From Scratch Implementation of Core Modules of Large Language Models: A Comprehensive Guide for Interview Preparation and Underlying Understanding

This project, llm-core-from-scratch, maintained by XUHIT, is published on GitHub (link: https://github.com/XUHIT/llm-core-from-scratch). It is a systematic hands-on tutorial for core LLM components. Using dual implementation approaches (NumPy and PyTorch), it explains 35 core modules one by one, covering everything from basic linear layers to a complete inference and training loop. It helps developers build an understanding of underlying mechanisms and is suitable for interview preparation, technical research, and educational scenarios.

Most current developers rely on ready-made interfaces from PyTorch or Hugging Face, but lack a thorough understanding of the underlying mechanisms of LLMs. This project aims to provide a systematic learning path, helping to build a complete understanding from theory to practice through hands-on implementation of core modules.

Dual-Track Implementation: Each module provides both a handwritten NumPy implementation (to see calculation details and tensor shape changes) and a PyTorch reference implementation (to verify correctness). Three-Tier Architecture:

1. P0 Basic Components: Pure NumPy implementation, including basic modules like tensor operations, linear layers, activation functions, etc.
2. P1 Core LLM Layers: Equal emphasis on NumPy and PyTorch, covering core modules such as attention mechanisms, positional encoding, feed-forward networks, etc.
3. P2 Inference and Training Loop: PyTorch-focused, including engineering modules like KV Cache, sampling strategies, optimizers, etc.

The project adopts the validation philosophy of "small numbers and readable examples", ensuring implementation correctness through synthetic tensors, fixed random seeds, and shape assertions. Each module has a unified structure: README (formula derivation + shape explanation), numpy_impl.py, torch_impl.py. Additionally, a scratchpad practice area is provided, supporting copying templates for handwritten implementation and quick verification.

For interviewees: Provides a question bank of high-frequency key points for hand-implementing LLM modules. For researchers/engineers: The NumPy implementation offers a "white-box" perspective, making the internal calculations of the framework transparent. For educators: The layered design is suitable for progressive teaching, helping students build intuition and engineering practice skills.

This project represents a learning attitude of returning to basics; hand-implementing underlying modules is an essential path to building technical depth. The existing 35 modules cover the complete path, and as LLM technology evolves, this kind of underlying understanding training will become even more valuable.