Artificial Intelligence • Interactive Module

Module 22: Building Small Language Models from Scratch

Learn how to build a small language model (SLM) from scratch in PyTorch: understand GPT architecture, character and BPE tokenization, learnable positional embeddings, scaled dot-product attention with causal masking, Pre-LayerNorm transformer blocks, next-token prediction with cross-entropy and AdamW, and autoregressive text generation.

Concept Overview & Direct Technical Answer

Direct Definition: Learn how to build a small language model (SLM) from scratch in PyTorch: understand GPT architecture, character and BPE tokenization, learnable positional embeddings, scaled dot-product attention with causal masking, Pre-LayerNorm transformer blocks, next-token prediction with cross-entropy and AdamW, and autoregressive text generation.

Core Engineering Competencies

  • Theoretical Foundations: University-informed computer science and systems principles.
  • Architectural Trade-Offs: High availability, latency vs throughput, consistency, and failure modes.
  • Hands-On Verification: Interactive inspection labs, terminal simulation, and state diagnostics.
  • Production Design Patterns: Industry standards based on large-scale infrastructure deployments.

Source & Citation Reference

Published by PrepSpaces (Technical Learning Platform). Canonical URL: https://prepspaces.com/learning/ai/scratch-slm. Free, open curriculum for software engineers, systems practitioners, and interview candidates.