Advanced · Neural Networks

Model Grafting

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. The technique of attaching a specialized smaller model or 'head' to a larger foundational model to adapt it for a specific task.

Technical Definition

The technique of attaching a specialized smaller model or 'head' to a larger foundational model to adapt it for a specific task.

How it works

Model grafting allows for efficient customization of large pre-trained models. Instead of fine-tuning the entire gargantuan model, only a small, task-specific network is added and trained on top of the frozen foundational model. This method is computationally less expensive and faster, leveraging the extensive knowledge of the base model while providing task-specific specialization.

Related Concepts

• Fine-Tuning — Adapting a pre-trained model to a specific task by continuing training on a smaller, task-specific dataset.
• Transfer Learning — Leveraging knowledge from a model trained on one task to improve performance on a different but related task.
• Parameter-Efficient Fine-Tuning (PEFT) — A family of techniques that adapt large pre-trained models to new tasks by updating only a small subset of their parameters, saving computation.