Hands-On-Large-Language-Models-CN: The Chinese Edition of the Alammar and Grootendorst Book
中文翻译的 Hands-On-Large-Language-Models (hands-on-llms)，动手学习大模型
At a glance
- What is it?
- A community Chinese translation of Hands-On Large Language Models, with annotated notebooks, video walkthroughs and a notebook path that does not require a VPN. It is a study companion, not a library you install into a service.
- Who is it for?
- Adopt this if you read Chinese and want to work through the Alammar and Grootendorst book in a network environment where Google Colab is slow, or if you want the author's added code comments alongside the original text. Do not adopt it if you need a maintained Python package, an API, or English-language material; the repository is a notebook collection, and the README itself points English readers to the original book.
- Can I use it commercially?
- Yes. Apache-2.0 is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 74 days ago.
- What is it written in?
- Mainly Jupyter Notebook, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on October 1, 2026, and from our analysis. They are not legal advice.
Editorial analysis
What Hands-On-Large-Language-Models-CN actually is
This repository is a Chinese-language edition of the book Hands-On Large Language Models, whose original authors are Jay Alammar and Maarten Grootendorst. The README states this directly and links to the upstream repository at HandsOnLLM/Hands-On-Large-Language-Models, recommending that readers with good English support the original. The translation is maintained by bbruceyuan, whose homepage is listed as yuanchaofa.com.
The audience is narrow and specific. It is for readers who want to study large language models through the book's chapter structure but who read Chinese more comfortably than English, and for readers in mainland China whose connection to Google Colab is slow. The README frames the second point as a practical one: it says domestic users may find Colab access slow, and offers a notebook path hosted on OpenBayes instead. That is the real differentiator here, not the translation alone.
What it is not: a package, a framework, or a service. The primary language is Jupyter Notebook, and the top-level layout is a sequence of chapter directories from chapter01 through chapter12, plus assets, images and a few environment files. If you are looking for something to import, this is the wrong repository.
How the Chinese edition differs from the upstream book
The README lists three features of the Chinese version. First, the code carries more detailed comments, and the author says he added his own understanding in parts of the content. That is the kind of annotation that is hard to get from a machine translation and is the main reason to prefer this edition over running the upstream notebooks through a translator.
Second, there are notebook versions suited to the domestic network environment, which the README says do not require circumventing the firewall and are mainly faster. These are hosted on OpenBayes, and the README provides a container link per chapter in a table with columns for the chapter, the Google Colab badge, the Chinese runnable notebook, and video explanations.
Third, there is a companion video series, published on both YouTube and Bilibili under the account chaofa用代码打点酱油. The README's chapter table links a Bilibili video and a YouTube video for each chapter it covers. Note the promotional elements in the README: badges for ApeCode.ai and ApeRouter, and registration links for OpenBayes and AIStackDC that the author says grant both parties extra free hours. These are referral links. They do not affect the notebooks, but you should know they are there before you click.
Running a chapter notebook: environment and first steps
The repository ships two dependency files with different purposes, and this is the first thing to get right. requirements.txt pins exact versions for the book's chapters, including torch 2.3.1, transformers 4.41.2 and sentence-transformers 3.0.1, plus chapter-specific packages such as bertopic, faiss-cpu and llama_cpp_python. The pyproject.toml, by contrast, declares requires-python >=3.12 and looser floors such as torch>=2.8.0 and transformers>=4.57.0. The two are not the same environment. If you follow the book's code, the pinned file is the one that matches what the chapters were written against.
A minimal setup with the pinned requirements looks like this:
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
jupyter labAfter this you should have a JupyterLab server running locally, with the chapter directories visible in the file browser. The README gives no explicit local install command, so this is the standard path implied by the presence of requirements.txt and the jupyterlab pin inside it.
If you prefer the managed route, the README's instruction is to copy the author's OpenBayes container for the chapter you want and run the notebook there. The chapter table links one container per chapter, for example the chapter01 container at openbayes.com/console/bbruceyuan/containers/pfiQnfIjPo6. Copying the container gives you a preconfigured environment, which avoids the dependency mismatch above entirely.
The upstream project also documents a dependencies.sh and environment.yml at the top level, which suggests conda is a supported alternative, though the README of this repository does not spell out a conda workflow.
