The "Large Language Model (LLM) Course" on GitHub by Maxime Labonne is a treasure trove for anyone interested in diving deep into the world of LLMs. This meticulously crafted course is designed to guide learners through the essentials of Large Language Models, leveraging Colab notebooks and detailed roadmaps to provide a hands-on learning experience. Here's a glimpse of what the course offers:
- LLM Fundamentals: The course begins with the basics, covering crucial mathematical concepts, Python programming, and the foundations of neural networks. It ensures that learners have the necessary groundwork to delve deeper into the subject.
- The LLM Scientist and Engineer: The curriculum is cleverly divided into two tracks – one for those aiming to master the science behind building state-of-the-art LLMs and another for those interested in engineering LLM-based applications and solutions.
- Hands-on Learning: With a rich collection of notebooks, the course provides practical experience in fine-tuning, quantization, and deploying LLMs. From fine-tuning Llama 2 in Google Colab to exploring quantization techniques for optimizing model performance, learners can get their hands dirty with real-world applications.
- Comprehensive Coverage: Topics range from the very basics of machine learning and Python to advanced areas like neural network training, natural language processing (NLP), and beyond. The course also dives into specific LLM applications, offering insights into decoding strategies, model quantization, and even how to enhance ChatGPT with knowledge graphs.
- Accessible and User-Friendly: Designed with the learner in mind, the course materials are accessible to both beginners and advanced users, with Colab notebooks simplifying the execution of complex codes and experiments.