Core Insights - MicroCloud Hologram Inc. (HOLO) has made significant advancements in scaling laws for large models, particularly in open-source configurations of 7B and 67B [1] - The company has discovered a new balancing mechanism that optimizes the relationship between model parameters and data volume, allowing for efficient scaling without performance bottlenecks [2][3] Scaling Mechanism - HOLO's new mechanism dynamically adjusts the ratio of model parameters to data volume based on specific model needs and computational limitations, enhancing resource utilization during scaling [2] - This approach addresses traditional scaling issues, leading to improved performance and efficiency across different scales [3] Deepseek LLM Project - The Deepseek LLM project aims to develop a robust open-source language model ecosystem through technological innovation and community collaboration, focusing on model performance, interpretability, security, and sustainable development [4] - HOLO has created a comprehensive dataset for the pre-training phase of Deepseek LLM, enhancing the model's adaptability and generalization capabilities [5] Optimization Techniques - The company has implemented supervised fine-tuning (SFT) and direct preference optimization (DPO) to improve the Deepseek LLM Base model, resulting in enhanced performance on specific tasks and alignment with user expectations [6] Industry Impact - HOLO's breakthroughs in large language model scaling technologies are expected to drive digital transformation across various industries, including intelligent customer service, smart writing, and intelligent translation, significantly improving work efficiency and service quality [7]
MicroCloud Hologram Inc. Achieves Breakthrough in Optimizing Scaling Methods for Open-Source Configurations Using Deepseek LLM