Optimize large language models efficiently with Prompt Tuning—reduce costs, save resources, and achieve task-specific adaptability effortlessly.
Prompt Tuning is a powerful and efficient technique for adapting large pre-trained models, such as Large Language Models (LLMs), to new tasks without altering the original model’s weights. It is a form of Parameter-Efficient Fine-Tuning (PEFT) that keeps the billions of parameters in the base model frozen and instead learns a small set of task-specific "soft prompts." These soft prompts are not human-readable text but are learnable embeddings prepended to the input, which guide the frozen model to produce the desired output for a specific downstream task. This approach dramatically reduces the computational cost and storage needed for task-specific adaptation, as documented in the original Google AI research paper.
The core idea is to train only a few thousand or million extra parameters (the soft prompt) per task, rather than retraining or fine-tuning the entire model, which could have billions of parameters. This makes it feasible to create many specialized "prompt modules" for a single pre-trained model, each tailored to a different task, without creating full model copies. This method also helps mitigate catastrophic forgetting, where a model forgets previously learned information when trained on a new task.
Prompt Tuning enables the customization of powerful foundation models for a wide range of specialized applications.
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