想了解大语言模型到底是怎么工作的，找到的资料都太过于学术看不懂，或者说的太浅只讲概念，就没一个从头到尾讲清楚的内容。 无独有偶，看到 how-llms-work 这个项目，把大模型的完整流程做成了一个可视化交互网页，内容基于 Karpathy 的经典讲座整理。 从网页数据采集、分词、神经网络训练，一直到推理生成、后训练对齐，每个阶段都有直观的图示和解释，一页看完整个流程。 GitHub：http://github.com/ynarwal/how-llms-work… 还涵盖了幻觉问题、上下文窗口、检索增强生成等进阶话题，帮我们理解模型的能力边界和实际应用方式。 想搞明白大模型从训练到对话的全过程，又不想啃学术论文，这份教程值得花十分钟看一遍。

ynarwal/how-llms-work

Source: https://github.com/ynarwal/how-llms-work

How LLMs Actually Work

A visual, interactive guide to how large language models are built — from raw internet text to a conversational assistant.

Live site: https://ynarwal.github.io/how-llms-work/

Based on Andrej Karpathy’s Intro to Large Language Models lecture.

What’s inside

• Data Collection — how the web is scraped and filtered into training data (Common Crawl, FineWeb)
• Tokenization — how text is broken into subword tokens via Byte Pair Encoding (BPE)
• Neural Network Training — the loss function, gradient descent, and what a forward pass looks like
• Inference & Sampling — how the model generates text token by token, and how temperature works
• The Base Model — what a model knows after pre-training and what it can’t do yet
• Post-Training — RLHF, instruction tuning, and how a base model becomes an assistant
• LLM Psychology — hallucinations, context windows, and how to think about what models “know”
• RAG — retrieval-augmented generation: embeddings, vector search, and context injection
• Full Pipeline Summary — end-to-end visual of every stage

Files

HN discussion

Posted to Hacker News and generated heated debate, mostly about it being LLM-generated. Fair point — but the content isn’t the AI’s. Every claim, figure, and framing is traced directly to Karpathy’s lecture, not hallucinated by a model.

Vibe check

The code and content in this repo is mostly LLM-generated (Claude via Claude Code). The ideas, direction, and editorial decisions are mine — the implementation was largely written by AI. The council fact-checker exists precisely because of this: automated content warrants automated verification.