Train Your Own LLM from Scratch
The article describes a hands-on workshop that teaches participants to build a small GPT-style language model from scratch using PyTorch. It emphasizes understanding each component of the training pipeline without relying on pre-built libraries or models. The workshop is designed to run on a laptop and results in a model capable of generating Shakespeare-like text.
- ▪The workshop guides users to build a GPT model with around 10 million parameters that can train in under an hour on a laptop.
- ▪Participants implement every part of the pipeline, including tokenization, transformer architecture, training loop, and text generation.
- ▪The project uses character-level tokenization and supports execution on Apple Silicon, NVIDIA GPUs, or CPU, including Google Colab.
- ▪It is inspired by Andrej Karpathy's nanoGPT and aims to make LLM mechanics accessible without requiring prior machine learning experience.
- ▪The workshop includes six parts, culminating in a competition to train the best AI poet using custom datasets.
Opening excerpt (first ~120 words) tap to expand
Train Your Own LLM From Scratch A hands-on workshop where you write every piece of a GPT training pipeline yourself, understanding what each component does and why. Andrej Karpathy's nanoGPT was my first real exposure to LLMs and transformers. 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. nanoGPT targets reproducing GPT-2 (124M params) and covers a lot of ground. This project strips it down to the essentials and scales it to a ~10M param model that trains on a laptop in under an hour — designed to be completed in a single workshop session. No black-box libraries.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News: Front Page.
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