Base models versus customized models
The nice thing about Large Language Models is that they have been trained and ready to use. As we saw in the previous section, training an LLM requires great investment in hardware (GPUs or TPUs) and it might last for months, hardly feasible from individuals. Luckily, pre-trained LLM are generalized enough to be applicable at various tasks, so they can be consumed as they are directly via their REST API (we will dive deeper into model consumption in next chapters). Nevertheless, there might be scenarios where a general-purpose LLM is not enough, since it lacks domain-specific knowledge or doesn’t conform to a particular style and taxonomy of communication. If this is the case, you might want to customize your model.
How to customize your model
There are three main ways to customize your model:
- Extending non-parametric knowledge. This allows the model to access external sources of information to integrate its parametric knowledge while responding...
