15 results about "Model expansion" patented technology

Disclosed is a device and method for processing tasks through model-based offline learning. The device includes: a dataset input unit configured to receive an offline dataset for offline reinforcement learning; an initialization unit configured to initialize a world model and a model generation dataset for predicting a state transition and a reward without interacting with a real environment; a model rollout unit configured to expand the offline data of the offline dataset based on the world model to generate an imagined trajectory and generate imaginary data of the model generation dataset; and a learning update unit configured to perform critic update and actor update based on the offline data and the imaginary data.

The application discloses a self-evolution multi-agent event semantic understanding system and method based on a dynamic scalable model, which comprises a multi-agent cooperation framework modeling module, a demand understanding and task planning module, a capability verification module, a model dynamic expansion module, a task execution module and a completion module connected in sequence, wherein the multi-agent cooperation framework modeling module comprises a central control type multi-agent, a task execution type multi-agent and a model expansion type multi-agent. The method focuses on a news event semantic understanding scene, constructs a dynamic scalable system architecture through a multi-agent cooperation mechanism, introduces a capability verification link to optimize a task execution process, and realizes on-demand iteration of model capability by using an active learning framework, so that adaptive evolution of agent capability is realized, thereby solving the problems of model rigidity and poor adaptability of a traditional event semantic understanding system in a complex news event scene, and significantly improving the dynamic adaptability and accuracy of news event semantic understanding.

Embodiments of the present application provide a pre-training model expansion method and device, electronic equipment, a storage medium and a computer program product, wherein the pre-training model expansion method comprises: sequentially subjecting a first model to an expansion operation corresponding to each of a plurality of set dimensions to obtain a second model; wherein the first model represents a pre-training model after the Nth expansion, the second model represents a pre-training model after the N+1th expansion, and N is an integer greater than or equal to 0.

The invention discloses a self-evolution multi-agent event semantic understanding system and method based on a dynamic extensible model, and the system comprises a multi-agent cooperation framework construction module, a demand understanding and task planning module, a capability verification module, a model dynamic extension module, a task execution module, and a completion module which are connected in sequence. The multi-agent collaboration framework construction module comprises a central control type multi-agent, a task execution type multi-agent and a model expansion type multi-agent, the method focuses on a news event semantic understanding scene, a dynamic extensible system architecture is constructed through a multi-agent collaboration mechanism, a capability verification link is introduced to optimize a task execution process, and the task execution efficiency is improved. And on-demand iteration of the model capability is realized by using an active learning framework, and adaptive evolution of the agent capability is realized, so that the problems of model stiffness, poor adaptability and the like of a traditional event semantic understanding system in a complex news event scene are solved, and the dynamic adaptability and accuracy of news event semantic understanding are remarkably improved.

This disclosure relates to a model expansion method, apparatus, device, storage medium, and computer program product. The model expansion method includes: during model training using first training data, monitoring the data compression capacity of each of k feedforward modules; in response to the detection of a first feedforward module among the k feedforward modules, adding a second feedforward module to the decoding module containing the first feedforward module; based on the expanded feedforward module, continuing to train the model using second training data, and monitoring the data compression capacity of each of the k feedforward modules; repeating the above process until the number of feedforward modules in the decoding module is expanded to a target number. This disclosure improves the utilization rate of training data by dynamically setting the model parameters of each added feedforward module, saves training resources, improves the continuity of the model's pre-training process, and thus improves the stability of the model.

The invention provides a novel prompt engineering method based on twinborn prompt, and relates to the technical field of prompt engineering. According to the method, a high-quality data set is constructed through real query and collection, multi-round screening and professional labeling, and then model expansion and lyric filtering, so that the data accuracy is improved; then, LLMs are adopted to construct a hierarchical reasoning model, and hierarchical reasoning comprises the steps that in the first stage, the heavy query is guided through a professional view angle, and LLMs are helped to focus on core problems in the dual-carbon field; in the second stage, specific example optimization response is supplemented, accuracy and understandability are both considered, domain knowledge is deeply activated, and response quality is remarkably improved; finally, the final response is evaluated through multiple indexes, the incredibility degree of the LLMs response is quantified through word amazing degree distribution, the illusion risk recognition accuracy is improved, and the information reliability is improved.

This invention provides a multi-teacher joint distillation and plug-in model expansion method, belonging to the field of data processing technology. Specifically, it includes: constructing a decoupled student model; performing joint distillation training on the decoupled student model using multiple teacher models, applying feature decoupling constraints to the common features output by the shared backbone network and the task-specific features output by the task-specific adapter during training; in the inference phase, dynamically calling the corresponding task-specific adapter based on the task identifier using the trained decoupled student model, jointly processing the input data with the shared backbone network to obtain the inference result corresponding to the task identifier; and performing plug-in incremental expansion, whereby, in response to the access of a new teacher model to introduce a new task, the shared backbone network remains unchanged, and a new task-specific adapter corresponding to the new teacher model is added and trained. This invention improves flexibility and adaptability.

