US 2026/0228540 A1 · MACHINE LEARNING MODELS WITH PARALLEL ADAPTERS

Abstract

Systems and techniques are described herein for configuring machine learning models with parallel low rank adapters. For example, an apparatus comprising one or more processors and configured to: obtain a plurality of embeddings associated with input content; determine a plurality of probability values corresponding to a plurality of clusters, wherein each probability value corresponds to a probability a respective embedding belongs to a particular cluster; generate a plurality of output values based on input tokens associated with the input content, the plurality of output values including a first output value associated with the first model, a second output value associated with a first adapter of the first model, and a third output value associated with a second adapter of the first model; combine the plurality of output values based on the plurality of probability values into a combined value; and output content generated based on the combined value.

Claims20

Dependent claims

An apparatus for generating content, comprising:

at least one memory;
and
at least one processor coupled to the at least one memory and configured to:
obtain a plurality of embeddings associated with input content;
determine, for the plurality of embeddings, a plurality of probability values corresponding to a plurality of clusters, wherein each probability value corresponds to a probability a respective embedding belongs to a particular cluster of the plurality of clusters;
generate, using a first model of a machine learning model, a plurality of output values based on input tokens associated with the input content, the plurality of output values including a first output value associated with the first model, a second output value associated with a first adapter of the first model, and a third output value associated with a second adapter of the first model;
combine the plurality of output values based on the plurality of probability values into a combined value;
and
output content generated based on the combined value.

6 more elements

A method of generating content, comprising:

obtaining a plurality of embeddings associated with input content;
determining, for the plurality of embeddings, a plurality of probability values corresponding to a plurality of clusters, wherein each probability value corresponds to a probability a respective embedding belongs to a particular cluster of the plurality of clusters;
generating, by a first model of a machine learning model, a plurality of output values based on input tokens associated with the input content, the plurality of output values including a first output value associated with the first model, a second output value associated with a first adapter of the first model, and a third output value associated with a second adapter of the first model;
combining the plurality of output values based on the plurality of probability values into a combined value;
and
outputting, by the machine learning model, content generated based on the combined value.

3 more elements

Dependencies are inferred from the claim text.

Machine learning models with parallel adapters

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Machine learning models with parallel adapters

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Abstract

Claims20