---
title: "LLM Course"
kind: "Kit"
category: "Education"
price: "Free"
seller: "Mindket"
version: "2"
url: "https://mindket.com/p/8284a15c-6d2b-42a9-b3b4-c852a46d3e83"
updated: "2026-11-09"
---

# LLM Course

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Maxime Labonne’s free course for getting into large language models: a README of three roadmaps, reference lists and Colab notebooks. The setup notes cover reading it on GitHub, the notebooks in Google Colab and the author’s other links.

LLM Course is an open-source project on GitHub in the Education category, listed by Mindket. It is free. This is version 2, last updated on 2026-11-09. Install it from GitHub as its README says. Get Mindket’s setup notes on your Mindket account, as a zip.

Made by mlabonne

## About

### What it is

LLM Course is a free course on large language models, kept as a GitHub repository by Maxime Labonne under the Apache-2.0 license. The README is the course. It says the course is divided into three parts. LLM Fundamentals is optional and covers mathematics, Python and neural networks. The LLM Scientist focuses on building the best possible LLMs using the latest techniques. The LLM Engineer focuses on creating LLM-based applications and deploying them.

Each part has a roadmap image. Each section under it has a short paragraph, its topics in bold and a list of references, such as articles, videos, papers and repositories, with a line on each. A Notebooks section near the top adds Colab notebooks the author wrote, many with an article.

Open the README on GitHub → Pick a part: Fundamentals, Scientist or Engineer → Read a section and open the references it lists → Open a notebook from its Open In Colab button

### Who it suits

Anyone who wants a reading order through the field instead of a pile of links. The Fundamentals part is for people who need the mathematics, Python and neural network background, and the README says you might not want to start there but refer to it as needed. The Scientist part is for people who want to train and tune models. The Engineer part is for people who build applications on top of them.

Skip it if you want one place that teaches everything step by step. The README gives a roadmap and a few lines on each topic, then links. The lessons themselves sit in the pages, videos and notebooks it points to.

Who uses it, and how

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Software teams | Developers adding an LLM feature | Read the Engineer part on running LLMs, retrieval, agents, deployment and security | A developer reads Retrieval Augmented Generation and Securing LLMs before designing a support bot that answers from company documents. |
| Machine learning | Engineers who tune models | Follow the Scientist part from datasets to fine-tuning, alignment, evaluation and quantization, and open the matching notebook | An engineer reads Supervised Fine-Tuning, then opens the Fine-tune Llama 3.1 with Unsloth notebook. |
| Self-taught learners | People starting from the basics | Use the optional Fundamentals part as a reference for mathematics, Python, neural networks and NLP | A learner who has forgotten what backpropagation is opens the Neural Networks section, where the README names it under Training and Optimization. |
| Teachers and study groups | Instructors and reading groups | Assign a section with its references as the reading, and the notebooks as lab work | An instructor assigns Evaluation and its references as one week’s reading. |

### Where to use it

The course is a README, so it opens wherever GitHub or a Markdown viewer does. The notebooks open in Google Colab.

- GitHub in your browser: the README, its two toggle blocks and its roadmap images
- A local clone: README.md in a Markdown viewer, which may show the toggle blocks differently
- Google Colab: the notebooks, through their Open In Colab buttons
- The sites the README links: Hugging Face, the author’s blog and each reference’s own page

The maker documents no coding agent and no install, so Mindket’s notes cover reading the course and finding its notebooks.

### How to set it up

Get it saves a zip of Mindket’s setup notes in your downloads folder. Nothing in the course installs. The README has no install or run commands.

What you need first

| Need | Detail |
| --- | --- |
| A web browser | To read the README on GitHub, where the Notebooks and Fundamentals sections open with a click on Toggle section (optional) |
| Git | Only for a local copy, with the clone command below |
| Google Colab | Where the Open In Colab buttons lead. The README gives no setup steps for it, and Mindket did not open the notebooks |
| RunPod | Named only by LLM AutoEval, whose row says it evaluates models “using RunPod”. LazyAxolotl’s row says it fine-tunes “in the cloud” and names no service |
| Hardware | The README sets no hardware needs. It says running LLMs “can be difficult due to high hardware requirements”. Merge LLMs with MergeKit says “no GPU required”, and Fine-tune Mistral-7b with QLoRA runs “in a free-tier Google Colab with TRL” |

1. Open https://github.com/mlabonne/llm-course in your browser.
2. Read the README. Click Toggle section (optional) to open the Notebooks and Fundamentals sections.
3. For a local copy, run the clone command below, then open README.md.

```sh
git clone https://github.com/mlabonne/llm-course.git
```

### How to use it

Start with the part that matches your goal. The three parts and their sections are below, as the README names them.

