Clear explanations of AI, DX, and technology terminology
A base model (Foundation Model) is a general-purpose AI model pre-trained on large-scale datasets. Rather than being specialized for a specific task, it functions as a "foundation" that can be adapted to a wide range of applications through fine-tuning or prompt engineering.
A Dense Model is a neural network architecture in which all of the model's parameters are used for computation during inference. In contrast to MoE (Mixture of Experts), which activates only a subset of experts, a Dense Model always involves all weights in computation regardless of the input.
Fine-tuning refers to the process of providing additional training data to a pre-trained machine learning model in order to adapt it to a specific task or domain.
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