Device Evaluation Based on Hybrid Language Models

A method, comprising:

• training, by a user computing device, the user computing device having at least one processor, and memory, a micro language model using historical transaction data captured by the user computing device, wherein training the micro language model causes the micro language model to identify correlations, sequences, or patterns in real-time transaction data to output recommended payment devices based on transaction details of current transactions;
• detecting, by the at least one processor, initiation of a transaction between a user and a merchant associated with the transaction;
• receiving, by the at least one processor and based on the detected initiation of the transaction, transaction details of the transaction including the user associated with the transaction, a first payment device being used to process the transaction, a type of transaction, the merchant associated with the transaction and a type of transaction;
• in real-time, receiving, by the at least one processor and from a computing platform and via a unified language model feed layer that formats data output by the large language model to ensure seamless processing by the micro language model, user specific payment device information generated by a large language model executing on the computing platform;
• in real-time and during the transaction, executing, by the at least one processor, the micro language model, wherein executing the micro language model includes inputting, to the micro language model, the user specific payment device information and the transaction details of the initiated transaction to output a recommended payment device;
• generating, by the at least one processor, a notification including the recommended payment device;
• and
• displaying, by the at least one processor, the notification including the recommended payment device.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing device comprising at least one processor, memory, and a communication interface, cause the computing device to:

• train a micro language model using historical transaction data captured by the user computing device, wherein training the micro language model causes the micro language model to identify correlations, sequences, or patterns in real-time transaction data to output recommended payment devices based on transaction details of current transactions;
• detect initiation of a transaction between a user and a merchant associated with the transaction;
• receive, based on the detected initiation of the transaction, transaction details of the transaction including the user associated with the transaction, a first payment device being used to process the transaction, a type of transaction, the merchant associated with the transaction and a type of transaction;
• in real-time, receive, from a computing platform and via a unified language model feed layer that formats data output by the large language model to ensure seamless processing by the micro language model, user specific payment device information generated by a large language model executing on the computing platform;
• in real-time and during the transaction, execute the micro language model, wherein executing the micro language model includes inputting, to the micro language model, the user specific payment device information and the transaction details of the initiated transaction to output a recommended payment device;
• generate a notification including the recommended payment device;
• and
• display the notification including the recommended payment device.