Building a Large Language Model with PyTorch

Overview

This hands-on workshop guides participants through the full process of building a GPT-style large language model in PyTorch, starting from core tensor operations and ending with pretraining, text generation, and fine-tuning for practical tasks. The workshop is designed for beginners and uses clear, step-by-step instruction, guided labs, and starter code throughout.

Participants will learn how raw text becomes model-ready data, how embeddings and attention work, how transformer blocks are assembled into a GPT architecture, and how to train and adapt the model for real use cases such as classification and instruction following.

The teaching approach emphasizes:

• Foundational concepts first
• Hands-on coding in PyTorch
• Guided exercises with starter code
• Simple explanations of core LLM building blocks
• Laptop-friendly examples and workflows

Learning Objectives

By the end of this workshop, participants will be able to:

• Explain, in simple terms, what a large language model is and how GPT-style models are built
• Use core PyTorch concepts including tensors, modules, parameters, and training loops
• Convert raw text into tokenized, padded, and batched training examples
• Describe and implement tokenization approaches, including Byte Pair Encoding concepts used in GPT-style tokenizers
• Create next-token prediction datasets using sliding windows over token sequences
• Implement token embeddings and positional embeddings in PyTorch
• Explain the intuition behind attention and why it improves sequence modeling
• Code self-attention, causal masking, and multi-head attention step by step
• Build transformer blocks and assemble a small GPT-style language model from scratch
• Generate text from model outputs using decoding loops
• Train a small language model on unlabeled text data and track loss during pretraining
• Improve text generation with practical decoding methods and load pretrained weights
• Fine-tune a GPT model for text classification
• Fine-tune a GPT model on instruction-response data and evaluate generated outputs

Target Audience

This workshop is intended for:

• Developers who are new to large language models
• Data and ML beginners who want a practical introduction to GPT-style architectures
• Python users who want to understand how modern text generation models work internally
• Educators, technical professionals, and students seeking a hands-on introduction to PyTorch-based LLM development

Prerequisites

Participants should have:

• Basic Python programming experience
• Comfort running Python scripts or notebooks
• A general understanding of variables, functions, loops, and lists
• Interest in machine learning concepts, but no prior LLM or deep learning experience is required

Helpful but optional:

• Familiarity with Jupyter notebooks
• Basic command-line usage
• Introductory exposure to linear algebra concepts such as vectors and matrices

Workshop Format

The workshop combines:

• Short instructor-led concept sessions
• Live coding demonstrations
• Guided labs with starter code
• Structured checkpoints and troubleshooting help
• Incremental model building across the week

Participants leave with a working end-to-end project and a clear mental model of how GPT-style language models are built, trained, and adapted in PyTorch.

Day 1 - PyTorch Foundations and Text Preparation

Topics

• Welcome, workshop setup, and roadmap for building an LLM from scratch
• What large language models are and how GPT-style systems work at a high level
• Core PyTorch concepts:
  • tensors
  • shapes
  • basic tensor operations
  • modules and parameters
  • forward passes
• From raw text to model-ready data
• Simple tokenization and vocabulary building
• Token IDs, padding, batching, and sequence formatting
• Byte Pair Encoding intuition and GPT-style tokenization
• Creating next-token prediction examples with sliding windows
• Token embeddings and positional embeddings

Activities

• Guided PyTorch warm-up exercises
• Walkthrough: converting text into token sequences step by step
• Lab: build a simple tokenizer and inspect tokenized outputs
• Lab: create input-target pairs for next-token prediction
• Lab: implement embedding layers and visualize tensor shapes

Day 1 Outcome

Participants understand the end-to-end data pipeline that turns text into vectors suitable for a GPT-style model.

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Day 2 - Attention and GPT-Style Sequence Modeling

Topics

• Why older sequence models struggle with long-range dependencies
• Attention intuition: letting each token focus on relevant context
• Self-attention concepts using small numerical examples
• Coding self-attention manually in PyTorch
• Queries, keys, values, and attention scores
• Causal masking for next-token prediction
• Multi-head attention and why multiple heads help

Activities

• Instructor-led visual explanation of attention flow
• Live coding: self-attention from scratch
• Lab: compute attention scores and weights by hand and in PyTorch
• Lab: add causal masking to prevent access to future tokens
• Lab: refactor into reusable PyTorch modules

ASCII Concept Sketch

Token t_i
   |
   +--> Query
   +--> Key
   +--> Value

Attention(t_i) = weighted sum of Values from allowed tokens

Day 2 Outcome

Participants can explain and implement self-attention, causal attention, and multi-head attention in PyTorch.

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Day 3 - Building Transformer Blocks

Topics

• The overall GPT architecture
• Transformer blocks as the core reusable component
• Layer composition:
  • embeddings
  • attention
  • feed-forward network
  • normalization
  • residual connections
• Implementing feed-forward layers in PyTorch
• Understanding residual paths and layer normalization
• Inspecting tensor shapes and outputs at each stage

Activities

• Live coding: assemble a transformer block piece by piece
• Lab: test each block independently with starter code
• Lab: trace activations through a transformer block
• Debugging session: inspect tensor shapes and intermediate outputs

Day 3 Outcome

Participants can build and validate the main components of a transformer block in PyTorch.

