Building an LLM Stack, Part 1: Implementing Encoders and Decoders

This article delves into the evolution of LLMs since the introduction of the Transformer architecture in 2017. It explores how models like GPT-3, LLaMA 2, and Mistral 7B have adapted and improved upon this foundational design. The discussion covers various aspects such as tokenization techniques (e.g., Byte Pair Encoding), positional encoding methods, self-attention mechanisms, and decoding strategies. It also highlights the importance of training data quality and fine-tuning techniques in enhancing model performance. Furthermore, it introduces Mamba, a novel sequence modeling approach that challenges the dominance of Transformer-based architectures by employing selective state space models (SSMs) and hardware-aware designs. The article concludes with an outlook on the future potential of LLMs, emphasizing the intersection of innovative architectural design and data optimization in advancing AI capabilities.

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