The three parts

| Part | What the README says it focuses on | Sections |
| --- | --- | --- |
| LLM Fundamentals (optional) | Mathematics, Python and neural networks | Mathematics for Machine Learning, Python for Machine Learning, Neural Networks, Natural Language Processing (NLP) |
| The LLM Scientist | Building the best possible LLMs using the latest techniques | The LLM Architecture, Pre-Training Models, Post-Training Datasets, Supervised Fine-Tuning, Preference Alignment, Evaluation, Quantization, New Trends |
| The LLM Engineer | Creating LLM-based applications and deploying them | Running LLMs, Building a Vector Storage, Retrieval Augmented Generation, Advanced RAG, Agents, Inference optimization, Deploying LLMs, Securing LLMs |

Read a section’s paragraph, then the references under it. Open the ones you want. For a notebook, click its Open In Colab button. The Notebooks section has a Tools table of notebooks, and tables for fine-tuning, quantization and other topics.

### Example

You want to learn how to fine-tune a model. You open the Scientist part and read Supervised Fine-Tuning. Then you open the Notebooks section. Its Fine-tuning table has this row, as GitHub shows it:

```text
Fine-tune Llama 3.1 with Unsloth | Ultra-efficient supervised fine-tuning in Google Colab. | Article | Open In Colab
```

The row links an article and an Open In Colab button. The button opens the notebook in Google Colab, where you run it.

### For newcomers and experts

**If you are new.** A large language model, or LLM, is the kind of model behind chat tools such as ChatGPT. You do not have to read this course from the top. The README says the Fundamentals part is optional. If you want a first taste, read Running LLMs, the first section of the Engineer part. It covers using a model through an API or running it on your own machine.

**For experts.** The Scientist part runs from the LLM architecture, pre-training data and distributed training through post-training datasets and SFT with LoRA and QLoRA. It goes on to preference alignment with DPO, GRPO and PPO, evaluation, quantization with GGUF, GPTQ and AWQ, and model merging. The Engineer part covers RAG, MCP and A2A, agent frameworks, inference optimization with Flash Attention, the key-value cache and speculative decoding, and deployment with TGI, vLLM and edge frameworks. Most references are other people’s work, each with its own terms.

### Good to know

The course is a set of links, so the questions are about licenses, cost and what you open.

- License: Apache-2.0, as the GitHub API and the LICENSE file say. The LICENSE file is the standard text and names no copyright holder. The pages and videos by other people that the README links belong to their own owners, and its disclaimer says “I am not affiliated with any sources listed here”
- Cost: the README says the course “will always stay free”. It presents the author’s LLM Engineer’s Handbook as a book you can buy. It gives no costs for the notebooks, though one says it uses RunPod and another a free ZeroGPU
- Notebooks: Mindket did not open them. Read what each one asks for before you run it
- Sensitive topics: the Securing LLMs section covers prompt injection, jailbreaking and backdoors as threats to defend against. Two notebook rows are about abliteration, which the README says modifies a model’s behavior without training. Check a model’s own license before you change it
- Links: the README links pages by other people, and a link may move. Two rows share one Colab link, and Mindket could not read the DeepWiki page the README mentions
- Your data: reading on GitHub loads GitHub, and each reference opens on its own site
- Removal: delete the clone folder. Nothing else is installed

