Temel Model
Large-scale model that can be trained with wide and varied data.
The basic model (foundation model) describes large-scale artificial intelligence models, which can be adapted to many different tasks with its very large and varied data clusters, then fine-tuning (fine-tuning), prompt engineering or adapter techniques. The term was first used by researchers at Stanford, to name the new paradigm that models such as GPT-3 are revealed: instead of arranging a special model from scratch for each task, it has become possible to build on a wide and general "themel". These models are trained with self-supervised learning, without requiring labeled data, they learn from the structure of text or data.
The importance of basic models comes from gaining unexpected, emerging (emergent) talents of the model, as the scale grows: language translation, code writing, logic execution, even if the model is not specifically trained for these tasks. Models such as GPT, Claude, Gemini, Llama and BERT are the basic model; companies use these models as the basis of many different products from customer service bot to medical text analysis. However, this power is an up-to-date discussion of basic models, as well as risks such as bias, security and calculation cost.
The importance of basic models comes from gaining unexpected, emerging (emergent) talents of the model, as the scale grows: language translation, code writing, logic execution, even if the model is not specifically trained for these tasks. Models such as GPT, Claude, Gemini, Llama and BERT are the basic model; companies use these models as the basis of many different products from customer service bot to medical text analysis. However, this power is an up-to-date discussion of basic models, as well as risks such as bias, security and calculation cost.