Fine-tuning is a machine learning technique where a pre-trained model is further trained (or fine-tuned) on a new dataset, usually smaller and domain-specific, to adapt it to a particular task. In this process, the pre-trained model retains the knowledge it has learned during its initial training and applies that to the new task, often with fewer resources and training time compared to training a model from scratch.
Fine-tuning is popular in NLP, computer vision, and other AI fields, especially when using large-scale models like BERT, GPT, T5, or ResNet, which are pre-trained on general datasets.
- Load Pre-trained Model: Start with a model pre-trained on a large, diverse dataset.
- Adapt Architecture: Adjust the model's layers or output to match the specific task (e.g., for classification or generation).
- Train on New Dataset: Train the model on a new, smaller dataset specific to your task, often using a smaller learning rate to avoid overfitting or disrupting the pre-trained weights.
-
Overfitting: When fine-tuning on a small dataset, there’s a risk of the model overfitting and losing its generalization capabilities.
- Solution: Use techniques like data augmentation, early stopping, and regularization. You can also freeze some pre-trained layers and only fine-tune the last few layers to prevent overfitting.
-
Catastrophic Forgetting: The model may "forget" the general knowledge it learned during pre-training when fine-tuned on a small, task-specific dataset.
- Solution: Use a lower learning rate or freeze parts of the model (e.g., lower layers) to preserve the pre-trained knowledge.
-
Limited Training Data: Fine-tuning often involves working with smaller datasets, which may not be sufficient to adapt the model effectively.
- Solution: Use data augmentation, transfer learning (by leveraging pre-trained models), and regularization techniques. Additionally, combining multiple small datasets can help.
-
Domain Mismatch: If there is a large difference between the domain of the pre-trained model and the target task (e.g., fine-tuning a model trained on English for use in a different language), performance might degrade.
- Solution: Gradual unfreezing, where you gradually unfreeze the model’s layers and fine-tune deeper layers slowly to adapt to the new domain, can help.
-
Hyperparameter Tuning: Finding the right hyperparameters (e.g., learning rate, batch size, weight decay) can be challenging during fine-tuning.
- Solution: Use grid search, random search, or more sophisticated approaches like Bayesian optimization to find the best hyperparameters. Start with lower learning rates since pre-trained models are sensitive to large updates.
-
Computational Resources: Fine-tuning large models, especially transformer-based models, can require significant computational resources, especially in terms of memory and processing power.
- Solution: Use techniques like
Low-Rank Adaptation (LoRA)or other methods ofParameter-Efficient Fine-Tuning (PEFT), which reduces memory usage, or opt for 4-bit or 8-bit quantization to reduce model size.
- Solution: Use techniques like
-
Evaluation and Validation: Properly evaluating a fine-tuned model on new data can be difficult if the dataset is unbalanced or there are no standard metrics for the task.
- Solution: Use cross-validation, domain-specific evaluation metrics (e.g., BLEU, ROUGE for text, F1 for classification), and create robust validation sets.
-
Bias in Pre-trained Models: The pre-trained models might carry biases from the data they were initially trained on, which can impact performance on new tasks.
- Solution: Bias mitigation techniques, like re-sampling the training data or fine-tuning on more representative data, can help reduce the impact of unwanted biases.
LLMs from Scratch
Topics in NLP and LLMs