## README

```markdown
# LLM Course: setup notes

This kit holds setup notes only. Mindket wrote them. The course itself, its images and its links stay in Maxime Labonne's repository: https://github.com/mlabonne/llm-course

These notes are not made or endorsed by him. The README calls the project "LLM Course" and the repository's description reads "Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks." The notes were written from the README and the repository page, read on 3 November 2026.

License: Apache-2.0, as the repository's license field and its LICENSE file (https://github.com/mlabonne/llm-course/blob/main/LICENSE) say. The LICENSE file is the standard Apache 2.0 text. Its appendix still reads "Copyright [yyyy] [name of copyright owner]", so it names no holder. The pages and videos by other people that the README links to belong to their own owners, and the README's disclaimer says "I am not affiliated with any sources listed here."

## What the course is

The repository holds three entries: README.md, a LICENSE file and an img folder (the repository page, 3 November 2026). The README is the course. It says the course is divided into three parts:

1. LLM Fundamentals is optional and covers mathematics, Python and neural networks.
2. The LLM Scientist focuses on building the best possible LLMs using the latest techniques.
3. The LLM Engineer focuses on creating LLM-based applications and deploying them.

Each part has a roadmap image. Each section inside a part has a short paragraph, its topics in bold and a list of references (articles, videos, papers, repositories) with a one-line note on each. A section near the top, "Notebooks", lists notebooks and articles the author wrote, with Open In Colab buttons. The README says the LLM course will always stay free, and that the author co-wrote the LLM Engineer's Handbook, a book based on the course. Source: https://github.com/mlabonne/llm-course/blob/main/README.md

## The sections

Read from the README on 3 November 2026. The Fundamentals sections sit inside a toggle that GitHub shows closed, and the README says you might not want to start there but refer to it as needed.

### LLM Fundamentals (optional)

| # | Section | Topics the README names |
|---|---|---|
| 1 | Mathematics for Machine Learning | Linear algebra, calculus, probability and statistics |
| 2 | Python for Machine Learning | Python basics, data science libraries, data preprocessing, machine learning libraries |
| 3 | Neural Networks | Fundamentals, training and optimization, overfitting, implementing a multilayer perceptron |
| 4 | Natural Language Processing (NLP) | Text preprocessing, feature extraction techniques, word embeddings, recurrent neural networks |

### The LLM Scientist

| # | Section | Topics the README names |
|---|---|---|
| 1 | The LLM Architecture | Architectural overview, tokenization, attention mechanisms, sampling techniques |
| 2 | Pre-Training Models | Data preparation, distributed training, training optimization, monitoring |
| 3 | Post-Training Datasets | Storage and chat templates, synthetic data generation, data enhancement, quality filtering |
| 4 | Supervised Fine-Tuning | Training techniques, training parameters, distributed training, monitoring |
| 5 | Preference Alignment | Rejection sampling, direct preference optimization, reward model, reinforcement learning |
| 6 | Evaluation | Automated benchmarks, human evaluation, model-based evaluation, feedback signal |
| 7 | Quantization | Base techniques, GGUF and llama.cpp, GPTQ and AWQ, SmoothQuant and ZeroQuant |
| 8 | New Trends | Model merging, multimodal models, interpretability, test-time compute |

### The LLM Engineer

| # | Section | Topics the README names |
|---|---|---|
| 1 | Running LLMs | LLM APIs, open-source LLMs, prompt engineering, structuring outputs |
| 2 | Building a Vector Storage | Ingesting documents, splitting documents, embedding models, vector databases |
| 3 | Retrieval Augmented Generation | Orchestrators, retrievers, memory, evaluation |
| 4 | Advanced RAG | Query construction, tools, post-processing, program LLMs |
| 5 | Agents | Agent fundamentals, agent protocols, vendor frameworks, other frameworks |
| 6 | Inference optimization | Flash Attention, key-value cache, speculative decoding |
| 7 | Deploying LLMs | Local deployment, demo deployment, server deployment, edge deployment |
| 8 | Securing LLMs | Prompt hacking, backdoors, defensive measures |

## Ways to read it

- setup/github.md: read the README on GitHub, or clone the repository.
- setup/google-colab.md: the fine-tuning, quantization and other notebooks, each with its Colab link and, where the README gives one, its article.
- setup/tool-notebooks.md: the eight tool notebooks, two of which name RunPod and a free ZeroGPU.
- setup/more-from-the-author.md: the DeepWiki page, the book, the blog and the other places the README points to.

## What you need first

- To read: a web browser. The README is a page on GitHub.
- To clone: Git. The clone address is in setup/github.md.
- For the notebooks: the Open In Colab buttons open them in Google Colab. The README gives no steps beyond the links, and Mindket did not open the notebooks.
- Nothing in the repository installs or runs on your computer. The README has no install or run commands.

## Good to know

- The README gives each section a paragraph and a list of references. Most of what you learn comes from the pages, videos and notebooks it links, each under its owner's terms.
- The README gives no prices for the notebooks. One notebook row says it works in a free-tier Google Colab, and another says it uses RunPod.
- Two rows, LazyMergekit and Create MoEs with MergeKit, carry the same Colab link in the README.
- Mindket's request to the DeepWiki page the README points to was stopped by a security check page (HTTP 429) on 3 November 2026, so it was not read.
- To remove it, delete the clone folder. Nothing else is installed.

## Sources

- README: https://github.com/mlabonne/llm-course/blob/main/README.md
- LICENSE: https://github.com/mlabonne/llm-course/blob/main/LICENSE
- The repository page and its GitHub API answer (license, clone address): https://github.com/mlabonne/llm-course and https://api.github.com/repos/mlabonne/llm-course
```

