# luhengshiwo/LLMForEverybody

每个人都能看懂的大模型知识分享，LLMs春/秋招大模型面试前必看，让你和面试官侃侃而谈

Repository: https://github.com/luhengshiwo/LLMForEverybody
Canonical: https://ross.abutalabs.com/products/llmforeverybody
Homepage: https://www.learnllm.ai
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: llm, interview-questions, interview-practice, agent, rag, learnllm
Last push: 2026-08-17T02:41:01+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 98, release rhythm 35, longevity 54
- inputs: {"age_days": 758, "days_push": 16, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7213, forks 671 (observed 2026-08-28T04:09:56.665342+00:00)

## What it is
A curated collection of LLM learning materials including interview question banks, systematic paper walkthroughs (Transformer, GPT, BERT, etc.), and links to practical courses on AI Agents, RAG, and LLM application development. Content is primarily in Chinese with Jupyter Notebook materials and companion video tutorials.

## Use cases
- prepare for LLM engineer job interviews
- learn how transformers and GPT models work
- study key AI papers from 2017 onward
- understand RAG and agent concepts for interviews
- find structured LLM learning roadmap
- review common large language model interview questions

## When to choose
- you are preparing for LLM-related job interviews, especially with Chinese-language content
- you want a guided, chronological path through foundational LLM papers
- you prefer free curated notes plus optional paid structured courses

## When to avoid
- you need production code or a software library to integrate into your project
- you require English-only learning materials
- you want hands-on model training or fine-tuning tooling rather than conceptual study

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-inference, rag, agent-framework, prompt-engineering, machine-learning
- domain: large-language-models, tutorials, education, artificial-intelligence
- platform: python
- tags: interview-preparation, llm, jupyter-notebooks, chinese-content, paper-reading, question-bank, learnllm, retrieval-augmented-generation, ai-agents, web

## Member repositories
- luhengshiwo/LLMForEverybody (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:56.665342+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:39:50.112819+00:00, confidence not recorded.
  - readme: https://github.com/luhengshiwo/LLMForEverybody (fetched 2026-08-28T04:09:56.665342+00:00, sha 7b797fdad876)
  - homepage: https://www.learnllm.ai (fetched 2026-08-29T08:34:48.057454+00:00, sha fd4533cec663)
  - site_page: https://www.learnllm.ai/pricing (fetched 2026-08-29T08:34:48.060958+00:00, sha c210d3fc950e)
- Data as of 2026-08-30T08:39:29.467469+00:00.