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Description
In the absence of a file system, all information stored in a medium would appear as a single, unbroken mass of data, making it impossible to discern where one piece of information ends and another begins. By organizing the data into discrete units and assigning each a unique identifier, the information can be easily accessed and recognized. This method of categorization mirrors traditional paper-based systems, where each collection of data is referred to as a "file." The framework and set of rules that govern the organization and naming of these data groups is known as a "file system." Network-attached storage (NAS) serves as a file-level data storage solution connected to a network, allowing diverse clients to access data seamlessly. NAS is specifically engineered to deliver file services through its hardware, software, or configuration, often taking the form of a dedicated computer appliance designed for this singular purpose. Consequently, NAS systems enhance the efficiency of file management across various devices and platforms.
Description
Recent breakthroughs in natural language processing, comprehension, and generation have been greatly influenced by the development of large language models. This research presents a system that employs Ascend 910 AI processors and the MindSpore framework to train a language model exceeding one trillion parameters, specifically 1.085 trillion, referred to as PanGu-{\Sigma}. This model enhances the groundwork established by PanGu-{\alpha} by converting the conventional dense Transformer model into a sparse format through a method known as Random Routed Experts (RRE). Utilizing a substantial dataset of 329 billion tokens, the model was effectively trained using a strategy called Expert Computation and Storage Separation (ECSS), which resulted in a remarkable 6.3-fold improvement in training throughput through the use of heterogeneous computing. Through various experiments, it was found that PanGu-{\Sigma} achieves a new benchmark in zero-shot learning across multiple downstream tasks in Chinese NLP, showcasing its potential in advancing the field. This advancement signifies a major leap forward in the capabilities of language models, illustrating the impact of innovative training techniques and architectural modifications.
API Access
Has API
No
API Access
Has API
No
Screenshots View All
No images available
Integrations
PanGu Chat
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
EasyNAS
Website
easynas.org
Vendor Details
Company Name
Huawei
Founded
1987
Country
China
Website
huawei.com