{"data":{"slug":"luhengshiwo-llmforeverybody","name":"LLMForEverybody","tagline":"LLM knowledge sharing for everyone, essential reading before big model interviews","github_url":"https://github.com/luhengshiwo/LLMForEverybody","owner":"luhengshiwo","repo":"LLMForEverybody","owner_avatar_url":"https://avatars.githubusercontent.com/u/13251733?v=4","primary_language":"Jupyter Notebook","stars":7167,"forks":662,"topics":["agent","interview-practice","interview-questions","learnllm","llm","rag"],"archived":false,"github_pushed_at":"2026-08-17T02:41:01+00:00","maintenance_label":"Very active","stars_delta_30d":198,"url":"https://www.graphcanon.com/tools/luhengshiwo-llmforeverybody","markdown_url":"https://www.graphcanon.com/tools/luhengshiwo-llmforeverybody.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/luhengshiwo-llmforeverybody","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=luhengshiwo-llmforeverybody","description":"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","homepage_url":"https://www.learnllm.ai","license":"Apache-2.0","open_issues":0,"watchers":30,"ai_summary":"Provides in-depth learning materials on large language models including interview questions, systematic paper reading guides, and practical courses covering AI agents, RAG knowledge base, and LLM application development. Supported by accompanying video tutorials.","readme_excerpt":"

\n\"LearnLLM.AI\"\n

\n

\n \"微信公众号\"\n  \n \"知乎\"\n  \n \"CSDN\"\n  \n \"掘金\"\n

\n\n

Learning LLM is all you need.

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\n 中文 | English | Русский\n

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👉 点击 LearnLLM.AI | 学习大模型，从这里开始

\n\n## LearnLLM.AI 核心亮点\n\n **精选大模型面试题库**：覆盖从基础到前沿的实战题目，助您高效备战求职，抓住职业机遇；\n\n **系统化论文研读**：从2017年Transformer奠基性论文出发，按清晰的知识体系梳理技术演进，适合不同基础的开发者循序渐进地深度提升；\n\n **精选实战课程**：围绕 AI Agent、RAG 知识库、大模型微调与 LLM 应用开发等核心方向，打磨成体系的中文实战课程，覆盖 LangChain、LlamaIndex、Dify、MCP 等主流工具链，配套项目代码与讲师答疑，支持按主题灵活拆分、按需选学，帮你由点及面搭建完整的大模型知识体系。👉 [浏览全部课程](https://learnllm.ai/courses?ref=github)\n\n\n**专属优惠码**\n\n我们为Github用户准备了限时专属优惠码：***GITHUB50*** ，期待在 [LearnLLM.AI](https://learnllm.ai?ref=github) 与您继续同行，共同成长！\n\n**配套视频教程(持续更新中)**：\n\n👉 点击这里 [bilibili](https://space.bilibili.com/37863979/lists?sid=7144646) \n\n👉 点击这里 [YouTube](https://www.youtube.com/@learnllm-ai)\n\n*如有疑问，欢迎随时联系我们。*\n\n*Happy Learning！*\n\n*LearnLLM.AI 团队*\n\n---\n\n## LLM 精选论文\n\n| 时间 | 论文 | 介绍 | 视频 | 开始学习 |\n| --- | --- | --- | --- | --- |\n| 2017-06-12 | [Transformer](https://arxiv.org/abs/1706.03762) | 提出自注意力与 Transformer 架构 | [](https://www.bilibili.com/video/BV1YPrKBuEjk) | |\n| 2018-06-11 | [GPT-1](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) | 预训练 + 微调的生成式 Transformer | [](https://www.bilibili.com/video/BV1gW6QBFEG4) | |\n| 2018-10-11 | [BERT](https://arxiv.org/abs/1810.04805) | 双向编码器：MLM + NSP | [](https://www.bilibili.com/video/BV1n2kFBgEJ5) | |\n| 2019-02-14 | [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) | 大规模无监督文本生成 | [](https://www.bilibili.com/video/BV1VwkWBtEfe) | |\n| 2019-10-23 | [T5](https://arxiv.org/abs/1910.10683) | 文本到文本统一框架 | [](https://www.bilibili.com/video/BV1fHBfBdEGY) | |\n| 2020-05-28 | [GPT-3](https://arxiv.org/abs/2005.14165) | 大模型与少样本学习能力 | [](https://www.bilibili.com/video/BV14Z63ByEWV) | |\n| 2020-10 | [ViT](https://arxiv.org/abs/2010.11929) | 将 Transformer 主干引入视觉领域 | [](https://www.bilibili.com/video/BV1UYAuzaEHd/) | |\n| 2021-02 | [ViLT](https://arxiv.org/abs/2102.03334) | 极简视觉语言预训练架构 | [](https://www.bilibili.com/video/BV1vgXDBAEzM) | |\n| 2021-02 | [CLIP](https://arxiv.org/abs/2103.00020) | 用自然语言监督实现零样本视觉学习 | [](https://www.bilibili.com/video/BV1wGDvBfEv6) | |\n| 2021-02 | [DALL·E 1](https://arxiv.org/abs/2102.12092) | 自回归文本生成图像的开端 | [](https://www.bilibili.com/video/BV1zPXDBTE3c) | |\n| 2021-07-07 | [CodeX](https://arxiv.org/abs/2107.03374) | 面向代码生成的 GPT 系列模型 | [](https://www.bilibili.com/video/BV1JC67BEE7b) | |\n| 2021-12 | [Stable Diffusion](https://arxiv.org/abs/211","github_created_at":"2024-08-05T07:28:49+00:00","created_at":"2026-07-07T17:37:30.107361+00:00","updated_at":"2026-08-18T12:01:22.391145+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"agent","name":"agent"},{"slug":"interview-practice","name":"interview-practice"},{"slug":"learnllm","name":"learnllm"},{"slug":"llm","name":"llm"},{"slug":"rag","name":"rag"}],"trust":{"provenance":{"is_fork":false,"github_id":838216432,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-18T12:01:21.687Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":0,"days_since_push":1,"last_release_at":null,"stars_delta_30d":198,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:09:41.507Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-18T12:01:22.104Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-08-18T12:01:22.104Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-18T12:01:22.104Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["If you are preparing for job interviews in the field of LLMs or related technologies and want access to practical questions and answers.","For developers who need a deep-dive understanding into various technical evolutions as outlined through selected research papers from Transformer onwards.","When you are looking for Chinese-language educational content surrounding AI Agents, RAG Knowledge Bases, fine-tuning of large models, and LLM application development, complete with project codes and"],"when_not_to_use":["If your learning preference leans towards a different language or if the Chinese-specific resources don't align with your needs.","For individuals looking for comprehensive open-source tools or frameworks to build upon directly; this is more about educational content than concrete implementations."],"source":"enrich:decision_facts","observed_at":"2026-07-09T08:09:32.003Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"LLMForEverybody is a repository primarily focused on sharing knowledge about large language models, with content that includes interview practice, research paper studies (from foundational Transformer papers to more up-t"}]}}