The document also provides instructions on how to use the model, both in a simple and advanced manner. In the simple usage, the model is trained on .txt files located in a /training/documents/ directory. In the advanced usage, users can either use a bootstrapped dataset and embeddings or train the model on their own text files. The document also mentions that the model was inspired by a 3Blue1Brown video on YouTube and that the goal is to provide a high-quality token prediction library and demystify language models.
Key takeaways:
- The Next-Token Prediction is a language model that can predict the next word, phrase, or pixel based on a body of text. It can be used as an alternative to decoder-only models like GPT and Mistral.
- The model can be installed using npm and can be used in two ways: simple (from a built-in data bootstrap) and advanced (providing 'trainingData' or creating it from .txt files).
- The model can be used for auto-completion, auto-correct, spell checking, and search/lookup. It aims to demystify LLMs and simplify methodologies.
- The project is inspired by a 3Blue1Brown video on YouTube and aims to create a high-quality, free/open LLM in JavaScript.