10 GitHub LLM Repositories Every AI Engineer Should Know

Are you an AI engineer, wondering how to attain resources that can put your skills to a practical test? It might be difficult to look for the right solution for you, based on the vast amount of information out there. Hence, we present this list of all ten GitHub llm repositories every AI engineer ought to be acquainted with. These are not mere assignments in academia; these are hands-on, real-world projects developed by experts from Microsoft, Karpathy, and open-source communities.

Whether you are just entering the world of machine learning, deep into large language models, or deploying AI agents into production, these repositories provide simple code, guided projects, and industry domains to explore. In other words, from learning to building to deploying, think of this as your guide to go smarter, faster, and better with AI.

1. Machine Learning for Beginners

Machine Learning for Beginners is a 12-week learning plan that was created by Microsoft that teaches the basics of machine learning with real-world data and the scikit-learn library. It is systematically laid out similar to a classroom course, and includes lessons on supervised learning and unsupervised learning, classification, regression, clustering, and time series analysis. Each module includes interactive Jupyter notebooks, activities, and quizzes to confirm understanding. This repository breaks down complicated machine learning concepts into more digestible topics, allowing individuals to learn valuable skills through practice and experimentation.

Best For:

• Complete beginners who want a structured way to start learning about machine learning.
• Educators who are teaching applied ML.
• Self-learners who wish to learn from real data and build a portfolio.

GitHub Repository: https://github.com/microsoft/ML-For-Beginners

2. AI for Beginners

AI for Beginners is an extension of the ML base to take students into AI, exploring deep learning, natural language processing, computer vision models, and transformers. Also created by Microsoft, it is a 12-week course that offers tools like PyTorch and TensorFlow and allows students to learn foundational AI principles through hands-on practice and interactive labs. While the former delves into algorithmic principles, the emphasis on ethical AI, model deployment, and the considerations for real-world implementation comprise the application end. While it does well to balance the two, it is best for some students transitioning from standard ML to AI.

Best For:

• Students transitioning from ML to AI
• Developers wanting to replace the need to work with neural networks and transformer models
• Students wanting experience and project exposure to modern AI applications

GitHub LLM Repository: https://github.com/microsoft/AI-For-Beginners

3. Neural Networks: Zero to Hero

A hands-on dive into the inner workings of deep learning created by Andrej Karpathy, Neural Networks: Zero to Hero, focuses on how to build neural networks and GPT-style models from scratch using only Python and NumPy, without high-level libraries. Karpathy takes difficult concepts like backpropagation, gradient descent, and self-attention and breaks them down into easy to learn lessons with code. The real prize is the mini-GPT implementation that goes over how transformers function at a low level.

Best For:

• Engineers and researchers wanting to learn about deep learning from the first principles
• People wanting to implement neural networks from scratch
• The curious learner who loves looking at low-level code

GitHub Repository: https://github.com/karpathy/nn-zero-to-hero

4. Deep Learning Paper Implementations

This is a curated collection of PyTorch implementations of the latest deep learning papers, including GANs, Transformers, Diffusion Models, and more. Our goal is to assist developers who wish to take the next step beyond reading deep learning papers and push forward with implementing the articles. Each model has been implemented clearly and concisely which often achieves the same results as referenced in the paper. With this repository, engineers can reproduce experiments, understand inventions, and extend modern state-of-the-art architectures in the fields of generative AI and computer vision.

Best For:

• Reproducing state-of-the-art results from leading ML papers
• Learning new architectures with actual code
• Extending or modifying advanced deep learning models

GitHub LLM Repository: https://github.com/lucidrains

5. Made With ML

Made With ML is a complete curriculum created for the entire machine learning lifecycle from design and development to deployment and monitoring. Built by Goku Mohandas, Made With ML focuses on practical skills like data versioning (DVC), continuous integrations, testing ML pipelines, serving models through APIs, and monitoring ML systems in production. It also includes concepts around responsible AI and reproducibility. This is a true MLOps bootcamp in a box, particularly valuable to engineers working on production systems.

