公开(公告)号：US20250045517A1

公开(公告)日：2025-02-06

申请号：US18749004

申请日：2024-06-20

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： En SHI

IPC: G06F40/186

Abstract: A copywriting generation method, an electronic device and a storage medium are provided and relate to a field of artificial intelligence technology, in particular to fields of deep learning and natural language processing technologies, and may be applied to scenarios of large language models and generative dialogues. The copywriting generation method includes: updating, in response to an input copywriting requirement information being received, a copywriting prompt information in the copywriting requirement information according to a copywriting generation operation related to the copywriting requirement information, so as to obtain a first target copywriting requirement information, where the first target copywriting requirement information includes a target copywriting prompt information related to a semantic attribute of the copywriting requirement information; and processing the first target copywriting requirement information based on a pre-trained deep learning model, so as to generate a first feedback copywriting corresponding to the copywriting requirement information.

公开(公告)号：US20220276899A1

公开(公告)日：2022-09-01

申请号：US17744396

申请日：2022-05-13

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Binbin XU ,  Liang TANG ,  Ying ZHAO ,  Shupeng LI ,  En SHI ,  Zhengyu QIAN ,  Yongkang XIE

IPC: G06F9/50 ,  G06T1/20

Abstract: A resource scheduling method and apparatus, a device, and a storage medium are provided, and relates to the field of computer technology, and in particular to the field of deep learning technology. The method includes: acquiring a graphics processing unit (GPU) topology relationship of a cluster according to GPU connection information of each of computing nodes in the cluster; and in a case where a task request, for applying for a GPU resource, for a target task is received, determining a target computing node of the target task and a target GPU in the target computing node according to the task request and the GPU topology relationship, to complete GPU resource scheduling of the target task. The present disclosure can optimize the resource scheduling.

公开(公告)号：US20230196245A1

公开(公告)日：2023-06-22

申请号：US18169727

申请日：2023-02-15

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Mengyue LIU ,  Haibin ZHANG ,  Penghao ZHAO ,  Shupeng LI ,  En SHI

IPC: G06Q10/0635 ,  G06N7/01

CPC classification number: G06Q10/0635 ,  G06N7/01

Abstract: A method and apparatus for predicting a risk are provided. The method may include: determining an inherent risk probability of a to-be-tested object; building a relationship graph between the to-be-tested object and different associated objects; determining a primary conduction probability between any two directly associated objects in the relationship graph; determining, based on the primary conduction probability between any two directly associated objects in the relationship graph and the relationship graph, a multi-level conduction probability of the to-be-tested object; and determining a target risk probability of the to-be-tested object, based on the inherent risk probability and the multi-level conduction probability of the to-be-tested object.

公开(公告)号：US20240419991A1

公开(公告)日：2024-12-19

申请号：US18747725

申请日：2024-06-19

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Zhenfang CHU ,  Zhengyu QIAN ,  En SHI ,  Mingren HU ,  Zhengxiong YUAN ,  Jinqi LI ,  Yue HUANG ,  Yang LUO ,  Guobin WANG ,  Yang QIAN ,  Kuan WANG

IPC: G06N5/04

Abstract: A method is provided that includes: creating a plurality of first model instances of a first service model to be deployed; allocating an inference service for each of a plurality of first model instances from the plurality of inference services; calling, for each first model instance, a loading interface of the inference service allocated for the first model instance to mount a weight file; determining, in response to a user request for a target service model, a target model instance from a plurality of model instances of the target service model to respond to the user request; and calling a target inference service allocated for the target model instance to use computing resources configured for the target inference service to run, in the target model instance, a base model mounted with a target weight file, and obtain a request result of the user request.

公开(公告)号：US20240411552A1

公开(公告)日：2024-12-12

申请号：US18748345

申请日：2024-06-20

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Jing XIA ,  Jun LIU ,  Binbin XU ,  Kuan SHI ,  Shupeng LI ,  Zhengyu QIAN ,  En SHI

IPC: G06F8/71 ,  G06F11/36

Abstract: A computer-implemented method for recommending a large model interface configuration includes: obtaining a search space of a model interface configuration and a test data set, wherein the search space comprises at least one candidate model interface and a value range of a hyperparameter; and obtaining a plurality of model interface configuration sets based on the search space, wherein each model interface configuration set comprises a candidate model interface and a value of the hyperparameter; and obtaining a test result corresponding to each model interface configuration set, by using the test data set to test a large model called based on each model interface configuration set; and determining a target interface configuration based on the test results corresponding to the plurality of model interface configuration sets.

公开(公告)号：US20230376726A1

公开(公告)日：2023-11-23

申请号：US17980204

申请日：2022-11-03

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Zhengxiong YUAN ,  Zhenfang CHU ,  Jinqi LI ,  Mingren HU ,  Guobin WANG ,  Yang LUO ,  Yue HUANG ,  Zhengyu QIAN ,  En SHI

IPC: G06N3/04

CPC classification number: G06N3/04

Abstract: Provided are an inference service deployment method, a device and a storage medium, relating to the field of artificial intelligence technology, and in particular to the field of machine learning and inference service technology. The inference service deployment method includes: obtaining performance information of a runtime environment of a deployment end; selecting a target version of an inference service from a plurality of candidate versions of the inference service of a model according to the performance information of the runtime environment of the deployment end; and deploying the target version of the inference service to the deployment end.