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海天瑞声(688787.SH):已与相关客户签订了大模型训练数据集相关的业务合同或意向订单

Haitian Ruisheng (688787.SH): A business contract or intended order related to the large model training data set has been signed with the relevant customer

Gelonghui Finance ·  Mar 15 16:13

Gelonghui, March 15 | Haitian Ruisheng (688787.SH) said on the investor interactive platform that the company is a professional artificial intelligence training data service provider and is currently investing and constructing in the field of AIGC/big model data services. On the one hand, the company will develop a professional training data set related to the big model based on the existing business model. After the data set is completed, it will provide no less than 10 categories of professional data sets that can be trained and evaluated for large models, significantly improving the category and quality of training data sets for large models in the industry, helping to gather various high-value data resources such as public data and social data, and achieve training and learning based on the general capabilities of large models and vertical field data. On the other hand, the company plans to develop a vertical model for design data production. The company's full-stack self-developed vertical data production model will assist in completing data design and processing rules for multiple downstream tasks, forming multiple generation capabilities including data cleaning scheme design, instruction plan design, instruction generalization and migration, instruction data verification, multi-modal data solutions, etc., as well as the ability to generate raw data and labeling results under the above scheme. The company has signed business contracts or intended orders related to large model training data sets with relevant customers. In addition, the company has signed cooperation agreements with other well-known organizations in the field of large models. In the future, it plans to cooperate on artificial intelligence big model data processing technology, large model data set supply, and large model evaluation.

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