📦 Module 5: Assembling & Pretraining Our GPT

[malibayram]

📦 Module 5: Assembling & Pretraining Our GPT

This module combines all components into a complete GPT model and implements the pretraining process.

Tasks to Complete:

• Lesson 5.1 — Stacking Decoder Blocks & Output Head

  • Stack multiple Transformer decoder blocks
  • Add final linear projection to vocabulary size
  • Implement the complete GPT architecture
  • Handle model initialization and parameter counting

• Lesson 5.2 — Objective: Next Token Prediction & Loss Function

  • Implement next-token prediction objective
  • Setup CrossEntropyLoss for language modeling
  • Handle label shifting for autoregressive training
  • Test loss calculation with sample data

• Lesson 5.3 — Optimizer Setup & Learning Rate Scheduler

  • Configure AdamW optimizer
  • Implement cosine learning rate schedule with warmup
  • Set appropriate hyperparameters
  • Add weight decay and gradient clipping

• Lesson 5.4 — Pretraining Loop Pt. 1: Forward + Backward

  • Implement forward pass through complete model
  • Calculate loss and perform backward pass
  • Handle batch processing and GPU utilization
  • Add basic logging and monitoring

• Lesson 5.5 — Pretraining Loop Pt. 2: Gradient Clipping & Logging

  • Implement gradient clipping for stability
  • Add comprehensive logging (loss, learning rate, etc.)
  • Monitor training metrics and model parameters
  • Setup model checkpointing

• Lesson 5.6 — Running Pretraining & Monitoring Loss Curves

  • Execute full pretraining on WikiText dataset
  • Monitor and plot loss curves
  • Track training progress and convergence
  • Save trained model checkpoints

• Lesson 5.7 — Inference with Your Trained Model

  • Implement greedy decoding for text generation
  • Add sampling strategies (temperature, top-k, top-p)
  • Create text generation interface
  • Test model capabilities and outputs

Deliverables:

• 7 video lectures (~25 minutes each)
• Complete GPT model implementation
• Pretraining pipeline notebook
• Training monitoring and visualization tools
• Inference and text generation notebook
• Trained model checkpoints
• Module quiz

Key Implementation Files:

• gpt_model.py - Complete GPT architecture
• training_loop.py - Pretraining implementation
• optimizer_config.py - Optimizer and scheduler setup
• inference.py - Text generation and sampling
• monitoring.py - Training metrics and logging
• Pretraining configuration files

Training Components:

• Model architecture assembly
• Loss function implementation
• Optimizer configuration (AdamW)
• Learning rate scheduling
• Gradient clipping
• Checkpointing system
• Progress monitoring
• Inference pipeline

Resources:

• GPT-1, GPT-2, GPT-3 papers
• AdamW optimizer paper
• Learning rate scheduling strategies
• WikiText dataset for pretraining