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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
| Date | Stars |
|---|---|
| 2026-07-24 | 81203 |
| 2026-07-25 | 81215 |
| 2026-07-28 | 81215 |
| 2026-07-30 | 81215 |
| 2026-07-31 | 81335 |
| 2026-08-06 | 81335 |
Today
— stars today
This week
+120 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.15%/day
<div align="center">
<img src="img/banner.png" alt="LLM Course">
<p align="center">
𝕏 <a href="https://twitter.com/maximelabonne">Follow me on X</a> •
🤗 <a href="https://huggingface.co/mlabonne">Hugging Face</a> •
💻 <a href="https://mlabonne.github.io/blog">Blog</a> •
📙 <a href="https://packt.link/a/9781836200079">LLM Engineer's Handbook</a>
</p>
</div>
<br/>
<a href="https://a.co/d/a2M67rE"><img align="right" width="25%" src="https://i.imgur.com/7iNjEq2.png" alt="LLM Engineer's Handbook Cover"/></a>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 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.
> [!NOTE]
> Based on this course, I co-wrote the [LLM Engineer's Handbook](https://packt.link/a/9781836200079), a hands-on book that covers an end-to-end LLM application from design to deployment. The LLM course will always stay free, but you can support my work by purchasing this book.
For a more comprehensive version of this course, check out the [DeepWiki](https://deepwiki.com/mlabonne/llm-course/).
## 📝 Notebooks
A list of notebooks and articles I wrote about LLMs.
<details>
<summary>Toggle section (optional)</summary>
### Tools
| Notebook | Description | Notebook |
|----------|-------------|----------|
| 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) | Automatically evaluate your LLMs using RunPod | <a href="https://colab.research.google.com/drive/1Igs3WZuXAIv9X0vwqiE90QlEPys8e8Oa?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> |
| 🥱 LazyMergekit | Easily merge models using MergeKit in one click. | <a href="https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> |
| 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. | <a href="https://colab.research.google.com/drive/1TsDKNo2riwVmU55gjuBgB1AXVtRRfRHW?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> |
| ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. | <a href="https://colab.research.google.com/drive/1b6nqC7UZVt8bx4MksX7s656GXPM-eWw4?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> |
| 🌳 Model Family Tree | Visualize the family tree of merged models. | <a href="https://colab.research.google.com/drive/1s2eQlolcI1VGgDhqWIANfkfKvcKrMyNr?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> |
| 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. | <a href="https://colab.research.google.com/drive/1LcVUW5wsJTO2NGmozjji5CkC--646LgC"><img src="img/colab.svg" alt="Open In Colab"></a> |
| ✂️ AutoAbliteration | Automatically abliteration models with custom datasets. | <a href="https://colab.research.google.com/drive/1RmLv-pCMBBsQGXQIM8yF-OdCNyoylUR1?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> |
| 🧼 AutoDedup | Automatically deduplicate datasets using the Rensa library. | <a href="https://colab.research.google.com/drive/1o1nzwXWAa8kdkEJljbJFW1VuI-3VZLUn?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> |
### Fine-tuning
| Notebook | Description | Article | Notebook |
|---------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|
| Fine-tune Llama 3.1 with Unsloth | Ultra-efficient supervised fine-tuning in Google Colab. | [Article](https://mlabonne.github.io/blog/posts/2024-07-29_Finetune_Llama31.html) | <a href="https://colab.research.google.com/drive/164cg_Excerpt of 56,528 characters
Read on GitHub
Maxime Labonne · Liquid AI · United Kingdom
94
Pietro Monticone · Harmonic · United States
1
Shrinija Kummari · JustPaid · United States
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5f7671c8ad2fb148, topic:course, topic:roadmap, readme:a list of
matched fp:5f7671c8ad2fb148, topic:large-language-models, topic:llm