Top LLM GitHub Repositories to Master Large Language Models

In today’s world, whether you are a working professional, a student, or in the domain of research. If you didn’t know about Large Language Models (LLMs) or aren’t exploring LLM GitHub repositories, then you are already falling behind in this AI revolution. Chatbots like ChatGPT, Claude, Gemini, and others use LLMs as their backbone for performing tasks like generating content and code using simple prompting techniques and natural language. In this guide, we will explore some of the top repositories like awesome-llm to master LLMs and the best open-source LLM GitHub projects, to help you learn the basics of these Large Language Models and how you can use them according to your work requirements.

Why You Should Master LLMs

Companies like Google, Microsoft, Amazon, and many other big giants are building their LLMs these days. Other organizations are hiring engineers to fine-tune and deploy these LLMs according to their needs. Thus, the rise in the demand for people with LLM expertise has increased significantly. A practical understanding of LLMs is now a prerequisite for all kinds of jobs in domains like software engineering, data science, etc. So, if you haven’t yet looked into learning about LLMs, now is the time to explore and upskill.

Top Repositories to Master LLMs

In this section, we will explore the top GitHub repositories with detailed tutorials, lessons, code, and research resources for LLMs. These repositories will help you master the tools, skills, frameworks, and theories necessary for working with LLMs.

Also Read: Top 12 Open-Source LLMs for 2025 and Their Uses

1. mlabonne/llm-course

This repository contains a complete theoretical and hands-on guide for learners of all levels who want to explore how LLMs work. It covers topics ranging from quantization and fine-tuning to model merging and building real-world LLM-powered applications.

Why it is important:

• It’s ideal for beginners as well as for working professionals to enhance their knowledge, as each course is divided into clear sections from foundational to advanced concepts.
• Helps to cover both theoretical foundations and practical applications, ensuring a well-structured guide.
• Has a rating of more than 51k stars and a large community contribution.

GitHub Link:

2. HandsOnLLM/Hands-On-Large-Language-Models

This repository follows the O’Reilly book ‘Hands-on Language Models’ and provides a visually rich and practical guide to understanding the working of LLMs. This repository also includes Jupyter notebooks for each chapter and covers important topics such as: tokens, embeddings, transformer architectures, multimodal LLMs, finetuning techniques, and many more.

Why it is important:

• It gives practical learning resources for developers and engineers by offering a wide range of topics from basic to advanced concepts.
• Each chapter includes hands-on examples that help users to apply the concepts in real-world cases rather than just remember them theoretically.
• Covers topics like fine-tuning, deployment, and building LLM-powered applications.

GitHub Link:

3. brexhq/prompt-engineering 

This repository contains a complete guide and offers practical tips and strategies for working with Large Language Models like OpenAI’s GPT-4. It also contains lessons learned from researching and creating prompts for production use cases. This guide covers the history of LLMs, prompt engineering strategies, and safety recommendations. Topics include prompt structures, token limits on top LLMs.

Why it is important:

• Focuses on real-world techniques for optimizing prompts, hence it helps developers a lot to enhance the LLM’s output.
• Contains a detailed guide and offers foundational knowledge and advanced prompt strategies. 
• Large community support, and also have regular updates to reflect that users can access the latest information.

GitHub Link:

4. Hannibal046/Awesome-LLM

This repository is a live collection of resources related to LLMs, it contains seminal research papers, training frameworks, deployment tools, evaluation benchmarks, and many more. It is organized into different categories, including papers and application books. It also has a leaderboard to track the performance of different LLMs.

Why it is important:

• This repository gives important learning materials, including tutorials and courses.
• Contains a large quantity of resources, which makes it one of the top resources for master LLMs.
• With over 23k stars, it has a large community that ensures regularly updated information.

GitHub Link:

5. OpenBMB/ToolBench

ToolBench is an open source platform, this one is designed to train, serve, and evaluate the LLMs for tool learning. It gives an easy-to-understand framework that includes a large-scale instruction tuning dataset to enhance tool use capabilities in LLMs.

Why it is important:

• ToolBench enables LLMs to interact with external tools and APIs. This increases the ability to perform real-world tasks.
• Also offers an LLM evaluation framework, ToolEval, with tool-eval capabilities like Pass Rate and Win Rate.
• This platform serves as a foundation for learning new architecture and training methodologies.

GitHub Link:

6. EleutherAI/pythia

This repository comes as a Pythia project. The Pythia suite was developed with the explicit purpose of enabling research in interpretability, learning dynamics, and ethics and transparency, for which existing model suites were inadequate.

