Post-Training, Fine-Tuning, and Adaptation
Explore fine-tuning, why it’s essential, and the various techniques used.
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After broad pretraining, foundation models require refinement to specialize, much like a doctor studying surgery after completing general medicine. Fine-tuning adapts general knowledge to specific tasks, turning generalists into specialists. In this lesson, we’ll explore transfer learning, common fine-tuning methods, the role of reinforcement learning from human feedback (RLHF) with ChatGPT as an example, and briefly cover model distillation for efficient deployment.
What is fine-tuning?
Fine-tuning is the process of taking a pretrained model, already equipped with broad general knowledge, and adapting it to a specific task by training it further on a smaller, specialized dataset. It is akin to transitioning from general medical training to a specialized focus in neurology. This works through transfer learning, where knowledge gained during pretraining is reused and refined, enabling the model to quickly excel in new but related tasks.
But why isn’t a pretrained model the final product?
Pretraining gives models a broad grasp of patterns and structures, but not the task-specific expertise needed in real-world use. Fine-tuning adapts this general knowledge to the demands of a particular task, creating models that are both effective and practical.
Efficiency: Reuses pretrained knowledge, reducing the data, time, and compute needed.
Specialization: Boosts accuracy on tasks like summarization, translation, or medical diagnosis.
Practicality: Modern fine-tuning methods require only small parameter updates, making deployment more affordable.
Educative byte: There are so many different applications of fine-tuning out there; for example, how do you think Tesla’s Autopilot adapts the general vision models to the specifics of regional roads and traffic conditions?
Before diving into techniques, let’s quickly understand transfer learning, the foundation behind fine-tuning. Transfer learning involves using knowledge gained from solving one problem (the source) to improve learning on a related problem (the target). It speeds up training, reduces the need for massive datasets, and boosts performance—particularly beneficial when task-specific data is limited.
For example, OpenAI’s GPT-3 was first pretrained on massive general text datasets. Then, by using transfer learning, it was quickly adapted (fine-tuned) to perform tasks like answering questions or writing summaries.
What is full fine-tuning?
Historically, the most straightforward way to adapt a pretrained model has been full ...