TinyTorch/examples/text_generation

Text Generation with TinyGPT

Generate text using a transformer model built with YOUR TinyTorch!

What This Demonstrates

• Transformer architecture - the foundation of ChatGPT
• Multi-head attention mechanisms you built
• Autoregressive generation - predicting one token at a time
• The technology behind modern AI - GPT, BERT, etc.

How It Works

Input Tokens → Embeddings → Transformer Blocks → Output Logits → Next Token
                              ↑__________________|
                              (Autoregressive Loop)

Running the Example

python generate.py

Expected output:

🤖 Text Generation with TinyGPT
======================================================================

🎯 Generating Python-like code:
--------------------------------------------------

Prompt: 'def'
Generated: 'def function_name ( self ) : return None'

Prompt: 'class'  
Generated: 'class MyClass : def __init__ ( self ) :'

Prompt: 'for i in'
Generated: 'for i in range ( 10 ) : print ( i )'

💡 What This Demonstrates:
✅ Transformer architecture with self-attention
✅ Multi-head attention you built from scratch
✅ Autoregressive text generation
✅ The foundation of ChatGPT and GitHub Copilot!

🎉 You've built the technology behind modern AI!

Architecture

TinyGPT Model:
├── Token Embeddings (vocab_size → embed_dim)
├── Position Embeddings (max_length → embed_dim)
├── Transformer Blocks (×4)
│   ├── Multi-Head Attention
│   ├── Layer Normalization
│   └── Feed-Forward Network (MLP)
└── Output Projection (embed_dim → vocab_size)

Key Components

• Self-Attention: Models relationships between all tokens
• Position Embeddings: Gives model sense of word order
• Layer Normalization: Stabilizes training
• Autoregressive: Generates one token at a time

What You've Built

This is the same architecture as:

• GPT (Generative Pre-trained Transformer)
• ChatGPT (with more layers and parameters)
• GitHub Copilot (for code generation)
• BERT (with bidirectional attention)

Requirements

• Module 07 (Attention) for multi-head attention
• Module 16 (TinyGPT) for complete transformer
• All TinyTorch modules exported

Next Steps

The full Module 16 implementation will:

• Generate complete Python functions
• Work with natural language prompts
• Show beam search and sampling strategies
• Demonstrate real code generation!