Course Intermediate  3537

Course Introduction：Golang has an in-depth understanding of the GPM scheduler model and full scenario analysis. I hope you will gain something from watching this video; it includes the origin and analysis of the scheduler, an introduction to the GMP model, and a summary of 11 scenarios.

Course Advanced  62644

Course Introduction：ThinkPHP is an open source PHP framework that was born to simplify enterprise application development and agile WEB application development. It was first born in early 2006, formerly known as FCS. It was officially renamed ThinkPHP on New Year's Day 2007, and was released under the Apache2 open source agreement. The early ideological architecture originated from Struts. Later, after continuous improvement and improvement, it also learned from many excellent foreign frameworks and models, using object-oriented development structure and MVC model, integrating Struts' Action and DAO ideas and JSP's TagLib ( tag library), RoR's ORM mapping and ActiveRecord mode, which encapsulates CURD and some common operations, single entry mode, etc., and has unique performance in template engine, caching mechanism, authentication mechanism and scalability.

Course Introduction：Xi Xiaoyao Science and Technology Talk Original Author | Xiaoxi and Python large models have an intuitive question behind their huge size: "How should large models be updated?" Under the extremely huge computing overhead of large models, the update of large model knowledge cannot be easily solved. It is not a simple "learning task". Ideally, with the complex changes in various situations in the world, large models should keep up with the times anytime and anywhere. However, the computational burden of training a new large model does not allow large-scale training. The model can be updated in real time, so a new concept "ModelEditing" emerged to achieve effective changes to model data in specific areas without adversely affecting the results of other inputs. where, represents the "effective neighbor"

2023-05-30 comment 0  1272

Course Introduction：Low-rank adaptation of large models is a method to reduce complexity by approximating the high-dimensional structure of a large model with a low-dimensional structure. The aim is to create a smaller, more manageable model representation that still maintains good performance. In many tasks, redundant or irrelevant information may exist in the high-dimensional structure of large models. By identifying and removing these redundancies, a more efficient model can be created while maintaining original performance, and can use fewer resources to train and deploy. Low-rank adaptation is a method that can speed up the training of large models while also reducing memory consumption. Its principle is to freeze the weights of the pre-trained model and introduce the trainable rank decomposition matrix into each layer of the Transformer architecture, thereby significantly reducing the trainability of downstream tasks.

2024-01-23 comment 0  646

Course Introduction：Vivo released its self-developed general artificial intelligence large model matrix - the Blue Heart Model at the 2023 Developer Conference on November 1. Vivo announced that the Blue Heart Model will launch 5 models with different parameter levels, respectively. It contains three levels of parameters: billion, tens of billions, and hundreds of billions, covering core scenarios, and its model capabilities are in a leading position in the industry. Vivo believes that a good self-developed large model needs to meet the following five requirements: large scale, comprehensive functions, powerful algorithms, safe and reliable, independent evolution, and widely open source. The rewritten content is as follows: Among them, the first is Lanxin Big Model 7B, this is a 7 billion level model designed to provide dual services for mobile phones and the cloud. Vivo said that this model can be used in fields such as language understanding and text creation.

2023-11-01 comment 0  1546