Abstract: Disclosed herein are methods and systems for generating metadata from content using one or more machine learning models. In an embodiment, a method may include receiving the content through a graphical user interface associated with the large language model, generating a first file by tokenizing the content into an input format for the large language model and merging the tokenized content with a content instruction, inputting the first file into the large language model, generating, using the large language model, metadata from at least the first file, the metadata reflecting a context associated with the content, generating a second file, the second file comprising the metadata, and displaying the generated metadata on the graphical user interface.

Abstract: While AI models, like large language models, are powerful tools with multiple applications, they can be complex to use and can require a lot of resources to operate. The disclosed systems and methods provide tools to generate customized models (or AI agents) that are configured with features like tailored knowledge, capabilities, and instructions that make them faster, more efficient, and use less computational resources. AI agents may offer several technical advantages of improved efficiency, resource use, and connectivity. This disclosure describes systems and methods to configure, evaluate, generate, and deploy the custom models that can more efficiently run specific tasks. Disclosed systems and methods are configured to, for example, receive a query to generate a custom model, generate the AI agent custom model with the information in the query, and then resolve user queries more efficiently using the custom model.

Abstract: While AI models, like large language models, are powerful tools with multiple applications, they can be complex to use and can require a lot of resources to operate. The disclosed systems and methods provide tools to generate customized models (or AI agents) that are configured with features like tailored knowledge, capabilities, and instructions that make them faster, more efficient, and use less computational resources. AI agents may offer several technical advantages of improved efficiency, resource use, and connectivity. This disclosure describes systems and methods to configure, evaluate, generate, and deploy the custom models that can more efficiently run specific tasks. Disclosed systems and methods are configured to, for example, receive a query to generate a custom model, generate the AI agent custom model with the information in the query, and then resolve user queries more efficiently using the custom model.

Abstract: The disclosed embodiments may include a method of interacting with a multimodal machine learning model; the method may include providing a graphical user interface associated with a multimodal machine learning model. The method may further include displaying an image to a user in the graphical user interface. The method may also include receiving a textual prompt from the user and then generating input data using the image and the textual prompt. The method may further include generating an output at least in part by applying the input data to the multimodal machine learning model, the multimodal machine learning model configured using prompt engineering to identify a location in the image conditioned on the image and the textual prompt, wherein the output comprises a first location indication. The method may also include displaying, in the graphical user interface, an emphasis indicator at the indicated first location in the image.

Abstract: The disclosed embodiments may include a method of interacting with a multimodal machine learning model; the method may include providing a graphical user interface associated with a multimodal machine learning model. The method may further include displaying an image to a user in the graphical user interface. The method may also include receiving a textual prompt from the user and then generating input data using the image and the textual prompt. The method may further include generating an output at least in part by applying the input data to the multimodal machine learning model, the multimodal machine learning model configured using prompt engineering to identify a location in the image conditioned on the image and the textual prompt, wherein the output comprises a first location indication. The method may also include displaying, in the graphical user interface, an emphasis indicator at the indicated first location in the image.