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On February 18th, Shanghai Mita Network Technology Co., Ltd. announced the launch of its latest research mode - 'Shallow Research'. This feature aims to quickly gather information and conduct preliminary analysis using AI technology, helping users obtain valuable research results in a short amount of time.

On January 20, the city of Foshan, Guangdong held a release conference for the AI Application Mall for the mold industry and the establishment meeting for the AI Small Model Industry Consortium, marking an important step forward in Foshan's exploration of 'AI+' enabling new productive forces. AI small models refer to artificial intelligence models optimized and customized for specific industries or fields. These models have significant advantages, including low data requirements, low computational resource consumption, low training and optimization costs, and high privacy security. Chen Xinwen, Vice Mayor of Foshan, mentioned at the conference that Foshan relies on manufacturing.

In recent years, training large language models (LLMs) has become increasingly expensive and complex, with only a few major tech companies possessing the necessary computing resources. However, Google has recently introduced a new method called SALT (Small Model-Assisted Large Model Training), which could fundamentally change the landscape of AI training. Image Source Note: The image was generated by AI, and the image is authorized by service provider Midjourney based on the latest research papers from Google Research and DeepMind, 'A Little Help Goes a Long Way: Leveraging Small Language Models...'

Have you ever been troubled by the huge computational resource demands of AI models? Do you wish to have a powerful yet efficient language model to enhance your work efficiency? Zamba2-7B, a 7B scale language model developed by the Zyphra team, may surprise you. Your AI efficiency dilemma can be solved by Zamba2-7B. In today's world, where AI applications are becoming increasingly popular, we often face the problems of large model sizes and excessive computational resource consumption. This not only limits the popularization of AI technology but also hinders more innovative possibilities.

Jina AI has introduced two small language models designed specifically for transforming raw HTML content into clean and tidy Markdown format, helping us move away from tedious web data processing. The model, named Reader-LM, stands out for its ability to quickly and efficiently convert web content into Markdown files. The benefit of using it is that you no longer need to rely on complex rules or cumbersome regular expressions. These models cleverly auto-remove clutter from web pages.

Recently, a project called Q * was released by the team led by Yan Shuicheng from Kunlun Wanwei in China, in collaboration with a research team from Nanyang Technological University in Singapore. The project aims to improve the inference capabilities of small models. This project differs from OpenAI, yet allows small models to achieve inference abilities comparable to models with tens or even hundreds of times more parameters.