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What is re-zero---starting-llm-?

jiaran-king/re-zero---starting-llm- — explained in plain English

Analysis updated 2026-05-18

91PythonAudience · researcherComplexity · 1/5Setup · easy

In one sentence

A personal Obsidian knowledge base of Chinese language notes on large language model research, covering topics from transformer basics to RLHF and inference optimization.

Mindmap

mindmap
  root((LLM notes vault))
    What it does
      Personal LLM study wiki
      Written in Obsidian
      Chinese language notes
    Tech stack
      Python helper script
      Markdown files
      Obsidian vault format
    Use cases
      Study roadmap for LLM topics
      Concept reference lookup
      Track research paper notes
    Audience
      Researchers
      LLM learners
      Obsidian users

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Browse a structured map of LLM concepts before reading a specific paper or framework.

USE CASE 2

Use the topic maps as a study roadmap for learning transformer architecture, fine tuning, and alignment.

USE CASE 3

Reference the concept notes as a refresher on terms like LoRA, RLHF, or KV caching.

What is it built with?

PythonObsidianMarkdown

How does it compare?

jiaran-king/re-zero---starting-llm-bbuf/kernel-pilotdjango-haystack/queued_search
Stars919090
LanguagePythonPythonPython
Last pushed2020-08-21
MaintenanceDormant
Setup difficultyeasyhardmoderate
Complexity1/55/53/5
Audienceresearcherdeveloperdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

Open the repository root in Obsidian to browse it as a linked vault, no installation or build step is required.

No license is specified, so the contents should not be treated as freely reusable without checking with the author first.

So what is it?

This repository is a personal knowledge base for studying large language models, built as an Obsidian vault and written in Chinese. It is not a piece of software you install or run. Instead, it is a structured set of notes the author uses to organize what they learn about LLMs: research papers, engineering frameworks, and hands on experiments. The notes are arranged in layers. Raw source material gets logged first, then distilled into an index, then rewritten into standalone concept notes, then linked together into topic maps, and finally connected to project and experiment write ups. Folders reflect this pipeline, with separate directories for the homepage and index, topic maps, concept notes, project notes, experiment logs, source excerpts, note templates, and an inbox for material that has not been sorted yet. A small Python script in the tools folder helps convert Obsidian style links and canvas diagrams into a form that renders properly on GitHub. The topics covered include core transformer architecture (attention, tokenization, positional encoding), pretraining and scaling laws, parameter efficient fine tuning methods like LoRA and QLoRA, alignment techniques such as RLHF, PPO, and DPO, and inference optimization topics like KV caching and Flash Attention. There are also notes on training infrastructure tools such as vLLM and Ray, plus summaries of specific papers and models like Qwen and DeepSeek. A new reader is pointed toward a suggested reading order: start with the overview page, move to the learning map to pick a topic, then drill into that topic's map and its underlying concept notes, tracing back to source material only when needed. The repository includes a maintenance guide describing how new material should be filed so the structure stays organized as it grows. This is best understood as a public study log rather than a tool or library. There is no installed application, API, or command line interface to use. The repository does not specify an open source license, so its contents should not be treated as freely reusable without checking with the author first.

Copy-paste prompts

Prompt 1
Summarize the reading order suggested in this repository's README for someone new to LLM research.
Prompt 2
Explain what the topic map folder in this repository is meant to do compared to the concept notes folder.
Prompt 3
List the inference optimization topics covered in this notes repository.
Prompt 4
Describe how this repository organizes raw source material into reusable concept notes.

Frequently asked questions

What is re-zero---starting-llm-?

A personal Obsidian knowledge base of Chinese language notes on large language model research, covering topics from transformer basics to RLHF and inference optimization.

What language is re-zero---starting-llm- written in?

Mainly Python. The stack also includes Python, Obsidian, Markdown.

What license does re-zero---starting-llm- use?

No license is specified, so the contents should not be treated as freely reusable without checking with the author first.

How hard is re-zero---starting-llm- to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is re-zero---starting-llm- for?

Mainly researcher.

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