Model architecture is the structural design of a machine-learning model: the choices that organize its components and shape what it can do. For a custom large language model, architecture is one lever alongside training data, fine-tuning, and configuration. section 9.1
Architecture becomes useful when it is treated as a practical design choice rather than mysterious AI plumbing:
- Tailor the architecture to a particular task, industry, application, or niche domain. section 9.1
- Balance performance and efficiency by adjusting architecture, training methods, and configuration settings. section 9.1
- Keep control of how the model is modified, extended, and distributed instead of accepting the limits of an existing pretrained model. section 9.1
A domain-specific model
A general-purpose model such as GPT may work across many domains, while a custom model can be designed and trained for a specialized use case. That can improve relevance, efficiency, privacy, or control—but it also means taking responsibility for the design choices. section 9.1