Why it is important: 

• This repository is designed to promote scientific research on LLMs.
• All models have 154 checkpoints, which enables us to get the intrinsic pattern from the training process.
• All the models, training data, and code are publicly available for reproducibility in LLM research.

GitHub Link:

7. WooooDyy/LLM-Agent-Paper-List

This repository systematically explores the development, applications, and implementation of LLM-based agents. This provides a foundational level resource for researchers and learners in this domain.

Why it is important:

• This repo offers an in-depth analysis of LLM-based agents and covers their making steps and applications.
• Contains a well-organized list of must-read papers, making it easy to access for learners.
• Explain in depth about the behaviour and internal interactions of multi-agent systems.

GitHub Link:

8. BradyFU/Awesome-Multimodal-Large-Language-Models

This repository has a great collection of resources for people focused on the latest advancements in Multimodal LLMs (MLLMs). It covers a wide range of topics like multimodal instruction tuning, chain-of-thoughts reasoning, and, most importantly, hallucination mitigation techniques. This repo is also featured on the VITA project. It is an open-source interactive multimodal LLM platform with a survey paper to provide insights about the recent development and applications of MLLMs.

Why it is important:

• This repo alone sums up a vast collection of papers, tools, and datasets related to MLLMs, making it a top resource for learners.
• Contains a large number of studies and techniques for mitigating hallucinations in MLLMs, as it is a crucial step for LLM-based applications.
• With over 15k stars, it has a large community that ensures regularly updated information.

GitHub Link:

9. deepseedai/DeepSpeed

Deepseed is an open-source deep learning library developed by Microsoft. It is integrated seamlessly with PyTorch and offers system-level innovations that enable the training of models with high parameters. DeepSpeed has been used to train many different large-scale models such as Jurassic-1(178B), YaLM(100B), Megatron-Turing(530B), and many more.

Why it is important:

• Deepseed has a zero-redundancy optimizer that allows it to train models with hundreds of billions of parameters by optimizing memory usage.
• It allows for easy composition of a multitude of features within a single training, inference, or compression pipeline.
• DeepSpeed was an important part of Microsoft’s AI at Scale initiative to enable next-generation AI capabilities at scale.

GitHub Link:

10. ggml-org/llama.cpp

LLama C++ is a high-performance open-source library designed for C/C++ inference of LLMs on local hardware. It is built on top of the GGML tensor library, it supports a large number of models that include some of the most popular ones, also as LLama, LLama2, LLama3, Mistral, GPT-2, BERT, and more. This repo aims to minimal setup and optimal performance across diverse platforms, from desktops to mobile devices.

Why it is important:

• LLama enables local inference of the LLMs directly on desktops and smartphones, without relying on cloud services.
• Optimized for hardware architectures like x86, ARM, CUDA, Metal, and SYCL, making it versatile and efficient. As it supports GGUF (GGML Universal file) to support quantization levels (2-bit to 8-bit), reducing memory usage, and enhancing inference speed.
• As of the recent updates now it also supports vision capabilities, allowing it to process and generate both text and image data. This also expands the scope of applications.

GitHub Link:

11. lucidrains/PaLM-rlhf-pytorch

This repository offers an open-source implementation of Reinforcement Learning with Human Feedback (RLHF), which is applied to the  Google PaLM architecture. This project aims to replicate ChatGPT’s functionality with PaLM. This is helpful for ones interested in understanding and developing RLHF-based applications.

Why it is important:

• PaLM-rlhf provides a clear and accessible implementation of RHFL to explore and experiment with advanced training techniques.
• It helps to build the groundwork for future advancements in RHFL and encourages developers and researchers to be a part of the development of more human-aligned AI systems.
• With around 8k stars, it has a large community that ensures regularly updated information.

GitHub Link:

12. karpathy/nanoGPT

This nanoGPT repository offers a high-performance implementation of GPT-style language models and serves as an educational and practical tool for training and fine-tuning medium-sized GPTs. The codebase of this repo is concise, with a training loop in train.py and model inference in model.py. Making it accessible for developers and researchers to understand and experiment with the transformer architecture.

Why it is important:

• nanoGPT offers an easy implementation of GPT models, making it an important resource for those looking to understand the inner workings of transformers.
• It also enables optimized and efficient training and fine-tuning of medium-sized LLMs.
• With over 41k stars, it has a large community that ensures regularly updated information.

GitHub Link:

Overall Summary

Here’s a summary of all the GitHub repositories we’ve covered above for a quick preview.

Login to continue reading and enjoy expert-curated content.

Keep Reading for Free