---
title: "TinyGPT - Language Models"
description: "Build GPT-style transformer models for language understanding using TinyTorch"
difficulty: "⭐⭐⭐⭐⭐"
time_estimate: "4-6 hours"
prerequisites: []
next_steps: []
learning_objectives: []
---

# Module 16: TinyGPT - Language Models

```{div} badges
⭐⭐⭐⭐⭐ | ⏱️ 4-6 hours
```


**From Vision to Language: Building GPT-style transformers with TinyTorch**

## Learning Objectives

By the end of this module, you will:

1. **Build GPT-style transformer models** using TinyTorch Dense layers and attention mechanisms
2. **Understand character-level tokenization** and its role in language model training
3. **Implement multi-head attention** that enables models to focus on different parts of sequences
4. **Create complete transformer blocks** with layer normalization and residual connections
5. **Train autoregressive language models** that generate coherent text sequences
6. **Apply ML Systems thinking** to understand framework reusability across vision and language

## What Makes This Special

This module demonstrates the **power of TinyTorch's foundation** by extending it from vision to language models:

- **~70% component reuse**: Dense layers, optimizers, training loops, loss functions
- **Strategic additions**: Only what's essential for language - attention, tokenization, generation
- **Educational clarity**: See how the same mathematical foundations power both domains
- **Framework thinking**: Understand why successful ML frameworks support multiple modalities

## Components Implemented

### Core Language Processing
- **CharTokenizer**: Character-level tokenization with vocabulary management
- **PositionalEncoding**: Sinusoidal position embeddings for sequence order

### Attention Mechanisms  
- **MultiHeadAttention**: Parallel attention heads for capturing different relationships
- **SelfAttention**: Simplified attention for easier understanding
- **CausalMasking**: Preventing attention to future tokens in autoregressive models

### Transformer Architecture
- **LayerNorm**: Normalization for stable transformer training
- **TransformerBlock**: Complete transformer layer with attention + feedforward
- **TinyGPT**: Full GPT-style model with embedding, positional encoding, and generation

### Training Infrastructure
- **LanguageModelLoss**: Cross-entropy loss with proper target shifting
- **LanguageModelTrainer**: Training loops optimized for text sequences
- **TextGeneration**: Autoregressive sampling for coherent text generation

## Key Insights

1. **Framework Reusability**: TinyTorch's Dense layers work seamlessly for language models
2. **Attention Innovation**: The key difference between vision and language is attention mechanisms
3. **Sequence Modeling**: Language requires understanding order and context across long sequences
4. **Autoregressive Generation**: Language models predict one token at a time, building coherently

## Educational Philosophy

This module shows that **vision and language models share the same foundation**:
- Matrix multiplications (Dense layers) 
- Nonlinear activations
- Gradient-based optimization
- Batch processing and training loops

The magic happens in the **architectural patterns** we add on top!

## Prerequisites

- Modules 1-11 (especially Tensor, Dense, Attention, Training)
- Understanding of sequence modeling concepts
- Familiarity with autoregressive generation

## Time Estimate

4-6 hours for complete understanding and implementation

---

*"Language is the most powerful tool humans have created. Now let's teach machines to wield it." - The TinyTorch Philosophy*


Choose your preferred way to engage with this module:

````{grid} 1 2 3 3

```{grid-item-card} 🚀 Launch Binder
:link: https://mybinder.org/v2/gh/mlsysbook/TinyTorch/main?filepath=modules/source/16_tinygpt/tinygpt_dev.ipynb
:class-header: bg-light

Run this module interactively in your browser. No installation required!
```

```{grid-item-card} ⚡ Open in Colab  
:link: https://colab.research.google.com/github/mlsysbook/TinyTorch/blob/main/modules/source/16_tinygpt/tinygpt_dev.ipynb
:class-header: bg-light

Use Google Colab for GPU access and cloud compute power.
```

```{grid-item-card} 📖 View Source
:link: https://github.com/mlsysbook/TinyTorch/blob/main/modules/source/16_tinygpt/tinygpt_dev.py
:class-header: bg-light

Browse the Python source code and understand the implementation.
```

````

```{admonition} 💾 Save Your Progress
:class: tip
**Binder sessions are temporary!** Download your completed notebook when done, or switch to local development for persistent work.

Ready for serious development? → [🏗️ Local Setup Guide](../usage-paths/serious-development.md)
```

---

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