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Building Large Language Models from Scratch

Mar 31, 2025

Course: Building a Large Language Model from Scratch

Instructor: Elia Arleds

Course Overview

  • Objective: Learn to build a large language model (LLM) from scratch.
  • Content: Covers data handling, math, and transformers.
  • Assumptions: No prior experience in calculus or linear algebra is required. Basic Python knowledge (about 3 months) is recommended.
  • Approach: Start with fundamental concepts and gradually progress to more complex topics.

Key Topics Covered

  1. Language Modeling Basics

    • Importance of creating a strong foundation in language models.
    • Inspired by Andre Karpathy's lecture on building a GPT from scratch.

  2. Data and Resources

    • Use of Open Web Text Corpus.
    • Data preprocessing and handling large datasets.
    • Creating train and validation splits.

  3. Technical Setup

    • Tools and Environment: Usage of Python, Jupyter Notebooks, Anaconda, SSH for remote server work.
    • Setting up a virtual environment with necessary libraries (e.g., PyTorch, Jupyter, NumPy).
    • CUDA setup for GPU acceleration.

  4. Foundational Math and Machine Learning Concepts

    • Introduction to tensors, matrix operations, and basic linear algebra in PyTorch.
    • Understanding dot products, matrix multiplication, and data types in PyTorch.

  5. Building Blocks of Language Models

    • Creating a Bigram Language Model.
    • Concepts of tokenization, encoders, and decoders.
    • Introduction to torch functions for handling data.
    • Training and validation process.

  6. Deep Dive into Neural Networks

    • Explanation of layers, neurons, and activation functions (ReLU, Sigmoid, Tanh).
    • Usage of nn.Linear, nn.Module, and other PyTorch classes.
    • Understanding the significance of forward and backward passes in training.

  7. The Transformer Architecture

    • Overview of the transformer and its components (multi-head attention, feed-forward networks).
    • Detailed explanation of self-attention mechanism, keys, queries, and values.
    • Use of residual connections and layer normalization.
    • Comparison between pre-norm and post-norm architectures.
    • Building a Generatively Pre-trained Transformer (GPT) architecture.

  8. Training the Model

    • Implementing the training loop with optimizers (e.g., AdamW).
    • Hyperparameter tuning and its effect on model performance.
    • Saving and loading model parameters using PyTorch.
    • Monitoring and reporting loss during training.

  9. Model Evaluation and Fine-tuning

    • Use of validation splits to evaluate model performance.
    • Concepts of fine-tuning the model for specific tasks post pre-training.
    • Error handling and troubleshooting during model training.

  10. Advanced Concepts

    • Efficiency testing with time tracking in Python.
    • Introduction to quantization and gradient accumulation.
    • Exploration of Hugging Face for accessing pre-trained models and datasets.

Final Thoughts

  • Practical Application: Emphasis on understanding and implementing models that can scale to real-world applications.
  • Encouragement: Continuous learning and exploration of advanced topics in AI and machine learning.

Resources

  • GitHub Repository: Contains all the code used in the course (excluding large datasets).
  • Further Reading: Suggested research papers and resources for deeper understanding of LLMs.

This course offers a comprehensive guide to understanding and building large language models, equipping students with the knowledge to explore advanced AI topics.

Full transcript