Dive Into LLMs: From Fine-Tuning to Jailbreak Attacks, All in One Free Course

If you've been meaning to get your hands dirty with large language models but felt overwhelmed by the sheer amount of scattered tutorials, papers, and code snippets, I've got something for you. There's a new GitHub repo called dive-into-llms that’s been making the rounds, and it’s basically a structured, hands-on curriculum for anyone who wants to learn by doing.

It’s not just about calling OpenAI’s API. It’s about understanding the mechanics, from supervised fine-tuning to adversarial attacks. And it’s completely free.

What It Does

dive-into-llms is a collection of practical programming tutorials designed to take you from the basics of LLM usage to more advanced topics. The repo is organized into progressive sections, each with clear code examples and explanations. The core idea is to bridge the gap between theoretical knowledge and real-world implementation.

You’ll find hands-on exercises covering:

• Fine-tuning open-source models on custom datasets.
• Prompt engineering and few-shot learning.
• Alignment techniques like RLHF (Reinforcement Learning from Human Feedback) and DPO (Direct Preference Optimization).
• Safety and security, including red-teaming and jailbreak attacks to understand model vulnerabilities.

The author wrote this in Chinese, and the tweet roughly translates to "Hands-on learning with large models: a free programming practice tutorial from fine-tuning to jailbreak attacks." So if you're comfortable with Chinese, you're in for a smooth ride. The code itself is universal, though.

Why It’s Cool

What sets this apart from the typical "here's how to use LangChain" blog posts is the depth and honesty.

First, it doesn't shy away from the messy parts. The section on jailbreak attacks is particularly interesting. Instead of just telling you to "be careful," it shows you how attacks like prompt injection or adversarial examples actually work in code. You learn how to break a model before you learn how to defend it. That’s the most effective way to understand security, in my opinion.

Second, it's practical. Each tutorial is self-contained and runs on consumer-grade hardware (or free Colab instances) if you use smaller models. You're not expected to have a cluster of A100s lying around.

Third, the structure is fantastic. It's written like a roadmap. You can skim the table of contents and pick exactly what you need, whether that's understanding LoRA or implementing a simple red-team tool.

How to Try It

Getting started is as simple as cloning the repo. There’s no complex package installation or proprietary setup.

git clone https://github.com/Lordog/dive-into-llms.git
cd dive-into-llms

From there, open up the notebooks or Python scripts in your preferred editor (Jupyter Lab or VS Code work great). The README has a solid overview of the sections, so you can start with the first tutorial or jump straight to the topic that interests you most.

If you don't have a GPU, don't worry. Many of the examples use models like Qwen or Llama in their smallest sizes, which are manageable on CPU for demo purposes. Just be patient with inference times.

Final Thoughts

If you're a developer who wants to move beyond being an API consumer, this repo is worth a bookmark. It gives you a structured path to understand the internals of LLMs without getting lost in academic papers.

I especially appreciate the inclusion of security topics. It's rare to find a resource that treats attacks and defenses as a core part of the learning journey, rather than an afterthought.

The repo is a work in progress, so expect things to evolve. But as of now, it's a solid gold mine for self-learners. Throw it on your list of resources, clone it, and spend a weekend working through a couple of sections. You'll come out the other side with a much stronger mental model of how these models actually behave.

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