A maximally tiny language model

This blog post introduces TinyLM, an extremely minimal language model that can be trained on a single CPU core. It explores how small a model can be while still performing a task, using a closed set of predefined sentences. The author discusses model accuracy, training techniques, and the implications of reduced complexity in language processing. Key aspects include the bag of words approach, learned embeddings, and basic model architecture without attention mechanisms. The post provides a comprehensive breakdown of the model’s structure, training process, and results while hinting at necessary changes for broader language capabilities.