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Day 4 - Assembling a GPT Model

Topics

• Connecting embeddings, transformer blocks, and output heads
• Building a small GPT model from scratch in PyTorch
• Configuring vocabulary size, context length, embedding size, and depth
• Forward propagation through the full model
• Producing next-token logits from hidden states
• Comparing outputs from different model sizes and settings

Activities

• Live coding: connect blocks into a working GPT model
• Lab: run sample inputs through the full architecture
• Lab: verify model outputs and tensor dimensions
• Lab: experiment with small architectural configurations

ASCII Architecture Sketch

Input Tokens
     |
[Token Embeddings + Positional Embeddings]
     |
[Transformer Block] x N
     |
  Linear Head
     |
 Next-Token Logits

Day 4 Outcome

Participants build and run a complete GPT-style model that produces next-token predictions.

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Day 5 - Text Generation Basics

Topics

• Autoregressive generation and decoding loops
• Logits, probabilities, and token selection
• Greedy decoding and temperature scaling
• Top-k sampling and simple sampling strategies
• Comparing generation from untrained and partially trained models
• Practical debugging of repetitive or unstable generations

Activities

• Live coding: implement autoregressive text generation
• Lab: generate text with different decoding settings
• Lab: compare outputs from untrained and partially trained models
• Debugging session: inspect logits, predictions, and generated text

Day 5 Outcome

Participants can generate text from a GPT-style model and understand how decoding choices affect output.

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Day 6 - Pretraining Objectives and Training Loops

Topics

• Language modeling objective and next-token prediction loss
• Pretraining on unlabeled text data
• Training loops in PyTorch:
  • forward pass
  • loss computation
  • backward pass
  • optimizer step
• Gradient flow and parameter updates
• Tracking progress with training and validation loss

Activities

• Live coding: implement a simple pretraining loop
• Lab: compute loss for next-token prediction
• Lab: step through backpropagation and optimizer updates
• Lab: record and visualize training and validation loss

Day 6 Outcome

Participants can implement the core training loop needed to pretrain a small language model.

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Day 7 - Running Small-Scale Pretraining Experiments

Topics

• Running laptop-friendly pretraining experiments
• Choosing batch size, sequence length, and learning rate
• Monitoring overfitting and underfitting
• Inspecting generated samples during training
• Saving checkpoints and resuming training
• Comparing from-scratch models with pretrained checkpoints

Activities

• Lab: train a small model on a sample corpus
• Lab: evaluate loss and inspect generated samples during training
• Lab: save and reload checkpoints
• Lab: compare a freshly initialized model with a pretrained model

Day 7 Outcome

Participants can run, monitor, and manage practical small-scale pretraining experiments in PyTorch.

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Day 8 - Using Pretrained Weights and Practical Inference

Topics

• Loading and using pretrained model weights
• Preparing a model for inference
• Efficient text generation workflows
• Prompting patterns for better outputs
• Trying different decoding options for higher-quality text
• Practical tradeoffs between custom training and pretrained reuse

Activities

• Lab: load pretrained weights and run inference
• Lab: compare outputs under different prompting styles
• Lab: try different decoding options for better text output
• Lab: benchmark simple inference settings

Day 8 Outcome

Participants can use pretrained GPT-style models effectively for practical text generation tasks.

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Day 9 - Fine-Tuning GPT for Classification

Topics

• Fine-tuning versus pretraining
• Adapting GPT for text classification
• Guided example: spam classification with a pretrained GPT model
• Preparing labeled datasets for supervised fine-tuning
• Training loops for classification tasks
• Evaluating accuracy, errors, and prediction confidence

Activities

• Lab: fine-tune GPT for classification with starter code
• Lab: evaluate accuracy and inspect prediction errors
• Lab: compare classification heads and output strategies
• Debugging session: analyze common failure cases

Day 9 Outcome

Participants gain hands-on experience adapting GPT models for classification tasks using PyTorch.

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Day 10 - Instruction Fine-Tuning, Evaluation, and Wrap-Up

Topics

• Batching and formatting instruction-response data
• Fine-tuning GPT to follow instructions
• Evaluating outputs for generation tasks
• Comparing base model behavior with fine-tuned behavior
• Practical discussion: limitations, responsible use, and next steps for continued learning
• Final review of the full LLM pipeline from data preparation to deployment-ready workflows

Activities

• Lab: prepare an instruction dataset for tuning
• Lab: fine-tune GPT on instruction-following examples
• Final exercise: compare base model behavior with fine-tuned behavior
• Wrap-up and Q&A

Day 10 Outcome

Participants leave with hands-on experience fine-tuning GPT models for instruction-following and a clear roadmap for continued learning with PyTorch.