Definition
Fine-tuning involves taking a model that has already been trained on a large, general dataset and continuing its training on a smaller, task-specific dataset. This technique leverages the general features learned during pre-training while adjusting the model weights to better suit the nuances of the new domain. It is computationally cheaper than training from scratch and often yields superior performance when target data is scarce.
Summary
The process of further training a pre-trained model on a specific dataset to adapt it to a particular downstream task.
Key Concepts
- transfer_learning
- weight_update
- task_specific
- pre-trained_model
Use Cases
- Adapting LLMs for legal document review
- Customizing vision models for industrial defect detection
- Specializing speech recognition for specific accents