Reducing Size of a Machine-Trained Model to Facilitate Storage and Transfer

A method for executing a machine-trained model in a local system, comprising:

• requesting a portion of model weights of the machine-trained model from a source system;
• receiving, in response to the requesting, a portion of model-variance information from the source system over a communication path;
• storing the portion of model-variance information;
• and
• executing a model part of the machine-trained model by using the portion of model-variance information in conjunction with a portion of base model weights associated with the model part of the machine-trained model, the portion of model-variance information having a smaller size than the portion of base model weights,
• the portion of base model weights being defined in a training operation to produce prescribed behavior of the machine-trained model, and the portion of model-variance information being defined in the training operation to produce a variation of the prescribed behavior.

A local system for executing a machine-trained model, comprising:

• a data store for storing computer-readable instructions;
• a processing system for executing the computer-readable instructions in the data store, to perform operations including:
• successively receiving portions of model-variance information from a source system over a communication path, and successively executing model parts of the machine-trained model associated with the portions of model-variance information to provide an output result,
• the source system storing a data structure that represents the machine-trained model,
• the data structure having a plurality of nodes associated with a plurality of respective portions of model-part information that are used to implement the machine-trained model, the nodes including a root node and a plurality of leaf nodes,
• the data structure having a main root-to-leaf (RTL) path through the data structure that includes a set of main-path nodes, the set of main-path nodes starting with the root node and ending with a particular leaf node, the main-path nodes being associated with respective portions of base model weights,
• the data structure having a plurality of non-main RTL paths between the root node and respective leaf nodes other than the main RTL path, the non-main RTL paths including non-main-path nodes that are associated with respective portions of model-variance information,
• the portions of base model weights being defined in a training operation to produce prescribed behavior of the machine-trained model, and the portions of model-variance information being defined in the training operation to produce variations of the prescribed behavior with less information compared to associated portions of base model weights, and
• the portions of model-variance information that are successively retrieved from the source system being associated with one of a plurality of paths represented by the data structure.