Fine tuning
Fine-tuning is the process of adapting a pre-trained model to a new, specific task using a smaller dataset. Rather than training a model from scratch, fine-tuning leverages the general patterns and knowledge that the model has already learned.
Why it matters
In the deep learning era, pre-trained models are the norm. Training models like GPT or ResNet from scratch requires massive data and compute resources. Fine-tuning makes it feasible for companies to customize state-of-the-art models for domain-specific applications—healthcare, law, finance, or niche industrial settings.
Practical examples
- Text classification: adapting BERT for customer support ticket categorization.
- Image analysis: fine-tuning CNNs for detecting rare medical conditions.
- Speech recognition: adapting generic speech-to-text models to a specific accent or dialect.
Pros and challenges
- Pros: faster training, lower cost, high performance on specialized tasks.
- Challenges: requires careful dataset curation; risk of overfitting when data is scarce.
- Recent trend: parameter-efficient fine-tuning (e.g., LoRA, adapters) reduces memory usage by only adjusting a subset of parameters.
Fine-tuning can be seen as a shortcut to specialization. Instead of training a large model from scratch—which can cost millions of dollars in compute—a pre-trained model provides a strong foundation. By adjusting it with task-specific data, researchers can achieve state-of-the-art performance at a fraction of the cost.
There are several strategies for fine-tuning. Full fine-tuning updates all parameters of the model, while parameter-efficient methods (such as LoRA, adapters, or prompt-tuning) only adjust a small fraction, reducing memory and training requirements. This distinction has become increasingly important with the rise of very large language models, where full fine-tuning may be impractical.
Fine-tuning also raises questions of generalisation and safety. If the target dataset is too narrow, the model risks overfitting or inheriting biases specific to that domain. That is why techniques such as data augmentation, regularisation, and careful evaluation are critical to ensure that the adapted model remains robust.
Further reading
- Raffel et al. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.
