GPT Models Tutorial

GPT Models

Generative Pre-trained Transformers by OpenAI.

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GPT Models

GPT (Generative Pre-trained Transformer) is the family of models from OpenAI that made AI mainstream. While BERT is for "understanding," GPT is built for "generating".

Level 1 — Autoregressive Generation

GPT models are Autoregressive. This means they predict the next word, then use that prediction to predict the next next word, and so on. It's essentially a very advanced "Autocomplete."

Level 2 — Decoder-only Architecture

Unlike BERT (Encoder-only) or T5 (Encoder-Decoder), GPT uses only the Decoder part of the Transformer. It uses "Masked Self-Attention" to ensure it only looks at the past and never cheats by looking at future words.

Prompt Engineering: Because GPT models are pre-trained on so much data, they don't always need fine-tuning. You can just "prompt" them to behave a certain way.

Level 3 — Zero-Shot and Few-Shot Learning

Starting with GPT-3, these models showed they could perform tasks (like translation) without seeing a single training example for that specific task, just by understanding the instruction. This is called Zero-Shot Learning.

GPT-2 Text Generation
from transformers import pipeline

generator = pipeline('text-generation', model='gpt2')
prompt = "The future of NLP is"
result = generator(prompt, max_length=30, num_return_sequences=1)

print(result[0]['generated_text'])
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