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A deep dive into how Eureka AI addresses three major pain points in R&D - information overload, innovation bottlenecks, and evaluation difficulties - through core features such as a technical Q&A assistant and TRIZ theory innovation. It provides end-to-end AI support solutions from concept to implementation.

In today's rapid development of Large Language Models (LLM), the training and inference costs of models have increasingly become a focus of research and application. Recently, the Tencent Hunyuan team released an important study that delves into the 'Scaling Laws' of low-bit floating point quantization training, which refers to the principles governing the scale of floating point quantization training. The core of this research lies in exploring how to significantly reduce computational and storage costs without sacrificing performance by lowering the precision of the model.

Title: OCR 2.0: A New Generation Optical Character Recognition Model for Easy Image Text Conversion. Recently, researchers have developed a new universal Optical Character Recognition (OCR) model called GOT (Universal OCR Theory). In their paper, they introduced the concept of 'OCR 2.0' for the first time. This new model aims to combine the advantages of traditional OCR systems with the powerful capabilities of large language models. The architecture of GOT is quite advanced, featuring an image encoder with approximately 80 million parameters and 500...