# llm-action: A Chinese-Language Tutorial Index for Training, Inference and Serving LLMs

> llm-action is a curated collection of LLM engineering tutorials and companion code, organised by topic rather than as a runnable framework. It is most useful if you read Chinese and want walkthroughs of LoRA, QLoRA, RLHF and inference stacks; it is not a library you install.

**liguodongiot/llm-action** — 本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）

- Repository: https://github.com/liguodongiot/llm-action
- Website: https://www.zhihu.com/column/c_1456193767213043713
- Stars: 25,062 · Forks: 2,846
- Language: HTML
- License: Apache-2.0
- Published: 2026-09-09 · Updated: 2026-09-09 · Language: en
- Canonical page: https://hysenlabs.com/projects/liguodongiot-llm-action

## What llm-action actually is, and the reader it targets

The repository describes itself as sharing large-model technical principles and hands-on experience, covering what its description calls large-model engineering and application deployment. That phrasing matters: llm-action is a knowledge base, not a runtime. The top level is a set of topic directories (llm-train/, llm-inference/, llm-compression/, llm-eval/, llmops/, llm-alignment/, llm-application/, paper/, docs/, faq/) plus shell helpers such as git-pull-push.sh and mkdir-dir-file.sh. The primary language reported for the repository is HTML, which fits a project whose main artefacts are rendered articles and tables.

The README is a table of contents. Each row pairs a tutorial link (mostly to a Zhihu column, with mirrors on Juejin and CSDN) with a companion code directory inside the repository when one exists. The homepage points at the same Zhihu column. So the intended reader is someone who wants a guided path through a technique, with code to copy, rather than someone who wants to add a dependency to a requirements file.

The topic tags tell you the intended scope: llm, llm-inference, llm-serving, llm-training, llmops. If your interest is any of the surrounding engineering disciplines rather than model architecture research, the index probably has a section for it.

## How the index is organised: tutorials first, code second

The mechanism is a two-column contract. The left side is prose: a Zhihu article that explains the technique and walks through the commands. The right side is a directory in the repository holding the scripts that the article references. For example, the row for Alpaca with full fine-tuning points at llm-train/alpaca, and the LoRA row for Alpaca points at llm-train/alpaca-lora. QLoRA on LLaMA 7B and 65B points at llm-train/qlora. The GaLore row is different: it links a single file, llm-train/galore/torchrun_main.py, rather than a directory. That inconsistency is worth knowing before you go looking for a README inside a folder that does not have one.

Not every row has code. The BELLE, Vicuna and MiniGPT-4 rows carry N/A in the code column, so those entries are article-only. If you are choosing what to reproduce, filter on that column first.

The directory tree extends well beyond the training table. llm-inference/, llm-serving topics, llm-compression/ with its quantisation, pruning, distillation and low-rank decomposition subsections, llm-eval/ split into quality evaluation and inference performance stress testing, and llm-localization/ for domestic hardware adaptation all appear at the top level. The README organises these as nested bullet lists with emoji markers, and the training table is the only part that is tabular. So navigation is manual: you scan the contents list, then open the directory.

## Installing nothing: cloning the repository and running one example

There is no package to install. The README does not document a pip install, an npm package, a Docker image or a release artefact; the releases list is empty. What you get is the repository itself, which you clone, and then you follow whichever tutorial you came for. The shell helpers at the top level, git-pull-push.sh and mkdir-dir-file.sh, are maintenance scripts for the repository author, not setup scripts for a user.

The first real step is to get the code locally, then locate the directory named in the table row you care about.

## Cloning and locating a tutorial's companion code

Clone the repository and list the training subdirectories. The README's table maps each technique to one of these paths, so this is how you check whether the tutorial you want actually ships code.

```bash
git clone https://github.com/liguodongiot/llm-action.git
cd llm-action
ls llm-train/
```

The README names llm-train/alpaca, llm-train/alpaca-lora, llm-train/chatglm, llm-train/chatglm-lora, llm-train/chinese-llama-alpaca, llm-train/deepspeedchat, llm-train/qlora and llm-train/galore among the companion code locations. If the directory you expected is absent, the corresponding README row probably reads N/A.

## Where llm-action breaks down as a source

The most concrete limitation is language. The README, the contents list and the tutorials are written in Chinese. An English-only reader can still clone the repository and read the scripts, but the explanatory layer, which is the point of the project, is not available in English. Treat it as a Chinese-language resource and plan accordingly.

The second limitation is that the index is not a test suite. Nothing in the README states that the companion code is executed against current library versions. Many of the linked tutorials target specific model families (Alpaca, ChatGLM, BELLE, Vicuna, MiniGPT-4, Chinese-LLaMA-Alpaca) and specific fine-tuning methods. Reproducing an older walkthrough today means resolving dependency drift yourself; the repository does not promise a pinned environment, and the absence of releases means there is no version tag to check out that corresponds to a known-good state.

