Building a 13 Billion Parameter LLM from Scratch Using Python — PART 3

Part 3: Scaling, Distributed Training, and Optimization

1. Overview and Recap

In Parts 1 and 2, we covered the fundamental motivations behind building a 13B parameter model, the architecture of Transformers, and the mathematical principles behind self-attention, multi-head attention, positional encoding, tokenization, and embedding layers. We also introduced basic building blocks and code examples for these components.

In this part, our focus shifts to the challenges of scaling up the architecture to billions of parameters and efficiently training such a model. The topics we cover now are critical for handling the enormous computational and memory demands that come with large-scale training.

2. Scaling Up the Architecture

Increasing Depth and Width

When scaling up your model, two primary factors come into play:

• Depth (Number of Layers):
  Increasing the number of Transformer layers allows the model to learn deeper hierarchical representations. However, deeper networks can lead to vanishing gradients and longer training times.
• Width (Embedding and Hidden…