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Module 4 Overview: Language Models with NanoGPT

Foundation Module

Time: 15-25 hours over 2 weeks

Learning Objectives

After completing this module, you will be able to:

  • Complete GPT Understanding: Gain line-by-line understanding of how GPT works, from tokenization to generation
  • Decoder-Only Transformers: Master causal self-attention and autoregressive generation, the core of modern LLMs
  • Training Large Models: Learn practical training techniques: gradient accumulation, learning rate schedules, checkpointing
  • Healthcare Sequence Foundation: Prepare to tokenize and model patient event sequences as language modeling problems

Why This Module Matters

This module provides complete, code-level understanding of how GPT-style language models are built and trained. By working through Andrej Karpathy’s NanoGPT implementation, you’ll solidify your transformer knowledge and learn the practical details of training large models.

Why building GPT from scratch is essential:

  • Hands-on implementation cements transformer understanding
  • Learn the practical training tricks that papers don’t mention
  • Understand how to apply language modeling to any sequential data
  • Foundation for understanding modern LLMs (GPT-4, Claude, etc.)

Connection to Healthcare AI

GPT-style models apply directly to healthcare:

  • Patient Trajectories: Model sequences of clinical events as language modeling
  • Clinical Notes: ClinicalGPT and Med-PaLM for clinical text generation
  • Event Prediction: Autoregressive prediction of next medical event
  • Sequential EHR Data: Apply GPT architecture to ICD codes, medication sequences

Your healthcare AI applications may use encoder-only (BERT-style) or decoder-only (GPT-style) transformers, or both. Understanding GPT gives you the complete picture.

Prerequisites

Before starting this module:

  • Module 3: Deep understanding of attention and transformers (required)
  • PyTorch: Comfortable with nn.Module, training loops, optimizers
  • Time: Block out substantial time—this module requires focused, uninterrupted work

Module Path

Follow Language Models with NanoGPT Learning Path for the complete hands-on curriculum.

Key concepts covered:

  1. Tokenization - BPE and subword encoding
  2. Causal Attention - Masked self-attention for autoregression
  3. GPT Architecture - Decoder-only transformer
  4. Language Model Training - Gradient accumulation, schedules
  5. Text Generation - Sampling strategies (greedy, top-k, top-p, beam)
  6. Scaling Laws - Understanding model size vs compute
  7. LLM Applications - Deployment and fine-tuning

Primary Resource

The primary resource for this module is Andrej Karpathy’s NanoGPT repository  and his 2-hour video tutorial “Let’s Build GPT” .

You must code along with the video—watching passively won’t be sufficient.

Remember: There’s no substitute for building things from scratch. The best way to understand GPT is to implement it yourself, one line at a time.

Critical Checkpoints

Must complete before proceeding to advanced modules:

  • ✅ Watched Andrej Karpathy’s “Let’s Build GPT” video (2 hours)
  • ✅ Coded along and implemented GPT from scratch in PyTorch
  • ✅ Understand causal masking and why it’s needed for autoregression
  • ✅ Can explain BPE tokenization algorithm
  • ✅ Implemented gradient accumulation for larger batch sizes
  • ✅ Understand the difference between greedy, top-k, and nucleus sampling
  • ✅ Trained a GPT model on a small dataset (Shakespeare or similar)
  • ✅ Generated text and understand what the model learned

Time Breakdown

Total: 15-25 hours over 2 weeks

  • Videos: 3-4 hours (Karpathy’s tutorial, supplementary lectures)
  • Coding along: 8-12 hours (implementing GPT from scratch)
  • Training experiments: 3-5 hours (train models, experiment with hyperparameters)
  • Reading: 2-3 hours (GPT papers, scaling laws)
  • Exercises: 2-3 hours

Important: The coding portion takes longer than you expect. Budget uninterrupted time blocks.

Key Architecture Insights

Decoder-Only Transformer:

  • Only uses self-attention (no cross-attention)
  • Causal masking prevents looking at future tokens
  • Simpler than encoder-decoder, but scales to massive sizes

Autoregressive Generation:

  • Model predicts next token given all previous tokens
  • Generation is sequential (can’t parallelize)
  • KV caching makes generation efficient

Scaling Properties:

  • GPT-2: 117M → 1.5B parameters
  • GPT-3: 175B parameters
  • Performance scales smoothly with model size, data, and compute

Key Takeaway

Implementation is understanding.

You can read about GPT in papers, but you won’t truly understand it until you’ve debugged your own implementation. The struggle of making it work—fixing shape mismatches, understanding why loss isn’t decreasing, tuning learning rates—is where deep understanding emerges. Embrace the implementation process.

Next Steps

After completing this module:

  1. Advanced: Advanced Deep Learning Topics
  2. Multimodal: Vision-Language Models
  3. Generative: Diffusion Models
  4. Healthcare: Clinical Language Models
  5. Healthcare: Healthcare EHR Analysis

Ready to start? Begin with Language Models with NanoGPT Learning Path.

Module 3: TransformersAdvanced Overview