The third is coverage asymmetry. Some rows have directories, some have a single file, and some have nothing. That is a signal about effort allocation, not a defect, but it means you cannot assume every topic in the contents list is backed by runnable code. The paper/ and docs/ directories suggest the project also collects reference material that is not meant to be executed at all.

Finally, this is the wrong tool if you want an inference server. llm-action discusses inference frameworks and serving, but it does not provide one. If your task is to stand up an OpenAI-compatible endpoint today, you want an actual serving project, and llm-action is reading material you consult alongside it.

## Comparing it with a cookbook-style, code-first resource

The closest alternative in shape is a cookbook-style repository: a collection of notebooks where each recipe is self-contained, runnable and versioned in the same tree as its documentation. The difference in approach is where the explanation lives. In a cookbook, the notebook is the documentation; you execute it and read the markdown cells. In llm-action, the explanation lives in an external article on Zhihu, and the repository holds the code that the article references. That split has a practical consequence: the article can be updated without the code changing, and the code can drift while the article stays accurate, and nothing in the repository records which happened.

A second difference is scope. A cookbook usually covers one layer of the stack, typically application patterns against a hosted API. llm-action spans training, fine-tuning, distributed parallelism, compression, evaluation, inference optimisation and LLMOps, and includes sections on domestic hardware adaptation and AI compilers. Breadth is the trade: you get pointers into many areas, and you get less depth per area than a dedicated repository would give.

A third is the language of the surrounding ecosystem. Cookbook-style projects in this space are usually English-first. llm-action is Chinese-first, which is a genuine advantage if your team reads Chinese, because the tutorials discuss model families and deployment contexts that English-language material covers less often.

## Maintenance, licensing and what an upgrade costs you

The repository is not archived, and the last push was on 2026-07-19. That is recent enough that the project is still being touched, but there is no release history, so there is no changelog to read and no version number to pin. Upgrading, in this context, does not mean bumping a dependency; it means pulling the latest commits and re-reading whichever tutorial changed. The cost of that is your time, not a compatibility matrix.

Licensing is Apache-2.0, stated in the LICENSE file at the repository root. For a documentation-and-code collection, that is permissive: you can reuse the code and adapt it. Two caveats belong here rather than in a legal opinion. First, the tutorials link out to Zhihu, Juejin and CSDN articles, and the licence on the repository does not automatically extend to that external prose. Second, several tutorials concern third-party model weights and frameworks with their own licences; the repository's Apache-2.0 grant covers what is in the repository, not the models you download to follow along. Check the upstream licence for any model you fine-tune.

## Conclusion

Adopt llm-action as a reading list if you or your team reads Chinese and you want concrete walkthroughs of LoRA, QLoRA, RLHF or inference serving before committing GPU time. Do not adopt it if you need a maintained library with a versioned API, or if you need English documentation: the README and the tutorial tables are in Chinese. Before relying on any single tutorial, open the matching directory under llm-train/ or llm-inference/ and check whether it still contains runnable code, because the README table lists several rows whose code column reads N/A.

## FAQ

### What does LLM stand for in the context of llm-action?

The repository's topics and contents list use LLM for large language model, and the tutorials cover training, fine-tuning, inference and serving of those models. The README does not spell the acronym out.

### What does LLM mean in AI?

In this repository the term is used for the large language models that the tutorials train, fine-tune and serve, including families such as Alpaca, ChatGLM, BELLE and Vicuna. The README does not provide a separate definition.

### Is llm-action a library I install, or a collection of tutorials?

It is a collection of tutorials. The README is a table of contents that pairs each article with a companion code directory, and it documents no package installation, Docker image or release.

### Which fine-tuning methods does llm-action cover?

The training table lists full fine-tuning, LoRA, QLoRA, P-Tuning v2, RLHF and GaLore across models ranging from 6B to 65B. Some rows include companion code directories and others list N/A.

### What language are the llm-action tutorials written in?

Chinese. The README and contents list are in Chinese, and the tutorial links point to a Zhihu column with mirrors on Juejin and CSDN.

## Sources

- [Issues](https://github.com/liguodongiot/llm-action/issues)
- [License: Apache-2.0](https://github.com/liguodongiot/llm-action/blob/main/LICENSE)
- [liguodongiot/llm-action on GitHub](https://github.com/liguodongiot/llm-action)
- [Project website](https://www.zhihu.com/column/c_1456193767213043713)
- [README](https://github.com/liguodongiot/llm-action/blob/main/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/liguodongiot-llm-action