A Guide to Building an LLM from Scratch

Building a large language model (LLM) from scratch has become increasingly feasible for organizations of all sizes, thanks to growing knowledge and resources. The process involves defining the use case, creating the model architecture, curating data, training the LLM, fine-tuning it, and evaluating its performance. Key factors influencing the complexity and time required include the intended use case, available computational resources, and quality of training data. Evaluating an LLM can be done using standardized benchmarks to measure various aspects such as knowledge, reasoning, natural language understanding, and more.