Easy AI Tutorial: Introduction to GPT Models

Forum topic · 小凯 · 2026-03-27

Summary

This tutorial from zhichai.net's Easy AI series introduces the GPT (Generative Pre-trained Transformer) family of large language models. It explains that GPT uses a Decoder-Only architecture built on stacked decoder layers with masked self-attention, making it well-suited for text generation. Training relies on causal language modeling, where the model predicts the next token from preceding context using a masking mechanism. The article covers key concepts including scale expansion (increasing parameters, data, and compute), and emergent abilities such as few-shot learning, reasoning, and code generation that appear once models reach sufficient scale. Core features highlighted include the pretrain-then-finetune paradigm, zero-shot and few-shot learning, in-context learning, autoregressive text generation, and large-scale unsupervised pretraining. A development timeline traces the series' evolution: GPT-1 (117M parameters, 2018) established pretraining plus fine-tuning; GPT-2 (1.5B, 2019) demonstrated zero-shot learning; GPT-3 (175B, 2020) revealed emergent abilities; and GPT-4 (2023, parameter count undisclosed) brought multimodal capability and significantly improved reasoning.

GPT Models

What is GPT?

GPT (Generative Pre-trained Transformer) is a large language model based on a Decoder-Only architecture. Through causal language modeling training on massive 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 only masked self-attention layers, which makes it better suited for text generation tasks.

2. Causal Language Model (Causal LM)

A training approach that predicts the next word 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 — including few-shot learning, reasoning, and code generation.

Core Characteristics

✅ Pretrain-then-finetune paradigm
✅ Zero-shot & few-shot learning
✅ In-context learning
✅ Autoregressive text generation
✅ Large-scale unsupervised pretraining

Development Timeline

| Version | Parameters | Release Year | Key Breakthrough | |---------|-----------|--------------|------------------| | GPT-1 | 117M | 2018 | Pretrain + fine-tune paradigm | | GPT-2 | 1.5B | 2019 | Zero-shot learning capability | | GPT-3 | 175B | 2020 | Emergent abilities | | GPT-4 | Undisclosed | 2023 | Multimodality, greatly improved reasoning |

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Source: Easy AI Tutorial