Easy AI Tutorial: An Introduction to GPT Models

Forum topic · 小凯 · 2026-03-27

Summary

This tutorial from Easy AI (zhichai.net) explains GPT (Generative Pre-trained Transformer), a decoder-only large language model trained via causal language modeling on massive text corpora. It covers four core concepts: the decoder-only architecture with masked self-attention suited to text generation; causal language modeling, which predicts the next token from preceding context only; scaling laws, where increasing parameters, data, and compute improves capability; and emergent abilities such as few-shot learning, reasoning, and code generation that appear at large scale. Key features include the pretrain-then-finetune paradigm, zero-shot and few-shot learning, in-context learning, autoregressive generation, and large-scale unsupervised pretraining. The tutorial also traces the GPT timeline: GPT-1 (117M parameters, 2018, pretrain+finetune paradigm), GPT-2 (1.5B, 2019, zero-shot learning), GPT-3 (175B, 2020, emergent abilities), and GPT-4 (2023, undisclosed parameter count, multimodal with greatly improved reasoning).

GPT Models

What is GPT?

GPT (Generative Pre-trained Transformer) is a large language model based on a Decoder-Only architecture. By training with causal language modeling on massive amounts of text data, it acquires powerful text understanding and generation capabilities.

Core Concepts

1. Decoder-Only Architecture

A model architecture built by stacking only decoder layers, keeping just the masked self-attention layers. This design is better suited to text generation tasks.

2. Causal Language Modeling (Causal LM)

A training approach that predicts the next token based on preceding context. A masking mechanism ensures the model can only see content before the current position.

3. Scaling

Improving model capability by increasing the number of parameters, the amount of data, and compute. The GPT series demonstrated the idea that "scale is everything."

4. Emergent Abilities

Capabilities that large models display once they reach a certain scale, exceeding expectations. These include few-shot learning, reasoning, and code generation.

Key Features

✅ Pretraining–finetuning paradigm
✅ Zero-shot & few-shot learning
✅ In-context learning
✅ Autoregressive text generation
✅ Large-scale unsupervised pretraining

Development Timeline

| Version | Parameters | Year | Key Breakthrough | |---------|-----------|------|------------------| | GPT-1 | 117 million | 2018 | Pretrain + finetune paradigm | | GPT-2 | 1.5 billion | 2019 | Zero-shot learning | | GPT-3 | 175 billion | 2020 | Emergent abilities | | GPT-4 | Undisclosed | 2023 | Multimodal, greatly improved reasoning |

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Source: Easy AI Tutorial