A structured educational course that teaches the theory and practical application of Large Language Models from fundamentals to deployment. It is divided into LLM Fundamentals, LLM Scientist, and LLM Engineer sections and includes interactive Colab notebooks.
Project overview
The course provides a structured learning path covering mathematics, Python, neural networks, and LLM application deployment while remaining free of charge.
Project type
Model Development · Evaluation & Observability · Learning Resources
Use cases
Learning & Education
Deployment
Refer to project documentation
License
Apache-2.0
Best for
Researchers, developers, and AI engineers seeking a structured educational resource to learn Large Language Model theory and engineering.
Key capabilities
Covers essential prerequisite knowledge about mathematics, Python, and neural networks for understanding Large Language Models.
Focuses on the practical skills needed for creating Large Language Model-based applications and deploying them.
Limitations and risks
GPU requirements, data boundaries, external service dependencies, coding prerequisites, and telemetry configurations are not documented for this tutorial.
Getting started
Consuming the content is rated as easy. Local deployment is not required. Learners can begin by opening a Colab notebook and executing the provided cells.
Alternatives and comparisons
Provides practical examples and Jupyter notebooks covering techniques like prompt engineering, RAG, and fine-tuning for building LLM applications.
Organizes generative AI research, courses, notebooks, and tools by user goals and topics.
GitHub project description: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
README: The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** is optional and covers fundamental knowledge about mathematics, Python, and neural networks. 2. 🧑🔬 **The LLM…
README: ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click.
README: Deploying LLMs at scale is an engineering feat that can require multiple clusters of GPUs. In other scenarios, demos and local apps can be achieved with much lower complexity.
README: Fine-tune Llama 3.1 with Unsloth | Ultra-efficient supervised fine-tuning in Google Colab.