Best For:

• MLOps and AI engineers deploying an ML system in the real world
• Teams building large-scale ML infrastructure
• Learners wanting to get a project-oriented experience of end-to-end ML

GitHub Repository for AI Engineers: https://github.com/GokuMohandas/Made-With-ML

6. Hands-On Large Language Models

Hands-On LLMs is a workflow for building and tuning large language models. The repo extends the popular O’Reilly book, and it has user interactivity for notebooks that explore tokenisation, attention, transformer blocks, RAG (retrieval-aided generation), embeddings, and evaluation methods. It used Hugging Face Transformers and LangChain integrations to provide the foundation for the development of real-world applications with full interpretability and modularity, real-world applications like chatbots, summarizers, and document QA systems.

Best For:

• Engineers are implementing LLMs into tangible, real-world applications.
• Developers who will fine-tune models for specific domain tasks.
• Researchers are investigating prompt strategies and evaluation metrics.

AI-based GitHub Repository: https://github.com/HandsOnLLM

7. Advanced RAG Techniques

This repository contains over 30 adaptations of the Retrieval-Augmented Generation (RAG) method, such as HyDE, GraphRAG, and more complex approaches to chunking. Its use supports the ability to make the experiment with different embedding models, vector stores, document splitting, reranking, and performance benchmarking. The community can carry out the search of different methods in order to reveal the most suitable approaches for each case, using types of documents and queries as criteria of performance, and hence optimising LLM-driven search and QA solutions.

Best For:

• AI engineers who are designing and building RAG systems for the industry
• Teams that are trying to make the knowledge retrieval process faster while keeping the quality intact
• Scientists who are making a comparative study of vector search, hybrid, and graph approaches

GitHub Repository: https://github.com/NirDiamant/RAG_Techniques

8. AI Agents for Beginners

This new user-friendly repo from Microsoft is an introduction for learners to AI agents, which are autonomous systems powered by LLMs and can plan, decide, and act on things. The repo has 11 experiential labs – all using AutoGen, LangChain, OpenAI APIs, etc., to code agents who can carry out multi-step, multi-turn tasks, invoke tools, search for knowledge, and collaborate with other agents. Each lab introduces concepts in action planning, tool chaining, memory, and prompt engineering in a clear and reproducible way.

Best for:

• Developers new to AI agents or agentic workflows
• Educators who want to develop a hands-on agent-based AI curriculum
• Hackers are building autonomous task agents from the ground up

GitHub LLM Repository: https://github.com/microsoft/AI-Agents

9. Agents Towards Production

Agents Towards Production is a well-rounded guide for putting AI agents from proof of concept to production. We will cover implementation patterns for orchestration, tool integration, error processing, retry logic, security, memory (Redis, vector DBs), and deployment with FastAPI and Docker. Interest in scalable agentic systems is growing, and this repo serves as a template to ship reliable and scalable agent workflows to industry.

Best For:

• Developers deploying AI agents in production
• Teams building full-stack agenting infrastructure
• Professionals using LangGraph, OpenAgents or AutoGen

GitHub LLM Repository: https://github.com/NirDiamant/agents-towards-production

10. AI Engineering Hub

AI Engineering Hub is a gigantic, curated collection of 70+ real-world projects, tutorials, and templates across LLMs, RAG, and autonomous agents. It is designed for engineers wanting to further their skills through practical, hands-on experiences. Each project on the site has difficulty and category tagging, with links to Colab, references, and suggested customisations. The Hub is a digital sandbox for learning every AI tool you have ever wanted to try, ready to fork and remix.

Best For:

• Building a portfolio of GenAI and agent-based applications
• Practicing advanced LLM workflows in a modular fashion
• Experimenting with new tools and frameworks

GitHub Repository: https://github.com/ashishps1/learn-ai-engineering

Conclusion

To get good at AI, you can’t expect to just read papers or follow tutorials; you need to build and iterate with appropriate tools. The GitHub LLM repositories that we’ve discussed are a complete package. You can go from learning about machine learning to interacting with these AI agents in real time. If you’ve been focusing on deep learning, large language models (LLMs), retrieval-augmented generation (RAG), and/or agent orchestration, you have a lot of strong real-world projects to draw on.

Look into them, fork the code, modify the models, and build something of your own. In a fast-moving field like AI, active = learning, and these repos are a good way to be active.

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