Open-Source LLMs
In prior chapters, we’ve seen how Large Language Models (LLMs) are extremely complex, with potentially trillions of parameters and hard-to-quantify accuracy. Another inherent challenge in working with these systems, though, is their lack of transparency. Many models are proprietary – the whitepaper for GPT-4 states up front that “Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar.”1 With few details about the training, exact architecture, and infrastructure implementation of models, understanding innovations in model structure and performance and developing improvements outside corporate labs becomes challenging. Luckily, the ability to experiment with state-of-the-art models is provided by a set of open-source LLMs that, with...
