Hysen Labsllm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
LLM Course, a free route into large language models
The LLM Course is a free, three-part path into large language models, with roadmaps and Colab notebooks. It runs from fundamentals through building models to deploying applications.
Three parts, one path
The repository is a course for getting into large language models, delivered as roadmaps and Colab notebooks. Part one, LLM Fundamentals, is optional and covers mathematics, Python, and neural networks. Part two, the LLM Scientist, focuses on building the best possible models with the latest techniques. Part three, the LLM Engineer, is about creating applications and deploying them.
Notebook tools
A table of tools sits near the top. LLM AutoEval evaluates models using RunPod. LazyMergekit merges models with MergeKit in one click, and LazyAxolotl fine-tunes them in the cloud with Axolotl. AutoQuant handles quantization into formats like GGUF, GPTQ, EXL2, AWQ, and HQQ. ZeroSpace spins up a Gradio chat interface on a free ZeroGPU. Model Family Tree visualizes how merged models relate.
The fundamentals section
The fundamentals cover three areas. Mathematics for machine learning runs through linear algebra, calculus, and probability. Python for machine learning starts with basics, then data science libraries like NumPy and Pandas, then data preprocessing. Neural networks content explains layers, activation functions, backpropagation, loss functions, and regularization.
Scientist and engineer tracks
The Scientist section works through the Transformer architecture, tokenization, and attention mechanisms, then pretraining with data preparation and distributed training. Post-training covers supervised fine-tuning and preference alignment. The Engineer track shifts to building and deploying LLM-based applications, with fine-tuning and quantization notebooks along the way.
The book and the license
The author notes he co-wrote the LLM Engineer's Handbook, a hands-on book that walks through an end-to-end LLM application from design to deployment. The course itself is described as staying free, with the book as a way to support the work. The repository carries the Apache-2.0 license.
Editorial conclusion
The course sets a clear sequence: understand the basics, learn to build, learn to ship. The README is equally clear that it stays free, with a paid book as optional support.
Community notes