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Fine-tuning is the process of adapting a pretrained model to a specific task or domain. It lowers training cost and data needs while enabling higher task-specific performance and faster iteration. Proper fine-tuning requires careful data curation, regularization and evaluation to avoid overfitting and degraded generalization.
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Fine-tuning is the post-training adaptation of a pretrained model to a specific task or domain. It lets existing models become more specialized, often with less data and compute than training from scratch.
Fine-tuning became important as a practical form of transfer learning: a model pretrained on large datasets is adapted with additional examples for a narrower downstream task or domain. Rather than starting over, the existing weights are reused; depending on the method, all or only selected layers are updated, often with adapters added. In NLP, vision, and multimodal workflows, it is a way to specialize broad prior knowledge efficiently.
Think of a pretrained model as a generalist that already knows the basics. Fine-tuning is the specialization loop: examples from the target task go in, some parts of the network stay fixed, and other parts are adjusted. The smaller the intervention, the cheaper and more controlled the process; the stronger the adaptation, the greater the chance of better task fit and the higher the risk of losing general capabilities.
A model that has already learned broad patterns from general data and serves as the starting point.
The concrete task or domain the model is later specialized for.
Certain model parts remain unchanged so existing knowledge is preserved and training cost drops.
Small add-on modules or parameter-efficient methods move the model toward the target task without changing all weights.
Separate checks show whether the adaptation really helps and is not just fitting the training data.
Fine-tuning is useful when a general model needs stronger domain performance and enough target examples are available. The options range from full fine-tuning to frozen layers and adapter-based methods; more adaptation can improve results, but it also raises compute cost and the risk of overfitting or reduced robustness. Clean data and separate evaluation are essential for judging the trade-off.
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