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LM from First Principles

A Notebook-Driven Guide to Building and Deploying Language Models

LM from First Principles
Amarpreet Singh Bassan

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About the Book

LM from First Principles is a notebook-driven guide to building and deploying language models from the ground up. Starting from a 9,025-parameter bigram that predicts one character at a time, each chapter adds one layer of capability — feedforward context, sampling strategies, self-attention, scaling — until you have a model you can push to Hugging Face. Every concept is grounded in a runnable Jupyter notebook, so you do not just read about how language models work; you watch them train, fail, improve, and generate. The book is written for practitioners who want to understand what is happening inside the model, not just call an API. You need Python and curiosity. Everything else is built from first principles.

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Computer Programming/Artificial Intelligence/Python

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About the Author

Amarpreet Singh Bassan

Amarpreet Singh Bassan is a senior software engineer with 19 years of experience building reliable software systems. His current work focuses on production debugging and improving the reliability of production systems at scale. Outside of his core engineering work, he builds open-source proof-of-concept projects to explore AI engineering, agentic workflows, memory, observability, grounded retrieval, and practical debugging support. He is an IEEE Senior Member and writes about his learning journey in software engineering, AI systems, and reliable production architecture.

Contents

Table of Contents

LM from First Principles

  1. A Notebook-Driven Guide to Building and Deploying Language Models

Chapter 1: The Data Pipeline — Text, Tensors, and GPU

  1. What You Will Learn
  2. Setup
  3. Getting the Corpus
  4. Building the Vocabulary
  5. The Lookup Tables
  6. From List to Tensor
  7. Train and Validation Split
  8. Moving to the GPU
  9. Building Batches
  10. A First Model: The Bigram
  11. Training
  12. Evaluation: Loss and Perplexity
  13. The Greedy Collapse
  14. How This Connects to Modern LMs and Inference Engineering
  15. Chapter Summary
  16. What’s Next
  17. The Journey
  18. Review Questions

Chapter 2: Vectors — The Geometry Behind Language Models

  1. What You Will Learn
  2. Setup
  3. 1. Revisit Chapter 1: What Was That Row of 95 Numbers?
  4. 2. Scalar, Vector, Matrix, Tensor
  5. 3. Rank, Shape, Number of Elements, and Dtype
  6. 4. Reading (B, T, C)
  7. 5. Coordinates, Magnitude, and Direction
  8. 6. Vector Addition and Scalar Multiplication
  9. 7. The Dot Product
  10. 8. Distance and Similarity
  11. 9. Matrices as Collections of Vectors
  12. 10. Matrix Multiplication as Transformation
  13. 11. Row Vectors, Column Vectors, and Orientation
  14. 12. Reshaping and Flattening
  15. 13. High-Dimensional Spaces
  16. 14. PyTorch Lab — Becoming Fluent in Vector Shapes
  17. 15. Research Connections: How Papers Enter This Chapter
  18. 16. Where These Vector Operations Reappear
  19. 17. What Chapter 2 Deliberately Does Not Explain
  20. Chapter Summary
  21. What’s Next
  22. Review Questions

Interlude — Can the Whole Sequence Be One Vector?

  1. Bigram: only one position contributes
  2. Flattening works
  3. But flattening is brittle
  4. Shared transformations remove the slot-specific wiring
  5. But the rows still cannot communicate

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