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Basic data
| Standard ID | GB/T 45288.1-2025 (GB/T45288.1-2025) |
| Description (Translated English) | Artificial intelligence - Large-scale model - Part 1: General requirements |
| Sector / Industry | National Standard (Recommended) |
| Classification of Chinese Standard | L70 |
| Classification of International Standard | 35.240 |
| Word Count Estimation | 14,160 |
| Date of Issue | 2025-02-28 |
| Date of Implementation | 2025-02-28 |
| Issuing agency(ies) | State Administration for Market Regulation, China National Standardization Administration |
GB/T 45288.1-2025: Artificial intelligence - Large-scale model - Part 1: General requirements
---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
GB/T 45288.1-2025 English version. Artificial intelligence - Large-scale model - Part 1.General requirements
ICS 35.240
CCSL70
National Standard of the People's Republic of China
Artificial Intelligence Big Model
Part 1.General requirements
Artificialinteligence-Large-scalemodel-Part 1.Generalrequirements
Released on 2025-02-28
2025-02-28 Implementation
State Administration for Market Regulation
The National Standardization Administration issued
Table of Contents
Preface III
Introduction IV
1 Scope 1
2 Normative references 1
3 Terms and Definitions 1
4 Reference Architecture 2
5 General requirements 3
5.1 Resource Pool 3
5.2 Tools 4
5.3 Data Resources 6
5.4 Model 6
5.5 Industry Application 7
5.6 Service Platform/Components 7
References 8
Foreword
This document is in accordance with the provisions of GB/T 1.1-2020 "Guidelines for standardization work Part 1.Structure and drafting rules for standardization documents"
Drafting.
This document is part 1 of GB/T 45288 "Artificial Intelligence Big Model". GB/T 45288 has been published in the following parts.
--- Part 1.General requirements;
--- Part 2.Evaluation indicators and methods;
--- Part 3.Service capability maturity assessment.
Please note that some of the contents of this document may involve patents. The issuing organization of the document does not assume the responsibility for identifying patents.
This document was proposed and coordinated by the National Information Technology Standardization Technical Committee (SAC/TC28).
This document was drafted by. China Electronics Technology Standardization Institute, Shanghai Artificial Intelligence Innovation Center, Huawei Cloud Computing Technology Co., Ltd.,
Ant Technology Group Co., Ltd., Tsinghua University, Institute of Automation, Chinese Academy of Sciences, Beijing Zhongguancun Laboratory, Beijing Baidu Network Technology Co., Ltd.
Technology Co., Ltd., China Railway Construction Corporation Limited, Beijing Qihoo Technology Co., Ltd., China Southern Power Grid Co., Ltd., China Mobile Communications
Xin Co., Ltd. Research Institute, China National Energy Investment Group Co., Ltd. Information Technology Branch, Hangzhou Lianhui Technology Co., Ltd.,
He Yinan, Zhao Chunhao, Yang Muyun, Yu Wenxin, Yang Chao, He Gang, Hao Wenjian, Xue Yunzhi, Liu Aishan, Wu Xihong, Liu Shang, Yu Tian, Liu Ying, Chen Xi,
Zheng Ruolin, Shen Zhiyue, Nie Jiandi, Wang Xianqing, Wang Jinqiao, Hu Quanyi, Zhu Guibo, Han Honggui, Pan Enrong, Wu Shanshan, Kong Hao, Yu Lei, Zheng Zhe,
Liu Zitao, Zhu Jiang, Chen Hongzhi, Fan Baoyu, Liu Wei, Cui Mingfei, Gao Pengjun, Zhang Feng, Mei Jingqing, Zeng Dingheng, Song Yu, Zhao Lei, Gao Hui, Zhang Xu,
Zhong Kaitao, Li Bin, Liu Shu, Liang Jiaen, Wei Zizhong, Shu Minglei, Chen Minggang, Meng Lingzhong, Wang Zikai, Liu Changxin, Fan Cunhang, Sheng Ruogu, Sun Jin,
Kong Weisheng, Chen Liming, Zheng Hua, Zhao Xiaowei, Feng Junlan, Yang Yukuan, Sun Wenqing, Zhu Lin, Zeng Jie, Qian Ling, and Zhang Tao.
Introduction
Big models have become an important technical means for the development of artificial intelligence and play an important role in leading industrial transformation.
Relevant institutions have successively researched and developed more than 100 large-scale model products and evaluation lists, making it difficult for users to effectively evaluate the technical level of artificial intelligence products.
GB/T 45288 "Artificial Intelligence Big Model" aims to specify the technical requirements, evaluation indicators and service capabilities of general big models.
Force is proposed to consist of five parts.
--- Part 1.General requirements. The purpose is to establish a reference architecture for large models and specify general technical requirements.
--- Part 2.Evaluation indicators and methods. The purpose is to establish the evaluation indicators of large models and describe the evaluation methods.
--- Part 3.Service capability maturity assessment. The purpose is to provide the large model service capability maturity level and assessment method.
--- Part 4.Computer vision big model. The purpose is to define the concept and function of the computer vision big model and specify the technical requirements
and testing methods.
--- Part 5.Multimodal large models. The purpose is to define the concept and function of multimodal large models, specify technical requirements and tests
method.
Artificial Intelligence Big Model
Part 1.General requirements
1 Scope
This document establishes a reference architecture for large models and specifies common requirements for large models.
This document is applicable to large-scale model development, preparation, deployment and application.
2 Normative references
The contents of the following documents constitute essential clauses of this document through normative references in this document.
For referenced documents without a date, only the version corresponding to that date applies to this document; for referenced documents without a date, the latest version (including all amendments) applies to
This document.
GB/T 42018-2022 Information technology artificial intelligence platform computing resources specification
GB/T 42755-2023 Artificial Intelligence Data Labeling Procedure for Machine Learning
GB/T 45401.1-2025 Scheduling and collaboration of artificial intelligence computing devices Part 1.Virtualization and scheduling
3 Terms and definitions
The following terms and definitions apply to this document.
3.1
large-scalemodel
large-scale deep learning model
A deep learning model that is trained based on a large amount of data, has a complex computing architecture, can handle complex tasks, and has a certain degree of generalization.
Note. The number of parameters of a large model is determined by its function and mode, and is generally not less than 100 million. The total amount of data used for large model training is affected by the number of parameters.
The logarithm of the number of parameters of the converged large model is proportional to the logarithm of the total amount of its training data.
3.2
Large-scale model service
Services for developing and applying large models and large model systems, as well as services that use these as a means to support the business activities of the demand side.
Note. Common large model services include large model platform services, large model development and customization services, and large model reasoning and operation services.
3.3
task
The training or inference object being scheduled.
Note. A task is used to complete a relatively independent business function. A task belongs to and only belongs to one job.
[Source. GB/T 25000.23-2019, 4.12, modified]
3.4
Fine-tuning
The process of continuing training a large model using specialized domain data to improve the prediction accuracy of a machine learning model.
Note 1.Specialized domain data are generally production data or synthetic data for specific scenarios.
Note 2.Commonly used fine-tuning methods include prompt word fine-tuning, full parameter fine-tuning, and parameter efficient fine-tuning.
...
