Hands‑On Large‑Model Tutorial: From Fine‑Tuning to Security Attacks (34k‑Star Repo)

Why the project matters

Most large‑model learning resources fall into two extremes: dense paper dissection or superficial API wrappers. The "Dive into LLMs" series fills the gap by delivering an end‑to‑end, practical curriculum that covers fine‑tuning, prompt learning, chain‑of‑thought reasoning, knowledge editing, mathematical reasoning, model watermarking, jailbreak attacks, and steganography. Every chapter ships PDF slides, a detailed README, and executable .ipynb notebooks, ensuring that learners can immediately apply what they study.

Core highlights

Full LLM pipeline: fine‑tuning → prompt learning → knowledge editing → math reasoning → security offense/defense

Each module provides PDFs, experiment manuals, and runnable scripts

June 2025 update adds a domestically‑focused LLM development tutorial supported by Huawei Ascend community

Completely free, open‑source, and community‑driven (PRs welcome)

Technical architecture and examples

The repository contains seven independent chapters. Chapter 1 walks users through selecting a pre‑trained model, fine‑tuning it on a specific task, and deploying the result as an interactive demo—mirroring common enterprise needs. Chapter 2 focuses on prompt engineering and chain‑of‑thought reasoning, illustrated with a relatable "AI online seeking encouragement" example that shows how a well‑crafted prompt can dramatically improve response quality. Later chapters teach knowledge editing (controlling what the model remembers), watermarking and steganography (embedding invisible signatures in generated text), and jailbreak attacks (demonstrating how to breach model defenses), offering a rare blend of offensive and defensive techniques.

Getting started quickly

All you need is a GitHub account. Clone the repo and launch the notebooks:

git clone https://github.com/Lordog/dive-into-llms.git<br/>cd dive-into-llms<br/>pip install jupyter notebook<br/>jupyter notebook

The notebooks list all dependencies and datasets, so no extra configuration is required. The project also provides a dedicated "Domestic LLM Development Full‑Process" tutorial tailored for the Huawei Ascend ecosystem.

Intended audience

The material targets computer‑science students, AI researchers, and engineers transitioning to LLM development. It is especially useful for graduation projects, competitions, and academic research, offering concrete code for tasks such as "rapidly distilling a mini‑R1" and providing security‑focused experiment templates that are hard to find elsewhere.

Final thoughts

In the surge of LLM hype, the real shortage is hands‑on, reproducible tutorials. "Dive into LLMs" proves, with its 34k+ stars, that developers crave runnable code, repeatable experiments, and deployable knowledge more than abstract concepts. Star the repository and start with the first notebook to begin your own LLM journey.