Composable low-rank adaptation models for defining large-language model text style

A non-transitory computer readable storage medium storing executable instructions, execution of which by a processor causing the processor to:

• maintain a plurality of low-rank adaptation (LoRA) models that each include a set of weights configured to modify parameters of a large-language model (LLM) to cause the LLM to generate text having a corresponding property;
• present a set of manipulable user-interface controls to allow configuration of properties of text generated using the LLM and wherein output of the LLM is to be modified based on one of more of the LoRA models of the plurality of LoRA models selected based on a state of the user-interface controls as manipulated;
• and
• provide a preview of LLM output corresponding to a current state of the user-interface controls during presentation and manipulation thereof, wherein providing the preview includes iteratively providing a prompt to the LLM and outputting the output of the LLM responsive to the prompt for each iteration, wherein, for each iteration, the output of the LLM is modified based on one or more of the LoRA models of the plurality of LORA models selected based on the current state of the user-interface controls as manipulated.

A system comprising:

• at least one hardware processor;
• and
• at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
• maintain a plurality of low-rank adaptation (LoRA) models that each include a set of weights configured to modify parameters of a large-language model (LLM) to cause the LLM to generate text having a corresponding property;
• present a set of manipulable user-interface controls to allow configuration of properties of text generated using the LLM and wherein output of the LLM is to be modified based on one of more of the LoRA models of the plurality of LORA models selected based on a state of the user-interface controls as manipulated;
• and
• provide a preview of LLM output corresponding to a current state of the user-interface controls during presentation and manipulation thereof, wherein providing the preview includes iteratively providing a prompt to the LLM and outputting the output of the LLM responsive to the prompt for each iteration, wherein, for each iteration, the output of the LLM is modified based on one or more of the LoRA models of the plurality of LoRA models selected based on the current state of the user-interface controls as manipulated.