MicroCloud Hologram Inc. Achieves Breakthrough in Optimizing Scaling Methods for Open-Source Configurations Using Deepseek LLM

Rhea-AI Summary

MicroCloud Hologram Inc. (NASDAQ: HOLO) has announced a breakthrough in optimizing scaling methods for open-source configurations using Deepseek LLM. The company developed a new dynamic balancing mechanism that efficiently adjusts the ratio of parameters to data volume in 7B and 67B configurations, addressing traditional performance bottlenecks in large language models.

HOLO has implemented key technical approaches including supervised fine-tuning (SFT) and direct preference optimization (DPO) to enhance the Deepseek LLM Base model. The company also developed a comprehensive dataset covering multiple fields and languages to support the model's pre-training phase.

The technology aims to improve efficiency in applications such as intelligent customer service, smart writing, and intelligent translation, contributing to industry digital transformation.

Positive

• Breakthrough in scaling technology optimization for large language models
• Development of new balancing mechanism improving computational efficiency
• Implementation of advanced technical approaches (SFT and DPO)
• Creation of comprehensive multi-field, multi-language dataset

Negative

• None.

SHENZHEN, China, Feb. 26, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, delved deeply into scaling laws and made unique discoveries, providing key support for the scaling of large models in two commonly used open-source configurations: 7B and 67B.

When addressing the relationship between model parameters and data volume, HOLO discovered a completely new balancing mechanism. Traditional scaling methods often face issues of insufficient data or wasted computational resources when model parameters increase, leading to performance bottlenecks. HOLO's new mechanism, however, dynamically adjusts the ratio of parameters to data volume based on the specific needs of the model and the limitations of computational resources. This allows the model to fully utilize computational resources during the scaling process, avoiding the common performance bottlenecks seen in traditional methods, thereby achieving efficient scaling at different scales.

As a result, HOLO conducted an in-depth analysis of scaling laws and identified a series of key factors that can optimize the scaling of large language models. These discoveries broke the limitations of traditional understanding and provided new directions for achieving efficient model scaling at different scales. For example, in addressing the relationship between model parameters and data volume, HOLO's research revealed a new balancing mechanism, allowing models to better utilize computational resources during the scaling process and avoiding the common performance bottlenecks found in traditional scaling methods.

Guided by scaling laws, the Deepseek LLM project focuses on the long-term development of open-source language models, striving to build a widely influential open-source language model ecosystem through technological innovation and community collaboration. Deepseek LLM not only focuses on improving model performance but also emphasizes model interpretability, security, and sustainable development, aiming to provide a reliable foundation for open-source language models.

To support the pre-training phase of Deepseek LLM, HOLO developed a massive dataset that covers a wide range of fields and languages. Carefully selected and preprocessed, this dataset provides the model with rich knowledge and linguistic patterns. By continuously expanding the dataset, Deepseek LLM is able to better adapt to different application scenarios and user needs, enhancing the model's generalization capabilities and performance.

HOLO made a series of optimizations and improvements to the Deepseek LLM Base model, with supervised fine-tuning (SFT) and direct preference optimization (DPO) being two key technical approaches. Through SFT, the model can perform targeted learning and adjustments for specific tasks, improving its performance on those tasks. DPO, on the other hand, directly optimizes the model's output preferences, making the generated results more aligned with user expectations and needs. The application of these optimization techniques has enabled Deepseek LLM to demonstrate outstanding performance across various benchmark tests.

HOLO's breakthroughs in large language model scaling technologies and the launch of the Deepseek LLM project can foster the prosperity and development of the open-source community. The application of these technologies will bring new opportunities and transformations to various industries. For instance, in fields such as intelligent customer service, smart writing, and intelligent translation, Deepseek LLM can significantly enhance work efficiency and service quality, driving the digital transformation and upgrading of industries.

About MicroCloud Hologram Inc.

MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud's holographic technology services include high-precision holographic light detection and ranging ("LiDAR") solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems ("ADAS"). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud's holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud's holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/

Safe Harbor Statement

This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as "may," "will," "intend," "should," "believe," "expect," "anticipate," "project," "estimate," or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company's expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company's goals and strategies; the Company's future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission ("SEC"), including the Company's most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company's filings with the SEC, which are available for review at www.sec.gov. The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof.

 

 

 

View original content:https://www.prnewswire.com/news-releases/microcloud-hologram-inc-achieves-breakthrough-in-optimizing-scaling-methods-for-open-source-configurations-using-deepseek-llm-302386182.html

SOURCE MicroCloud Hologram Inc.

FAQ

What breakthrough did HOLO achieve in scaling methods for Deepseek LLM?

HOLO developed a new dynamic balancing mechanism that optimizes the ratio of parameters to data volume in 7B and 67B configurations, improving computational resource utilization and avoiding traditional performance bottlenecks.

How does HOLO's Deepseek LLM optimize model performance?

HOLO uses supervised fine-tuning (SFT) for targeted task learning and direct preference optimization (DPO) to align outputs with user expectations.

What applications will benefit from HOLO's Deepseek LLM technology?

The technology will enhance intelligent customer service, smart writing, and intelligent translation services, supporting digital transformation across industries.

What dataset improvements has HOLO made for Deepseek LLM?

HOLO developed a massive dataset covering multiple fields and languages, carefully selected and preprocessed to provide rich knowledge and linguistic patterns.