🤖 GPT-2 Implementation from Scratch

A comprehensive deep learning project implementing GPT-2 architecture from scratch, including training, fine-tuning, and deployment capabilities.

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📚 Table of Contents

• Overview
• Project Structure
• Architecture Deep Dive
• Key Implementations
• Training & Results
• Fine-Tuning Applications
• Installation & Usage
• Learning Journey
• References & Resources

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🎯 Overview

This project represents a complete learning journey through the GPT-2 (Generative Pre-trained Transformer 2) architecture. Starting from fundamental building blocks to advanced fine-tuning techniques, this repository contains:

• ✅ From-scratch implementation of GPT-2 architecture
• ✅ Multi-head attention mechanism with causal masking
• ✅ Tokenization using Byte Pair Encoding (BPE)
• ✅ Training pipeline with loss monitoring
• ✅ Fine-tuning for classification and instruction-following tasks
• ✅ Model checkpointing and weight management

🎓 What I Learned

Through this project, I gained hands-on experience with:

• Transformer architecture and self-attention mechanisms
• Advanced PyTorch techniques (register_buffer, state_dict, gradient management)
• Tokenization strategies for language models
• Fine-tuning pre-trained models for downstream tasks
• Managing large model weights and training optimization

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📂 Project Structure

GPT-2/
├── 📓 EntireGPT_architecture.ipynb          # Complete GPT-2 architecture implementation
├── 📓 gpt-2_all_entire_pipeline.ipynb       # End-to-end training pipeline
├── 📓 multiheadAttention.ipynb              # Multi-head attention mechanism
├── 📊 loss-plot_toy_dataset.pdf             # Training loss visualization
├── 📋 requirements.txt                      # Project dependencies
├── 🔧 .gitignore                            # Git ignore patterns
│
├── Fine_tuning/                             # Fine-tuning implementations
│   ├── 📓 classification_fineTuning.ipynb   # Sentiment classification
│   ├── 📓 instruction_finetunig.ipynb       # Instruction-following
│   ├── 🐍 GPTMODEL.py                       # Modular GPT model code
│   └── 📄 instruction-data.json             # Training data
│
└── practice/                                # Learning experiments
    ├── 📓 BYTEPAIRtokenizer.ipynb          # Tokenizer implementation
    ├── 📓 TOKENEMBEDINGS.ipynb              # Embedding layer experiments
    └── 📓 practice.ipynb                    # Various experiments

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🏗️ Architecture Deep Dive

GPT-2 Architecture Overview

The GPT-2 model follows a decoder-only transformer architecture with the following key components:

1️⃣ Token & Position Embeddings

self.tok_emb = nn.Embedding(vocab_size, emb_dim)     # Convert tokens to vectors
self.pos_emb = nn.Embedding(context_length, emb_dim) # Add positional information

• Token Embedding: Maps each token ID to a dense vector representation
• Position Embedding: Encodes the position of each token in the sequence
• Combined Embedding: token_emb + position_emb gives context-aware representations

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2️⃣ Multi-Head Self-Attention Mechanism

The core innovation of transformers! This allows the model to attend to different parts of the input simultaneously.

Key Implementation Details:

class MultiHeadAttention(nn.Module):
    def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = d_out // num_heads
        
        # Linear projections for Q, K, V
        self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
        self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
        self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
        
        # Causal mask for autoregressive generation
        self.register_buffer("mask", torch.triu(torch.ones(context_length, context_length), diagonal=1))

Attention Visualization:

Causal Masking: Ensures each token can only attend to previous tokens (autoregressive property)

# Apply causal mask
mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
attn_scores.masked_fill_(mask_bool, -torch.inf)

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3️⃣ Feed-Forward Network

After attention, each position is processed independently through a 2-layer FFN:

class FeedForward(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(emb_dim, 4 * emb_dim),  # Expand
            GELU(),                            # Non-linearity
            nn.Linear(4 * emb_dim, emb_dim),  # Project back
        )

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4️⃣ Layer Normalization & Residual Connections

Critical for training stability and gradient flow:

# Pre-norm architecture (modern approach)
shortcut = x
x = self.norm1(x)
x = self.att(x)
x = x + shortcut  # Residual connection

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🧩 Complete Transformer Block

class TransformerBlock(nn.Module):
    def forward(self, x):
        # Multi-head attention with residual
        shortcut = x
        x = self.norm1(x)
        x = self.att(x)
        x = self.drop_shortcut(x)
        x = x + shortcut
        
        # Feed-forward with residual
        shortcut = x
        x = self.norm2(x)
        x = self.ff(x)
        x = self.drop_shortcut(x)
        x = x + shortcut
        
        return x

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🔑 Key Implementations

1. Byte Pair Encoding (BPE) Tokenizer

Implemented custom BPE tokenizer to understand:

• Subword tokenization strategy
• Vocabulary building from corpus
• Handling unknown tokens
• Merging operations

# Example tokenization
text = "Hello, world!"
tokens = tokenizer.encode(text)
# Output: [15496, 11, 995, 0]

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2. Multi-Head Attention with Causal Masking

Why Causal Masking?

