Understanding AI Large Model Architecture: A Complete Visual Guide

AI large models (Large Language Models, LLMs) are deep neural networks with billions to trillions of parameters that learn from massive text corpora and exhibit natural‑language understanding, generation, reasoning, and code‑generation capabilities.

Most mainstream LLMs—including OpenAI’s GPT series, Google’s Gemini, Anthropic’s Claude, Meta’s Llama, and DeepSeek’s models—share a common core architecture: the Transformer, introduced by Google in 2017 and which fundamentally replaced recurrent neural networks (RNNs).

Transformer core mechanisms

1. Token Embeddings – Input text is first tokenized into discrete tokens. Each token is then projected into a high‑dimensional vector (e.g., 4096‑dim). The model does not recognize raw characters; it works with these vector representations. Example tokenization:

ChatGPT很强
↓
Chat
GPT
很
强

2. Self‑Attention – Unlike RNNs, self‑attention lets every token attend to all other tokens in the sequence, computing attention weights that capture semantic relationships. For instance, in the sentence “苹果的味道很好”, the word “味道” causes the token “苹果” to be associated with “fruit”, whereas in “苹果的股价大涨”, the same token “苹果” is linked to “technology company”.

3. Positional Encoding – Because self‑attention processes tokens in parallel, positional encodings are added to token vectors to convey order information; otherwise, sentences like “我通过了面试” and “面试通过了我” would be indistinguishable.

AI large model training

The training process is typically divided into three stages:

Pre‑training : The model learns general language patterns from vast amounts of unlabelled or weakly labelled data (web pages, books, research papers, code repositories, multimodal data). The goal is to enable the model to “learn language”.

Instruction fine‑tuning : Because a pre‑trained model can continue text but may not follow human instructions, a curated set of question‑answer, dialogue, and task examples is used to teach the model to obey commands, answer questions, output in specific formats, and adapt to concrete scenarios.

Alignment optimization : Techniques such as reinforcement learning from human feedback (RLHF) are applied to make responses more helpful and to reduce harmful, biased, or fabricated content.

In summary, the essence of AI large models can be captured as a Transformer‑based architecture trained at massive scale: the model predicts the next token, learns language regularities from huge data, and, through staged training, acquires general‑purpose intelligence.