Where the notebook-first design breaks down
The most concrete limitation is the dependency split already described. A reader who runs pip install -e . or otherwise resolves pyproject.toml will get newer torch and transformers than the book's code expects, and chapters that rely on specific model APIs or on bitsandbytes and peft versions may behave differently. The pyproject.toml description field is literally the placeholder text "Add your description here", which tells you how much attention that file has received relative to requirements.txt.
Second, several chapters depend on cloud API keys. The requirements list openai and cohere, and pyproject.toml adds langchain-deepseek. The README does not document how keys are supplied, so expect to configure environment variables yourself and to consult the individual notebook for the expected names.
Third, hardware. The README recommends Google Colab with a 16GB T4 GPU as the simplest setup and says the examples were mainly built and tested there. Chapters that load larger models will not run comfortably on a laptop CPU. The OpenBayes route is the answer the author gives, and the README notes the platform's free CPU and GPU hours, but that is a third-party service with its own limits, not a property of this repository.
Finally, this is a book companion. There is no versioning story, no changelog, and no releases. If a notebook breaks against a newer library, your options are to pin versions or to read the upstream repository's issue tracker.
Compared with the original English repository
The direct alternative is the upstream HandsOnLLM/Hands-On-Large-Language-Models repository, which this project translates. The difference in approach is straightforward: upstream is the source of truth, keeps the English text and code, and is the version the README tells you to support. This project adds Chinese prose, heavier code comments, per-chapter video walkthroughs, and the OpenBayes notebook path.
The trade-off is maintenance surface. A translation has to track upstream changes, and any divergence between the two is a place where a reader can be misled. The README does not describe a sync policy or a translation status per chapter, so you cannot tell how current each chapter is relative to upstream. If your English is good enough, upstream is the safer default. If it is not, or if Colab is impractical for you, this edition is the more usable one.
The related searches around this project mostly surface other LLM books, such as Build a Large Language Model (From Scratch) by Sebastian Raschka and the LLM Engineer's Handbook. Those are different in kind. Raschka's book builds a model from scratch, while this one, following Alammar and Grootendorst, works through using and adapting existing models across classification, clustering, prompt engineering, semantic search, and fine-tuning.
Licence and the cost of keeping up
The repository is licensed Apache-2.0, which permits commercial and non-commercial use, modification and redistribution, subject to the licence's notice and attribution conditions. The book text itself is a separate matter: the translation reproduces content from a published book, and the README directs readers to support the original authors. Apache-2.0 covers the repository's code and notebooks as distributed here; it does not grant you rights to the underlying book's text beyond what the licence on that material allows. That is a question for the publisher, not for this repository, and nothing here should be read as legal advice.
The upgrade cost is low in the sense that there is nothing to upgrade: you clone the repository and read. It is high in the sense that the environment is pinned and will drift. The last push to the repository was on 2026-07-19, so the project has seen changes within the last two months, but there are no releases and no changelog, so you cannot tell what those changes were without reading the commit history. Treat the pinned requirements.txt as the contract and expect to spend time resolving package conflicts if you deviate from it.
Editorial conclusion
Adopt this if you read Chinese and want to work through the Alammar and Grootendorst book in a network environment where Google Colab is slow, or if you want the author's added code comments alongside the original text. Do not adopt it if you need a maintained Python package, an API, or English-language material; the repository is a notebook collection, and the README itself points English readers to the original book. Before starting, verify which chapter notebooks match the dependencies you intend to use, since requirements.txt pins older versions than pyproject.toml, and check the chapter table in the README for the OpenBayes container link that corresponds to the chapter you want.
Frequently asked questions
What is the large language model in Chinese?
The repository's title gives the Chinese rendering as 动手学大模型, and the project describes itself as the Chinese translation of Hands-On Large Language Models. The term appears throughout the chapter titles, which cover tokens, embeddings, transformers and prompt engineering.
What are the top 5 LLM models?
The repository does not rank or list models. It is a translation of a book about working with language models, and its dependency files reference providers such as OpenAI, Cohere and DeepSeek but do not name a top-five list.
Is there a PDF version of the book "Hands-On Large language Models"?
The repository does not mention a PDF. It distributes Jupyter notebooks organized by chapter, and the README links to the original English repository rather than to a PDF edition.
What is the content of the book "Hands-On Large language Models" by Alammar & Grootendorst?
The README's chapter table runs from an introduction to language models through tokens and embeddings, looking inside transformer LLMs, text classification, text clustering and topic modeling, prompt engineering, and further chapters up to chapter12. Each chapter has a corresponding notebook and, for the chapters listed, a Bilibili and YouTube video.
Official sources
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