Train Your Own LLM from Scratch (github.com)

Article summary

The article presents a hands-on workshop where participants can train their own language model from scratch, understanding each component of the pipeline. The project is based on nanoGPT and is designed to be completed in a single session, resulting in a working GPT model capable of generating Shakespeare-like text. The model is scaled down to 10M parameters, allowing it to train on a laptop in under an hour. The workshop covers tokenization, model architecture, training loop, and text generation.

Main themes

• Language Model Training
• LLM Development
• AI Education
• Model Scaling
• GPU Computing

What commenters say

• The workshop is a great introduction to training language models from scratch, making it accessible to those without extensive ML experience.
• The term 'Large Language Model' is subjective and can be misleading, as the definition of 'large' is constantly changing with advancements in hardware.
• Training a language model on a single machine is feasible, but the scale and complexity of the model are limited by the available hardware resources.
• The distinction between 'large' and 'small' language models is not well-defined, and the terminology may become outdated as models continue to grow in size and capability.
• Renting cloud computing resources can be a viable option for training larger language models, making it more accessible to individuals without specialized hardware.
• The workshop's approach to training a language model from scratch is similar to other existing resources, such as Andrej Karpathy's nanoGPT and Sebastian Raschka's book on building large language models.