Fine-tuning
A post-training technique to specialize a trained model on specific data for a particular task.
Detailed Definition
Fine-tuning is a post-training technique where you take a trained model and do additional training on specific data that's tailored to what you want the model to be especially good at. For example, you would fine-tune a model on your company's customer service conversations to make it respond in your brand's specific style, or on medical literature to make it better at answering healthcare questions. This additional training tweaks the model's internal weights to specialize its responses for your specific use case, while preserving the general knowledge it learned during pre-training.
Learning MethodsMore in this Category
Few-Shot Learning
The ability of AI models to learn new tasks with only a small number of training examples.
Post-training
Additional steps taken after a model's initial training is complete to make it more useful.
RLHF (reinforcement learning from human feedback)
A post-training technique to align AI models with human preferences using feedback.
Training/Pre-training
The process by which an AI model learns by analyzing massive amounts of data.
Supervised learning
A type of machine learning where a model is trained on labeled data with correct answers provided.
Unsupervised learning
A type of machine learning where a model is given unlabeled data to find patterns on its own.