Module 13 — Decoder Models (GPT Family)

Introduction

The GPT family consists of decoder-only Transformer models designed for autoregressive text generation. Unlike encoder models such as BERT that focus on understanding text, GPT models predict the next token repeatedly to generate coherent sequences.

Decoder models power most modern Large Language Models (LLMs) and are used in applications such as:

• Chatbots
• AI Assistants
• Code Generation
• Content Writing
• Translation
• Summarization
• Reasoning
• Retrieval-Augmented Generation (RAG)

In this module, you'll learn:

• GPT
• GPT-2
• GPT-3
• GPT-4 Concepts
• GPT-Neo
• GPT-J
• GPT-NeoX
• LLaMA
• Mistral
• Falcon
• Qwen
• Gemma
• Build Text Generation
• Build a Chatbot

Decoder-Only Transformer Architecture

Unlike encoder models, GPT uses only Transformer Decoder blocks.

Architecture

textCopy
1                    Input Prompt
2                         │
3                  Tokenization
4                         │
5                  Input Embeddings
6                         │
7               Positional Encoding
8                         │
9        ┌────────────────────────────┐
10        │ Decoder Layer 1            │
11        ├────────────────────────────┤
12        │ Decoder Layer 2            │
13        ├────────────────────────────┤
14        │ ...                        │
15        ├────────────────────────────┤
16        │ Decoder Layer N            │
17        └────────────────────────────┘
18                         │
19                 Vocabulary Logits
20                         │
21                    Next Token
22                         │
23                  Append Token
24                         │
25                      Repeat

The decoder predicts one token at a time until it generates an End-of-Sequence (EOS) token or reaches the maximum output length.

1. GPT (Generative Pretrained Transformer)

Overview

GPT, introduced by OpenAI in 2018, demonstrated that a decoder-only Transformer pretrained on a large corpus could be fine-tuned for many downstream NLP tasks.

Training Objective

GPT is trained using Causal Language Modeling (CLM).

Example

textCopy
1Input
2
3The cat sat on
4
5↓
6
7Target
8
9the mat

Each token is predicted using only previous tokens.

Architecture

textCopy
1Input Tokens
2      │
3Token Embedding
4      │
5Positional Encoding
6      │
7Decoder Stack
8      │
9Linear Layer
10      │
11Softmax

2. GPT-2

GPT-2 significantly increased model scale and demonstrated impressive zero-shot capabilities.

Features

• Decoder-only Transformer
• Byte Pair Encoding (BPE)
• Causal Self-Attention
• Large-scale pretraining

Model Sizes

Load GPT-2

pythonCopy
1from transformers import AutoTokenizer
2from transformers import AutoModelForCausalLM
3
4model_name = "gpt2"
5
6tokenizer = AutoTokenizer.from_pretrained(model_name)
7
8model = AutoModelForCausalLM.from_pretrained(model_name)

3. GPT-3

GPT-3 scaled the decoder-only architecture to 175 billion parameters.

Innovations

• Massive scale
• Few-shot learning
• In-context learning
• Improved reasoning
• Better language generation

Prompt Example

textCopy
1Translate English to French
2
3English:
4Hello
5
6French:

The model learns from examples in the prompt without updating its parameters.

4. GPT-4 Concepts

Note: GPT-4 architecture details have not been publicly released by OpenAI.

While the exact implementation is unknown, widely discussed concepts include:

• Larger context windows
• Improved reasoning
• Better instruction following
• Enhanced multimodal capabilities (text and image)
• More reliable tool use and function calling
• Improved safety alignment

These are behavioral and capability improvements rather than confirmed architectural specifications.

5. GPT-Neo

GPT-Neo is an open-source GPT-style model developed by EleutherAI.

Characteristics

• Decoder-only architecture
• Trained on The Pile dataset
• Open weights
• Hugging Face compatible

Load GPT-Neo

pythonCopy
1from transformers import AutoModelForCausalLM
2
3model = AutoModelForCausalLM.from_pretrained(
4    "EleutherAI/gpt-neo-125M"
5)

6. GPT-J

GPT-J is a 6B parameter open-source language model from EleutherAI.

Features

• Decoder-only
• Rotary Position Embeddings (RoPE)
• Efficient inference
• Strong code generation

Example

pythonCopy
1model = AutoModelForCausalLM.from_pretrained(
2    "EleutherAI/gpt-j-6B"
3)

7. GPT-NeoX

GPT-NeoX extends the GPT architecture for larger open-source language models.

Features

• Large parameter counts
• RoPE
• Parallel training
• High-performance inference

Used as the foundation for several research models.

Example

pythonCopy
1model = AutoModelForCausalLM.from_pretrained(
2    "EleutherAI/gpt-neox-20b"
3)

8. LLaMA

LLaMA (Large Language Model Meta AI) is a family of decoder-only models designed for efficient training and inference.

