Building Your Own Large Language Model

Aug 27, 2024

Course Notes: Building Your Own Large Language Model

Course Overview

  • Instructor: Elia Arleds
  • Description: Learn how to build large language models (LLMs) from scratch, covering data handling, math, and transformers behind LLMs.
  • Prerequisites:
    • No calculus or linear algebra experience required.
    • Familiarity with Python (3 months experience recommended).

Key Concepts

Learning Approach

  • Build from the basics: Start with fundamental concepts before moving to complex topics.
  • Use of analogies and step-by-step examples.
  • Focus on local computation (no paid datasets or cloud computing).

Data Requirements

  • Training Data Size: Scale data to 45GB for training.
  • Data Setup:
    • Use tools like SSH for remote coding.
    • Maintain a virtual environment for project isolation.

Course Structure

Tools and Technologies

  • Jupyter Notebooks: For coding and experimentation.
  • Python Libraries:
    • matplotlib, numpy, torch (PyTorch).
    • visual studio build tools for certain library installations.

Setting Up the Environment

  1. Install Anaconda and set up Jupyter notebooks.
  2. Create a virtual environment for the course.
  3. Install necessary libraries using pip.
  4. Set up CUDA for GPU acceleration.

Building the Model

  1. Initialization:
    • Define parameters: vocab size, embedding size, model layers.
  2. Forward Pass:
    • Pass inputs through token embeddings and positional encodings.
    • Use multi-head attention to capture relationships between tokens.
  3. Training Loop:
    • Implement loss calculation and optimization steps.
    • Track training and validation losses.
  4. Checkpointing:
    • Save model parameters using torch.save() for later retrieval.

Important Functions and Techniques

Tokenization and Embedding

  • Tokenization: Break down text into manageable units.
  • Embedding: Use nn.Embedding layer to convert tokens into dense vectors.

Attention Mechanisms

  • Self-Attention: Focus on relevant parts of the input sequence.
  • Multi-Head Attention: Use multiple attention heads to capture diverse relationships.
  • Scaled Dot-Product Attention: Normalize attention scores to prevent exploding gradients.

Optimization Techniques

  • Adam Optimizer: Combines momentum and RMSprop techniques for efficient learning.
  • Learning Rate: Crucial for convergence; often tested through experimentation.
  • Gradient Accumulation: Update model weights every few iterations to handle larger datasets.

Metrics and Performance

  • Measure model performance using loss metrics (cross-entropy loss).
  • Evaluate train and validation loss to ensure model generalization.

Practical Applications

Fine-Tuning

  • Adjust pre-trained models on specific tasks using smaller datasets.
  • Utilize generative models to generate coherent text based on learned patterns.

Conclusion

  • This course equips learners with the skills to build and train LLMs, emphasizing practical coding skills and understanding of underlying mechanisms.
  • Additional Resources:
    • GitHub repo with course code.
    • Recommended reading: "Survey of Large Language Models".