Math Behind Transformers and LLMs

This blog post provides an introduction to transformers and large language models, focusing on the OpenGPT-X project and the transformer neural network architecture. It explains the concept of language models as probability distributions for word sequences and their applications in natural language processing tasks like text generation and summarization. The post discusses the training process for large language models, including pre-training, fine-tuning, and inference, highlighting the computational demands and the use of GPUs for efficient matrix multiplications. It also covers traditional neural network architectures like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, pointing out their limitations in processing variable-length sequences. The introduction of transformers, based on the attention mechanism, is described as a breakthrough in NLP, enabling models to learn relationships between words efficiently without sequential processing. The post explains the components of a transformer block, such as queries, keys, and values, and outlines the forward-pass through a self-attention layer. Overall, it provides a comprehensive overview of the evolution from traditional neural networks to transformer architectures in the context of language modeling and NLP applications.