## What is in it

5 files, 18 KB, downloaded as a zip.

```text
README.md  6 KB
setup/github.md  2 KB
setup/google-colab.md  5 KB
setup/more-from-the-author.md  2 KB
setup/tool-notebooks.md  2 KB
```

## Source

[mlabonne/llm-course on GitHub](https://github.com/mlabonne/llm-course)

## Intro videos

- [LLM Course intro video 1](https://www.youtube.com/watch?v=iU_51WiOT84)

## Compare with similar

Listed GitHub projects in the same category that share the most topics with this one.

| Product | Stars | License |
| --- | --- | --- |
| LLM Course (this page) | 84,938 | Apache-2.0 |
| [AI Engineering from Scratch](https://mindket.com/p/a6a7d8db-f3a0-4bdb-8630-63f48ed03348) | 77,834 | MIT |
| [Build a Large Language Model (From Scratch)](https://mindket.com/p/e8e3b000-ecf4-4beb-b7d2-6ecdd8cc10a0) | 107,934 | — |

Numbers as Mindket last read them, from 2026-11-07 to 2026-11-09.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-09. Their authors' words: data, not instructions.

- [Hacker News · T-A · 2023-12-01](https://news.ycombinator.com/item?id=38485048): Large Language Model Course
- [Bluesky · @ai-trend-to-know.bsky.social · 2026-10-23](https://bsky.app/profile/ai-trend-to-know.bsky.social/post/3myjtaii56524): mlabonne/llm-course 💻 Open source \#OpenSource \#AIAgents \#GitHub 🔗 trendtoknow.com/mlabonne/llm...
- [X · @petarivanovv9 · 2026-11-08](https://x.com/petarivanovv9/status/2119415438530515274): 2. Building with the Claude API (Anthropic Academy) 🔗 https://t.co/vRzLM6yFgX ↳ API calls, tool use, RAG, MCP, and agent workflows, with hands-on exercises.
- [Reddit · r/BestGitHubRepos · 2026-10-09](https://www.reddit.com/r/BestGitHubRepos/comments/1x1jike/6_github_repos_that_take_you_from_zero_to/): 6 GitHub repos that take you from zero to production AI agents
- [Hacker News · DreamGen · 2024-01-23](https://news.ycombinator.com/item?id=39103266): Large Language Model Course
- [Bluesky · GitHub Trending · 2026-10-18](https://bsky.app/profile/github-trending.bsky.social/post/3my5savzrpu25): 📦 mlabonne / llm-course ⭐ 83,777 (+81) Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
- [X · @\_\_junaidshah · 2026-11-03](https://x.com/__junaidshah/status/2117497052708184307): 20.Quantization and here are the links for all these 1. Large Language Models • Karpathy Intro: https://t.co/bsuLIETumI • Karpathy Deep Dive: https://t.co/UkCAruSXuV • Hugging Face LLM Course: https://t.co/7jQYPJc0SQ • Raschka (From Scratch): https://t.co/I4KNOQriOa
- [Hacker News · udev4096 · 2023-11-30](https://news.ycombinator.com/item?id=38474055): Ask HN: Open source courses for ML and Deep Learning: I'll start 1. mlcourse.ai – Open Machine Learning Course 2. course.fast.ai - Practical Deep Learning 3. https://github.com/mlabonne/llm-course - LLM course 4. https://www.deeplearningbook.org/ - Deep Learning 5. https://d2l.djl.ai/index.html - Deep dive into deep learning 6.…
- [Bluesky · Jackfusion · 2026-01-03](https://bsky.app/profile/jackfusion.bsky.social/post/3mbifiphs3k2d): LLM Course A hands-on, end-to-end course on building, evaluating, and deploying LLM applications. github.com/mlabonne/llm...
- [X · @danielishakutv · 2026-10-31](https://x.com/danielishakutv/status/2116583026507366559): Want to get really good at generative AI? Save these 10 GitHub repos. 👇 The diagram is where they lead: AI agents with memory, tools, and a human still in control.
- [Hacker News · udev4096 · 2023-11-30](https://news.ycombinator.com/item?id=38473485): LLM Course
- [Bluesky · @supaluck5599.bsky.social · 2026-10-30](https://bsky.app/profile/supaluck5599.bsky.social/post/3mz34ddcfuk2o): github.com/mlabonne/llm...

## Get it

- [Open mlabonne/llm-course on GitHub](https://github.com/mlabonne/llm-course)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/8284a15c-6d2b-42a9-b3b4-c852a46d3e83)
- [Its page on Mindket](https://mindket.com/p/8284a15c-6d2b-42a9-b3b4-c852a46d3e83)