Finetunable

Finetunable is an adjective used to describe software, models, or components that can be adapted to a new task or domain through a controlled adjustment of their parameters, typically via a process known as fine-tuning. In machine learning, finetunability refers to the ability of a pre-trained model to be specialized after initial training to improve performance on a specific dataset or objective, without requiring training from scratch.

In practice, finetuning involves continuing the optimization of the model on task-specific labeled data, usually with a smaller learning rate and often with some layers kept frozen. It contrasts with full retraining and with systems that are not easily adaptable. The goal is to transfer learned representations to a new target while preserving useful general features.

Techniques to achieve finetunability include full fine-tuning, where all parameters are updated, as well as parameter-efficient fine-tuning (PEFT) methods such as adapters, LoRA (low-rank adapters), prefix-tuning, and BitFit. PEFT updates a small subset of parameters or introduces additional trainable modules, reducing computational cost and data requirements while enabling rapid adaptation.

Considerations for finetunability include data availability and quality, compute resources, and risk factors such as overfitting and catastrophic forgetting. Domain shift between the pre-training and target tasks can affect effectiveness, and careful evaluation using task-specific metrics is important.

Common contexts for finetuning include large language models like BERT, GPT, and T5, as well as vision models like ResNet and vision transformers, which are frequently adapted to downstream tasks. In practice, finetunability is central to transfer learning and the efficient deployment of task-specific models.