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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

Screenshots View All

No images available

Integrations

PanGu Chat No 

Integrations

PanGu Chat Yes 

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

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