The application discloses an information extraction algorithm based on large model extended label enhancement. The application comprises the following steps: taking relation extraction as an example, first, using the text in the training data set and the corresponding head-tail entity pair to expand the predefined relation type; second, using the expanded relation to extract the head-tail entity pair from the text in the verification data set, if the extracted head-tail entity pair is consistent with the real entity pair, the corresponding expanded relation is retained; finally, integrating the retained expanded relation into the predefined relation set, using the large model to perform the relation extraction task on the test set, thereby enhancing the performance of the model on the relation extraction task. The application is suitable for information extraction tasks in the field of natural language processing using large models, by designing an information extraction algorithm based on large model extended label enhancement, it is proved that the large model has the phenomenon of "false association" when performing information extraction tasks, and further using the extended type label to improve the performance of the large model.

The application discloses a learning index model facing update distribution, comprising the following steps: (1) using an overhead model to construct a key into an RMI structure; (2) uniformly dividing the range of data by using the cumulative distribution function of data at each internal node; (3) constructing a monotonous gapped unary linear regression model at each leaf node; (4) learning the update distribution of data during the construction of the index; (5) point query and insertion; and (6) model expansion and splitting. The application uses a machine learning model to replace a traditional B-tree-based database index structure, simultaneously learns the update distribution of data, utilizes the characteristics of the update distribution of data and the advantages of the machine learning model, and greatly reduces the storage overhead of the index structure and the overhead during data insertion.

The application discloses a model instance adjustment method and an electronic device, relates to the technical field of artificial intelligence, and comprises the following steps: performing aggregate calculation on original performance index data by means of instantaneous value index items and incremental value index item classification calculation to obtain service performance index data of a model; setting a model expansion and contraction limit threshold value through an open-source expansion and contraction component; in response to the service performance index data reaching the model expansion and contraction limit threshold value, adjusting the service instance of the model; in response to a new valid request of the model service and the service performance index data value being greater than a first threshold value, expanding the model container service instance copy; and in response to the service continuously having no valid request within a preset time or the service performance index data value being less than a second threshold value, reducing the number of model container service instance copies to a preset minimum value to release display memory and computing resources. The application can optimize the performance and resource use of a large model inference service and make it easier for users to analyze and maintain the service.

This disclosure provide a method and a related apparatus. The method includes: determining, from a learnware library, a first reuse model matching a target data set, where the learnware library includes a plurality of learnwares, each of the learnwares includes one reuse model and one model specification, the first reuse model is a reuse model included in one learnware in the learnware library, the target data set includes a plurality of pieces of target data, and the plurality of pieces of target data are tabular data; performing shrinkage adjustment on the first reuse model based on the target data set, to obtain a second reuse model; performing model expansion based on the target data set and the second reuse model, to obtain a third reuse model; and integrating the second reuse model and the third reuse model, to obtain a target reuse model corresponding to the target data set.

This application discloses a model access method, apparatus, storage medium, and computer device, relating to the computer field. The method includes: receiving an access request through a RESTful interface; wherein the access request carries a user identifier, model name, business capability name, and business parameters; determining a target model in a model set based on the model name; wherein multiple models in the model set share the RESTful interface; using reflection to invoke a class object indicated by the business capability name in the target model; and returning the invocation result obtained by the class object in response to the business parameters to the user indicated by the user identifier. This application can save time in model development and expansion, avoid adding a large number of interfaces during model expansion, improve development efficiency, and ensure code reuse.

The embodiment of the application provides a model expansion method of a face access control device, a computing device and a storage medium. In the embodiment of the application, a file of a to-be-downloaded model is generated by a server device, and a description feature for describing input data of the to-be-downloaded model is stored. The to-be-downloaded model refers to a model for updating the face access control device. The file and the description feature are acquired by the face access control device from the server device. According to the file, the corresponding model is loaded on the face access control device to update the model. Therefore, the face access control device can dynamically update the model through the file of the model of the server device, so that different application scenarios can be supported, and the capability of the face access control device, such as accuracy, is improved, thereby improving the experience of the user.

This application discloses a method for extending a model, electronic equipment, and a storage medium, which belong to the art of computers. The method includes the steps of: setting an index field in a TR181 extended model file for target traffic based on the type of target traffic; setting at least one description field in the TR181 extended model file for target traffic based on the description parameters of the target traffic, wherein one of the description fields is for expressing the characteristics of the description parameters; and setting the length of each of the description fields based on the globalization adaptability and global standards of the target traffic, thereby realizing the extension of the TR181 extended model file.