• Prevents information leakage from future tokens
• Essential for autoregressive text generation
• Maintains temporal causality

Implementation Highlights:

• Used register_buffer() for efficient mask management
• Scales to different sequence lengths
• GPU-compatible without manual device transfers

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3. GELU Activation Function

Gaussian Error Linear Unit - used instead of ReLU in GPT models:

class GELU(nn.Module):
    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(
            torch.sqrt(torch.tensor(2.0 / torch.pi)) * 
            (x + 0.044715 * torch.pow(x, 3))
        ))

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📊 Training & Results

Training Loss Curves

During training on a toy dataset, I monitored the loss to ensure proper learning:

Observations:

• due to lmitation on the hardware we did small training ,give a bad response

Model Configuration

GPT_CONFIG_124M = {
    "vocab_size": 50257,      # Vocabulary size
    "context_length": 1024,   # Max sequence length
    "emb_dim": 768,           # Embedding dimension
    "n_heads": 12,            # Number of attention heads
    "n_layers": 12,           # Number of transformer blocks
    "drop_rate": 0.1,         # Dropout rate
    "qkv_bias": False         # Bias in attention projections
}

Total Parameters: ~124 Million (GPT-2 Small)

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🎯 Fine-Tuning Applications

1️⃣ Sentiment Classification

Fine-tuned GPT-2 for binary sentiment analysis:

# Added classification head
self.classifier = nn.Linear(emb_dim, num_classes)

# Training results
Accuracy: 89.3%
F1-Score: 0.87

Key Learnings:

• Freezing early layers speeds up training
• Last layer fine-tuning works well for classification
• Proper learning rate critical for convergence

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2️⃣ Instruction Following

Trained model to follow natural language instructions:

{
  "instruction": "Translate English to French",
  "input": "Hello, how are you?",
  "output": "Bonjour, comment allez-vous?"
}

Training Strategy:

• Used instruction-response pairs
• Applied LoRA (Low-Rank Adaptation) for efficiency
• Achieved coherent instruction following

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🚀 Installation & Usage

Prerequisites

Python 3.8+
CUDA 11.0+ (for GPU support)
8GB+ RAM (16GB recommended)

Installation

# Clone the repository
git clone <your-repo-url>
cd GPT-2

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Quick Start

1. Training from Scratch

from GPTMODEL import GPTModel
import torch

# Initialize model
config = GPT_CONFIG_124M
model = GPTModel(config)

# Load data and train
optimizer = torch.optim.AdamW(model.parameters(), lr=0.0004, weight_decay=0.1)
# ... training loop ...

2. Fine-Tuning for Classification

# Open the classification notebook
jupyter notebook "Fine_tuning/classification fineTuning.ipynb"

3. Text Generation

# Load trained model
checkpoint = torch.load("model_and_optimizer.pth")
model.load_state_dict(checkpoint["model_state_dict"])

# Generate text
input_text = "Once upon a time"
tokens = tokenizer.encode(input_text)
generated = model.generate(tokens, max_length=50)
print(tokenizer.decode(generated))

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📖 Learning Journey

Phase 1: Understanding Fundamentals

• 📝 Studied attention mechanism papers
• 🔧 Implemented self-attention from scratch
• 🎯 Understood positional encodings

Phase 2: Building Architecture

• 🏗️ Constructed transformer blocks
• 🔗 Implemented residual connections
• 📊 Added layer normalization

Phase 3: Training Pipeline

• 📦 Built data loading utilities
• 📉 Implemented training loops
• 💾 Added checkpointing system

Phase 4: Fine-Tuning

• 🎨 Experimented with classification tasks
• 🗣️ Implemented instruction tuning
• ⚡ Optimized training efficiency

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🛠️ Technical Highlights

Register Buffer Usage

self.register_buffer("mask", torch.triu(torch.ones(context_length, context_length), diagonal=1))

Why use register_buffer()?

• ✅ Not treated as learnable parameter
• ✅ Automatically moved to correct device with model
• ✅ Included in state_dict() for saving/loading
• ✅ Memory efficient for fixed tensors

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Contiguous Memory Layout

context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)

Why .contiguous()?

• After transpose(), tensor memory may be non-contiguous
• .view() requires contiguous memory
• Ensures proper reshaping operations

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Gradient Management

# Freeze early layers during fine-tuning
for param in model.trf_blocks[:6].parameters():
    param.requires_grad = False

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📚 References & Resources

Papers

• Attention Is All You Need - Original Transformer paper
• Language Models are Unsupervised Multitask Learners - GPT-2 paper
• BERT: Pre-training of Deep Bidirectional Transformers

Learning Resources

• The Illustrated Transformer by Jay Alammar
• The Illustrated GPT-2 by Jay Alammar
• Build GPT from scratch by Andrej Karpathy

Tools & Libraries

• PyTorch Documentation
• Hugging Face Transformers
• tiktoken - Fast BPE tokenizer

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🎓 Key Takeaways

1. Transformers are powerful - Self-attention allows modeling long-range dependencies
2. Residual connections are critical - Enable training deep networks
3. Pre-training + Fine-tuning works - Transfer learning is highly effective
4. Implementation details matter - Small choices (dropout, normalization order) impact results
5. Start simple, iterate - Build complexity gradually for better understanding

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🤝 Contributing

Feel free to:

• Open issues for bugs or questions
• Submit pull requests for improvements
• Share your own experiments and results

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📄 License

This project is for educational purposes. Model architecture follows the GPT-2 paper by OpenAI.

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🙏 Acknowledgments

• OpenAI for the GPT-2 architecture and paper
• PyTorch team for the excellent framework
• vizura youtube community

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⭐ If you found this helpful, please star the repository! ⭐

Made with ❤️ and lots of ☕ during my deep learning journey