10 GitHub Repositories to Master Large Language Models

If you are not familiar with large language models (LLMs) today, you may already be falling behind in the AI revolution. Companies are increasingly integrating LLM-based applications into their workflows. As a result, there is a high demand for LLM engineers and operations engineers who can train, fine-tune, evaluate, and deploy these language models into production.

In this article, we will review 10 GitHub repositories that will help you master the tools, skills, frameworks, and theories necessary for working with large language models.

 
This repository is a goldmine for learning prompt engineering, one of the most critical skills for working effectively with LLMs. It provides tips, tricks, and examples to help you craft better prompts and get the most out of models like GPT-4o.

Why it is important:

• Focuses on practical techniques for optimizing prompts.
• Includes examples for diverse use cases, such as summarization, coding, and creative writing.

 
This repository offers a comprehensive course on LLMs, designed for learners of all levels. It includes tutorials, projects, and hands-on exercises to help you understand and apply LLMs effectively.

Why it is important:

• Covers both theoretical foundations and practical applications.
• Perfect for beginners and professionals looking to deepen their knowledge.

 
This is a complete list of resources related to LLMs, including research papers, tools, frameworks, and tutorials. It is a one-stop shop for exploring the LLM ecosystem and staying updated on the latest advancements.

Why it is important:

• Includes resources on training, evaluation, and serving LLMs.
• Regularly updated to include new models, tools, and research.

 
This repository is a treasure trove of research papers on LLM-based agents. It is perfect for those interested in cutting-edge AI applications that use AI agents to improve capabilities of LLMs.

Why it is important:

• Stay up-to-date with the latest research on LLM-based agents.
• Ideal for academics and professionals exploring LLM agent applications.

 
This repository focuses on integrating LLMs into workflows. It provides an ebook-style introduction to various topics such as prompt engineering, local LLMs, retrieval-augmented generation (RAG) problems, and more. Furthermore, it includes exercises with solutions for you to practice your learning.

Why it is important:

• Learn to leverage LLMs in technical projects.
• Tailored for data scientists looking to expand their skill set.

 
This repository is a collection of awesome LLM-based applications, showcasing real-world use cases built with OpenAI, Anthropic, Gemini, and open-source models. It also highlights AI agents and retrieval-augmented generation (RAG) systems.

Why it is important:

• Explore real-world applications of LLMs.
• Get inspired by unique use cases and easy to use frameworks.

 
This repository focuses on multimodal LLMs, which can process multiple input types like text, images, and audio. It is a must-read for those exploring the next frontier of LLM capabilities.

Why it is important:

• Provides insights into the latest multimodal AI advancements.
• Includes a list of papers, tools, and datasets.

 
This is the official code repository for the O’Reilly book “Hands-On Large Language Models”. It includes practical examples and projects to help you gain hands-on experience with LLMs.

Why it is important:

• A practical learning resource for developers and engineers.
• Covers topics like fine-tuning, deployment, and building LLM-powered applications.

 
This handbook contains a list of resources for LLM engineers, covering everything from model training to deployment. It is perfect for developers building or fine-tuning LLM applications.

Why it is important:

• A complete guide for LLM engineering.
• Includes tools and frameworks for both training and serving LLMs.

 
If you are interested in building your own LLM from scratch, this repository is for you. It walks you through the process of implementing a ChatGPT-like model in PyTorch, step by step.

Why it is important:

• Ideal for those who want a deep understanding of LLM internals.
• A hands-on approach to mastering the foundational concepts of LLMs.

Conclusion

 
Mastering LLMs requires a blend of theoretical knowledge, familiarity with modern tools, and hands-on practical experience. The 10 GitHub repositories covered in this blog offer all three by introducing you to cutting-edge AI frameworks, providing valuable resources, papers, and tutorials, and guiding you through exercises and projects to build your own LLM-based applications. Additionally, these repositories are regularly updated, helping you stay current with advancements in LLM applications, AI agents, and frameworks.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.