llm-course is a README and an image folder, and every notebook lives behind a Google Drive link

The repository holds a README, a license and an image folder

The complete top-level listing of this project is three entries: LICENSE, README.md, and img/. There is no package.json, no requirements file, no notebook, no dataset and no test directory. The repository's primary language is reported as unknown. Every notebook in the course is an outbound link to a Google Colab session, and each one is addressed by a Drive file identifier rather than a path, for example a link of the form colab.research.google.com/drive/1o_w0KastmEJNVwT5GoqMCciH-18ca5WS. Consequence for a reader: the material you came for is the part this repository does not hold. You cannot clone the course, diff a notebook between versions, vendor it, or run it offline, and a Drive permission change or a deleted file breaks the link with nothing in this repository to fall back on.

The last commit is dated 2026-02-05 and the newest dated article is from July 2024

The last push to the default branch is dated 2026-02-05, and there are no GitHub releases, so nothing here has been versioned or tagged. The linked writing carries its own dates in the URLs, and the ones visible in the tables run from 2024-04-19 through 2024-07-29, covering an ORPO fine-tuning post, a Llama 3.1 fine-tuning post and a weight quantization introduction. So the writing the README points at is around two years older than the most recent commit, and the Colab links carry no date at all, which means you cannot tell from this file whether a given notebook has been revisited for a newer model generation or has been sitting as it was. Consequence: a reader has three clocks running at different speeds, a repository commit date, article dates in URLs, and undated live notebooks, and the file does not reconcile them or say which one reflects the current state of a notebook.

The free course and the sold book are the same material, and the file says so

There is a note directly under the three-part outline. It says the LLM Engineer's Handbook was co-written based on this course, describes it as a hands-on book covering an end-to-end LLM application from design to deployment, and then makes the arrangement explicit: the LLM course will always stay free, but you can support the work by purchasing the book. A separate line points at a DeepWiki as a fuller version of the course itself. So the same body of work appears three times, as a free repository, as a paid book, and as a third-party wiki. Consequence for a reader: the free tier is not a teaser for a larger free tier, it is the complete stated offering, and the paid version is a different format of the same material rather than new depth. If you were hoping the repository is the abridged version, the file says the opposite.

The fine-tuning table spans Llama 2 through Llama 3.1 without separating them

The fine-tuning section is one table of six rows and it mixes base model generations in a single list. There is a notebook for fine-tuning Llama 3.1 with Unsloth, one for Llama 3 with ORPO, one for Mistral-7b with DPO, one for Mistral-7b with QLoRA, one for CodeLlama using Axolotl, and one for Llama 2 with QLoRA. Two are labelled free-tier, with the Mistral QLoRA row described as a supervised fine-tune in a free-tier Google Colab with TRL, and the Llama 2 row described as a step-by-step guide in Google Colab. The quantization table repeats the spread, with one row on Llama 2 and llama.cpp producing GGUF uploads and another on EXL2. Consequence for a reader: nothing in the table tells you which generation a technique was written against, so you are choosing a technique and a base model at the same time with no way to tell from the row whether the two belong together. The oldest entry is two generations behind the newest.

Two of the eight tools are separate repositories and the rest are Colab sessions

The tools table lists eight entries and they are not the same kind of thing. LLM AutoEval is the only one with a repository of its own, described as automatically evaluating your LLMs using RunPod. The other seven are Colab links: LazyMergekit for merging models with MergeKit in one click, LazyAxolotl for fine-tuning in the cloud with Axolotl, AutoQuant for quantizing into GGUF, GPTQ, EXL2, AWQ and HQQ, Model Family Tree for visualizing merged model families, ZeroSpace for generating a Gradio chat interface on a free ZeroGPU, AutoAbliteration for ablating models with custom datasets, and AutoDedup for deduplicating datasets with the Rensa library. Four of them delegate to a named external service, RunPod, Hugging Face uploads, ZeroGPU and the Rensa library. Consequence: the tooling section is a directory of third-party integrations as much as it is course material, and running any of it means an account somewhere else, with no version recorded for any of the eight.

The first part is labelled optional and the other two carry no such note

The outline is three numbered parts, and only the first is qualified. LLM Fundamentals is optional and covers fundamental knowledge about mathematics, Python, and neural networks. The LLM Scientist is described as focusing on building the best possible LLMs using the latest techniques. The LLM Engineer focuses on creating LLM-based applications and deploying them. Read the second and third together and neither says who it is for, so the only audience signal in the entire structure is attached to the part you are told you can skip. Consequence for a reader: someone with a mathematics background and someone without are pointed at the same two parts with no guidance on where to start, and the word that carries the most weight in the second description, the latest techniques, is also the one with no date attached to it in a repository whose newest visible article is from 2024.

Editorial conclusion

llm-course is a good map of the field and a poor curriculum you can pin, version or run offline, because the substance lives in Colab notebooks and blog posts this repository does not contain. Treat it as an index and bookmark what you use, since a Drive link is the only copy. Before you rely on a notebook, check which base model generation it targets, since the fine-tuning list spans Llama 2 through Llama 3.1, and read the linked article rather than assuming the notebook has been updated alongside it.

Frequently asked questions

Official sources

1. Official documentation
2. Official README
3. Project repository