Methods for tuning foundation models

Learn more about different tuning methods in watsonx.ai and how to choose the method that's right for your solution.

Foundation models can be tuned in the following ways:

• 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.

• Quantized low-rank adaptation (QLoRA) fine tuning: QLoRA is a variant of LoRA that incorporates quantization to further reduce the memory footprint and computational resources that are required during tuning. See Quantized low-rank adaptation (QLoRA) fine tuning.