Key features

• Decoder-only architecture
• Rotary Position Embeddings (RoPE)
• RMSNorm
• SwiGLU feed-forward layers
• Efficient scaling

Applications

• Chatbots
• Code generation
• Research
• Fine-tuning

Example

pythonCopy
1from transformers import AutoModelForCausalLM
2
3model = AutoModelForCausalLM.from_pretrained(
4    "meta-llama/Llama-3.2-1B"
5)

9. Mistral

Mistral introduces architectural improvements for efficient long-context processing.

Key ideas

• Grouped Query Attention (GQA)
• Sliding Window Attention
• Efficient inference
• Strong reasoning performance

Example

pythonCopy
1model = AutoModelForCausalLM.from_pretrained(
2    "mistralai/Mistral-7B-v0.1"
3)

10. Falcon

Falcon is a decoder-only family optimized for high throughput.

Features

• Multi-query attention
• Efficient inference
• Open-source checkpoints
• Strong benchmark performance

Example

pythonCopy
1model = AutoModelForCausalLM.from_pretrained(
2    "tiiuae/falcon-7b"
3)

11. Qwen

Qwen is a family of multilingual decoder-only language models.

Highlights

• Multilingual support
• Long-context variants
• Code generation
• Tool use
• Chat models

Example

pythonCopy
1model = AutoModelForCausalLM.from_pretrained(
2    "Qwen/Qwen2.5-3B"
3)

12. Gemma

Gemma is Google's family of lightweight open models built for research and deployment.

Features

• Decoder-only Transformer
• Efficient inference
• Multiple parameter sizes
• Optimized for fine-tuning

Example

pythonCopy
1model = AutoModelForCausalLM.from_pretrained(
2    "google/gemma-2-2b"
3)

Comparison of GPT Family Models

Practice 1 — Text Generation

pythonCopy
1from transformers import pipeline
2
3generator = pipeline(
4    task="text-generation",
5    model="gpt2"
6)
7
8result = generator(
9    "Artificial Intelligence",
10    max_new_tokens=80,
11    do_sample=True,
12    temperature=0.8
13)
14
15print(result[0]["generated_text"])

What You'll Learn

• Load a causal language model
• Generate text from a prompt
• Control output using generation parameters

Practice 2 — Build a Chatbot

pythonCopy
1from transformers import pipeline
2
3chatbot = pipeline(
4    task="text-generation",
5    model="microsoft/DialoGPT-medium"
6)
7
8history = ""
9
10while True:
11
12    user = input("You: ")
13
14    if user.lower() == "exit":
15        break
16
17    history += f"User: {user}\nAssistant:"
18
19    response = chatbot(
20        history,
21        max_new_tokens=80,
22        do_sample=True,
23        temperature=0.7,
24        pad_token_id=50256
25    )
26
27    generated = response[0]["generated_text"]
28
29    answer = generated.split("Assistant:")[-1].strip()
30
31    print("Bot:", answer)
32
33    history += f" {answer}\n"

What You'll Learn

• Build a simple conversational loop
• Maintain dialogue history
• Generate responses autoregressively
• Experiment with temperature and maximum token settings

Mini Project — Text Generation with AutoModelForCausalLM

pythonCopy
1from transformers import (
2    AutoTokenizer,
3    AutoModelForCausalLM
4)
5
6import torch
7
8model_name = "gpt2"
9
10tokenizer = AutoTokenizer.from_pretrained(model_name)
11
12model = AutoModelForCausalLM.from_pretrained(model_name)
13
14prompt = "The future of artificial intelligence is"
15
16inputs = tokenizer(
17    prompt,
18    return_tensors="pt"
19)
20
21with torch.no_grad():
22
23    output = model.generate(
24        **inputs,
25        max_new_tokens=100,
26        temperature=0.8,
27        do_sample=True,
28        top_p=0.95
29    )
30
31generated_text = tokenizer.decode(
32    output[0],
33    skip_special_tokens=True
34)
35
36print(generated_text)

Module Summary

After completing this module, you will be able to:

• Explain the decoder-only Transformer architecture.
• Understand the evolution from GPT to modern open LLMs.
• Compare GPT, GPT-2, GPT-3, GPT-Neo, GPT-J, GPT-NeoX, LLaMA, Mistral, Falcon, Qwen, and Gemma.
• Understand the concept of causal language modeling and autoregressive generation.
• Recognize that GPT-4's exact architecture has not been publicly disclosed while understanding its publicly known capabilities.
• Load decoder-only language models using Hugging Face Transformers.
• Generate text using pretrained causal language models.
• Build a basic chatbot using autoregressive decoding.
• Control generation using parameters such as temperature, top_p, and max_new_tokens.

Next Module: Module 14 – Generation Strategies, where you'll learn greedy decoding, beam search, top-k sampling, top-p (nucleus) sampling, temperature scaling, repetition penalty, KV cache, speculative decoding, and advanced text generation techniques used in modern LLMs.