anaksor-ocr
Sokheng · Kit
Reads Khmer and English text from documents, receipts and photos of signs, using a detector for text lines and a recogniser for each line. Suits developers who…
Lets Chinese readers study the HuggingFaceTB guide to training language models in Simplified Chinese, charts included. An unofficial translation for people who build or learn about LLMs.
The Smol Training Playbook(简体中文) is a kit in the Education category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.
pip install -U huggingface_hub hf download fat-pig/smol-training-playbook-zh --repo-type space --local-dir smol-training-playbook-zh cd smol-training-playbook-zh
This Space is an unofficial Simplified Chinese translation of The Smol Training Playbook, a guide to training language models from the HuggingFaceTB team. It is a static website you read in a browser, with the full text and the words inside 79 interactive charts translated. The content matches a snapshot of the upstream guide.
Chinese readers who train or study language models and want the guide in their own language. It also suits teams who share the guide with colleagues who prefer Chinese.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| AI and ML | Model training teams | Keep the guide open as a Chinese reference | Read a chapter and follow its charts |
| Research | Researchers and students | Study training ideas with interactive charts | Open a chart and read its Chinese labels |
| Education | Teachers and study groups | Hand a Chinese version to a class | Send the Space page to the class |
| Community | Translators | Check wording choices and report problems | Open a Discussion that names the page section |
Readers who are comfortable in English can skip it and use the original guide.
The site works in these places:
| Use case | Who | What they get back |
|---|---|---|
| Read the guide online | Chinese-speaking ML engineers | The full guide text in Chinese, with its charts |
| Study the charts | Students | Interactive charts whose inner text is translated |
| Report a translation problem | Community members | A Discussion thread on the Space, with the section name |
Reading online needs nothing. For a local copy you need a few things:
The steps download the Space and then open its main page by hand.
pip install -U huggingface_hub hf download fat-pig/smol-training-playbook-zh --repo-type space --local-dir smol-training-playbook-zh cd smol-training-playbook-zh
Open the Space page on Hugging Face, or open index.html from the downloaded folder in a browser, and read the guide as a normal web page. The charts react to the pointer, as in the English original.
| Tool | What it does |
|---|---|
| hf download | Copies the Space's files into a folder on your computer |
| index.html | The main page you open in a browser |
You open the Space page and scroll to a chapter. The text reads in Chinese, while key terms such as loss, batch size and tokenizer stay in English, as the translators chose. The labels inside the charts are Chinese too.
If you are new to this, open the Space page and read the first chapter. You do not need to install anything. A local copy is only useful if you want the files offline.
If you know the field, the translators kept code, command lines, API and parameter names, people's names, paper titles and addresses in the original. Terms like pretraining and ablation stay in English, which matches common usage in Chinese technical circles. The translated source sits in app/, built with astro build, and the built files are committed so Hugging Face can serve them as a static site. The upstream guide is a Docker Space, and the template follows the Distill tradition of interactive writing.
The PDF and LaTeX export and the English interactive demo frame are outside the translation.
Lets Chinese readers study the HuggingFaceTB guide to training language models in Simplified Chinese, charts included. An unofficial translation for people who build or learn about LLMs.
Made by fat-pig on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.
What it is: This Space is an unofficial Simplified Chinese translation of The Smol Training Playbook, a guide to training language models from the HuggingFaceTB team. It is a static website you read in a browser, with the full text and the words inside 79 interactive charts translated. The content matches a snapshot of the upstream guide.
Who it suits: Chinese readers who train or study language models and want the guide in their own language. It also suits teams who share the guide with colleagues who prefer Chinese.
Industry | Who uses it | How they use it | Example
AI and ML | Model training teams | Keep the guide open as a Chinese reference | Read a chapter and follow its charts
Research | Researchers and students | Study training ideas with interactive charts | Open a chart and read its Chinese labels
Education | Teachers and study groups | Hand a Chinese version to a class | Send the Space page to the class
Community | Translators | Check wording choices and report problems | Open a Discussion that names the page section
Readers who are comfortable in English can skip it and use the original guide.
Where to use it: The site works in these places:
Use case | Who | What they get back
Read the guide online | Chinese-speaking ML engineers | The full guide text in Chinese, with its charts
Study the charts | Students | Interactive charts whose inner text is translated
Report a translation problem | Community members | A Discussion thread on the Space, with the section name
How to set it up: Reading online needs nothing. For a local copy you need a few things:
The steps download the Space and then open its main page by hand.
pip install -U huggingface_hub
hf download fat-pig/smol-training-playbook-zh --repo-type space --local-dir smol-training-playbook-zh
cd smol-training-playbook-zh
How to use it: Open the Space page on Hugging Face, or open index.html from the downloaded folder in a browser, and read the guide as a normal web page. The charts react to the pointer, as in the English original.
Tool | What it does
hf download | Copies the Space's files into a folder on your computer
index.html | The main page you open in a browser
Example: You open the Space page and scroll to a chapter. The text reads in Chinese, while key terms such as loss, batch size and tokenizer stay in English, as the translators chose. The labels inside the charts are Chinese too.
For newcomers and experts: If you are new to this, open the Space page and read the first chapter. You do not need to install anything. A local copy is only useful if you want the files offline.
If you know the field, the translators kept code, command lines, API and parameter names, people's names, paper titles and addresses in the original. Terms like pretraining and ablation stay in English, which matches common usage in Chinese technical circles. The translated source sits in app/, built with astro build, and the built files are committed so Hugging Face can serve them as a static site. The upstream guide is a Docker Space, and the template follows the Distill tradition of interactive writing.
Translators edit the article and chart files in app/
astro build makes index.html, _astro and data
The built files are committed to the Space
Hugging Face serves them as a static site
Good to know: The PDF and LaTeX export and the English interactive demo frame are outside the translation.
Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.
README.md 5 KB
Listed Hugging Face Spaces in the same category that share the most tags with this one.
| Product | Likes | Built with | License | Last change |
|---|---|---|---|---|
| The Smol Training Playbook(简体中文) This page | 1 | static | cc-by-4.0 | |
| The Ultra-Scale PlaybookShares static | 4,131 | static | apache-2.0 | |
| LLM训练终极指南 | The Ultra-Scale PlaybookShares static | 281 | static | apache-2.0 | |
| Attention VisualizationShares static | 200 | static | apache-2.0 | |
| Number Tokenization BlogShares static | 127 | static | mit | |
| Train the agent slidesShares static | 20 | static | apache-2.0 |
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