Model Training
Fine-tuning, PEFT and the choice between fine-tuning and RAG: when a model is worth adapting to your data and when it is wasted spend. These articles help you decide before the budget is committed.
What Parameter-Efficient Fine-Tuning Is and When It Pays Off
Parameter-efficient fine-tuning adapts a ready-made model to a specific task by training a fraction of a percent of its weights: the result weighs megabytes and trains in hours.
Fine-tuning or RAG — which one fits your task
Fine-tuning and RAG are two ways to adapt a model to a company's work: the first changes how it answers, the second gives it access to current documents.
Task-Specific Fine-Tuning — Explained Simply
Task-specific fine-tuning means adapting an off-the-shelf model on a company's own examples so that it answers in the required format and follows the rules of one particular process.
What is model fine-tuning and when it pays off
Fine-tuning adjusts an off-the-shelf model on a company's own examples so that it answers in the required format and tone; it adds no knowledge of the company's documents.