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

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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.