineers, researchers, and developers using Large Language Models. It covers topics like GPT, LLaMA, LangChain, and Hugging Face.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Large-Language-Model-Notebooks-Course?

Large-Language-Model-Notebooks-Course is an unofficial, open-source repository offering a practical, hands-on course on Large Language Models (LLMs) and their applications. It's designed for engineers, researchers, and developers, providing updated notebooks and new examples beyond its associated book. The course is divided into three sections: Techniques and Libraries, Projects, and Enterprise Solutions, covering topics such as OpenAI API, Hugging Face, vector databases, LangChain, fine-tuning (PEFT, LoRA, QLoRA), and model evaluation. It includes practical projects like building chatbots, NL2SQL translators, RAG systems, moderation systems, and data analyst agents. Most notebooks are hosted on Colab or Kaggle, with accompanying Medium articles for detailed explanations.

Best used for

Ideal for engineers, researchers, and developers who need to gain practical experience with Large Language Models, build real-world AI applications, and understand advanced LLM techniques. Especially valuable for those looking for hands-on projects using OpenAI, Hugging Face, and LangChain.

Common actions

learn LLM development

build AI applications

practice prompt engineering

evaluate language models

fine-tune LLMs

face swappinggithub copilot"AI Agents"collaborationautomated workflowworkflowsopen-sourcedeepfakelow-code/no-code

Capabilities

Key features

Hands-on LLM projects
OpenAI API integration
Hugging Face library
LangChain applications
Fine-tuning techniques
Model evaluation metrics
Vector database usage

Target Audience

studentteacherprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What topics are covered in the Large-Language-Model-Notebooks-Course?

The course covers a wide range of topics including an introduction to LLMs with OpenAI, vector databases, LangChain, LLM evaluation, fine-tuning (PEFT, LoRA, QLoRA), and practical projects like chatbots, NL2SQL translators, and RAG systems. It also delves into enterprise solutions for LLM integration.

Is this course suitable for beginners in Large Language Models?

While it provides practical examples, the course is described as being in permanent development and covers advanced topics. It's geared towards engineers, researchers, and developers, suggesting a foundational understanding of programming and machine learning concepts would be beneficial.

Where can I run the notebooks provided in the course?

Most of the notebooks are designed to be run on Google Colab, with some also available on Kaggle. Users can also run them in their local environment. Some notebooks may require more memory than the free versions of Colab or Kaggle provide.

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