Building Your Own LLM From Scratch: A Hands-On Workshop for Understanding AI Fundamentals

The field of AI has been dominated by massive models with billions of parameters that most developers can't fully comprehend. A new hands-on workshop aims to change that by enabling anyone to build a functional GPT model from scratch in just a few hours.

The llm-from-scratch project by developer Angelos P. provides a structured approach to understanding how large language models work by building a smaller (~10M parameter) GPT model from the ground up. This educational initiative addresses a critical gap in AI education - the black-box problem where developers use powerful models without understanding their underlying mechanics.

"Andrej Karpathy's nanoGPT was my first real exposure to LLMs and transformers," the project's creator explains. "Seeing how a working language model could be built in a few hundred lines of PyTorch completely changed how I thought about AI and inspired me to go deeper into the space. This workshop is my attempt to give others that same experience."

What sets this workshop apart from other educational resources is its practical, hands-on approach. Unlike tutorials that simply load pre-trained models with AutoModel.from_pretrained(), this project requires participants to build every component themselves - from tokenization to the transformer architecture, training loop, and text generation.

The workshop is carefully designed to be completed in a single session, with the entire training process taking under an hour on modern hardware. It offers three model configurations: Tiny (0.5M parameters, ~5 minutes), Small (4M parameters, 20 minutes), and Medium (10M parameters, ~45 minutes).

Participants will build a working GPT model capable of generating Shakespeare-like text by implementing:

• Tokenizer - Converting text into numerical representations
• Model architecture - The transformer components including embeddings, attention mechanisms, and feed-forward layers
• Training loop - Forward pass, loss calculation, backpropagation, optimization, and learning rate scheduling
• Text generation - Sampling methods including temperature and top-k sampling

The project uses character-level tokenization rather than Byte Pair Encoding (BPE) to work effectively with smaller datasets like Shakespeare (~1MB). This design choice reflects a practical understanding of educational constraints - BPE tokenization typically requires much larger datasets to be effective.

"BPE tokenization (GPT-2's 50k vocab) doesn't work on small datasets — most token bigrams are too rare for the model to learn patterns from," the documentation explains. "Part 5 covers switching to BPE for larger datasets."

The workshop is accessible to anyone with basic Python programming skills, requiring no prior machine learning experience. It automatically detects and utilizes available hardware including Apple Silicon (MPS), NVIDIA GPU (CUDA), or CPU, and can also run on Google Colab for those without local development environments.

Getting started is straightforward, with the project using Python's package manager uv for dependency management. After cloning the repository, participants work through six sequential parts, each building upon the previous one to create a complete pipeline by the end.

This approach to AI education comes at a crucial time as language models become increasingly prevalent in software development. By understanding how these models work at a fundamental level, developers can better apply them appropriately, troubleshoot issues, and contribute more meaningfully to the field.

The project draws inspiration from several key resources, including Andrej Karpathy's nanoGPT, microGPT, and the original "Attention Is All You Need" transformer paper. It represents a valuable addition to the growing ecosystem of practical AI education resources that prioritize understanding over blind application.

For developers looking to demystify the AI hype and gain genuine understanding of how large language models function, this workshop provides an accessible yet technically rigorous path to building knowledge through hands-on implementation.