Perusmallien

Perusmallien, in Finnish, means "base models" and refers to pre-trained machine learning models that provide a general-purpose starting point for downstream tasks. They are trained on broad data and large corpora to capture general patterns, representations, and capabilities that can be adapted to specific applications through transfer learning.

In practice, a base model is fine-tuned or adapted for particular tasks, domains, or languages. The base model learns representations during pre-training; subsequent fine-tuning aligns the model with task-specific labels. Sometimes, instruction tuning or alignment is applied to produce models that follow human intent more closely.

Common characteristics include large-scale architectures, extensive pretraining data, and substantial compute requirements. They are evaluated on general benchmarks, and downstream task performance depends on data quality, task similarity, and the chosen fine-tuning method. In natural language processing, base models include BERT, GPT, and T5; in computer vision, ResNet and ViT are frequently used as baselines.

Considerations for perusmallien include ethical and safety concerns such as bias and misinformation, licensing and access restrictions, reproducibility and evaluation standards, and environmental impact from training on large datasets.

See also: transfer learning, fine-tuning, base model, alignment.