llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
GraphCanon updated 1w · GitHub synced 1w · 29 views this month
Decision brief
The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to
Good fit when
- - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge
- - If you are interested in using the latest techniques for building state-of-the-art LLMs
Avoid when
- - If you only require a quick introduction to LLMs without deep dive into core components
- - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI
- Requirements:
- Course materials are available in Colab notebooks; access requires a Google account
Observed Jul 11, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (183d since push)
- As of 1w
- Provenance
- Not a fork · Personal account
- As of 1w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/mlabonne/llm-courseHow it fits your stack(28)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Alternative
Integrates
Depends on
Related
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides a guided course on LLMs divided into three parts: fundamentals, building the best possible models using latest techniques, and creating/deploying applications. Includes materials like notebooks for automated evaluation, model merging, fine-tuning in the cloud, and quantization.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 8, 2026)
Fundamentals** is optional and covers fundamental knowledge about mathematics, Python, and neural networks.Source link
Tags
README
𝕏 Follow me on X • 🤗 Hugging Face • 💻 Blog • 📙 LLM Engineer's Handbook
The LLM course is divided into three parts:
- 🧩 LLM Fundamentals is optional and covers fundamental knowledge about 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.
[!NOTE] Based on this course, I co-wrote the LLM Engineer's Handbook, 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.
📝 Notebooks
A list of notebooks and articles I wrote about LLMs.
Toggle section (optional)
Tools
| Notebook | Description | Notebook |
|---|---|---|
| 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod | |
| 🥱 LazyMergekit | Easily merge models using MergeKit in one click. | |
| 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. | |
| ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. | |
| 🌳 Model Family Tree | Visualize the family tree of merged models. | |
| 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. | |
| ✂️ AutoAbliteration | Automatically abliteration models with custom datasets. | |
| 🧼 AutoDedup | Automatically deduplicate datasets using the Rensa library. |
Fine-tuning
| Notebook | Description | Article | Notebook |
|---|---|---|---|
| Fine-tune Llama 3.1 with Unsloth | Ultra-efficient supervised fine-tuning in Google Colab. | Article | <a href="https://colab.research.google.c |
For agents
This page has a .md twin and JSON over the API.