Methods for tuning foundation models

Learn more about different tuning methods.

Foundation models can be tuned in the following ways:

• Prompt tuning: Adjusts the content of the prompt that is passed to the model to guide the model to generate output that matches a pattern you specify. The underlying foundation model and its parameter weights are not changed. Only the prompt input is altered.

  Although the result of prompt tuning is a new tuned model asset, the prompt-tuned model merely adds a layer of function that runs before the input is processed by the underlying foundation model. When you prompt-tune a model, the underlying foundation model is not changed, which means that it can be used to address different business needs without being retrained each time. As a result, you reduce computational needs and inference costs. See Prompt tuning.

  Deprecated Prompt tuning is deprecated and will be removed in the future.

• Full fine tuning: Using the base model’s previous knowledge as a starting point, full fine tuning tailors the model by tuning it with a smaller, task-specific dataset. The full fine-tuning method changes the parameter weights for a model whose weights were set through prior training to customize the model for a task.

  Restriction: You currently cannot use full fine tuning with foundation models in watsonx.ai.

• Low-rank adaptation (LoRA) fine tuning: Adapts a foundation model for a task by changing the weights of a representative subset of the model parameters, called low-rank adapters, instead of the base model weights during tuning. At inference time, weights from the tuned adapters are added to the weights from the base foundation model to generate output that is tuned for a task. See Low-rank adaptation (LoRA) fine tuning.

Parent topic: Tuning foundation models