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What is PanGu-α?

PanGu-α is developed with the MindSpore framework and is powered by an impressive configuration of 2048 Ascend 910 AI processors during its training phase. This training leverages a sophisticated parallelism approach through MindSpore Auto-parallel, utilizing five distinct dimensions of parallelism: data parallelism, operation-level model parallelism, pipeline model parallelism, optimizer model parallelism, and rematerialization, to efficiently allocate tasks among the 2048 processors. To enhance the model's generalization capabilities, we compiled an extensive dataset of 1.1TB of high-quality Chinese language information from various domains for pretraining purposes. We rigorously test PanGu-α's generation capabilities across a variety of scenarios, including text summarization, question answering, and dialogue generation. Moreover, we analyze the impact of different model scales on few-shot performance across a broad spectrum of Chinese NLP tasks. Our experimental findings underscore the remarkable performance of PanGu-α, illustrating its proficiency in managing a wide range of tasks, even in few-shot or zero-shot situations, thereby demonstrating its versatility and durability. This thorough assessment not only highlights the strengths of PanGu-α but also emphasizes its promising applications in practical settings. Ultimately, the results suggest that PanGu-α could significantly advance the field of natural language processing.

What is CodeGeeX?

Meet CodeGeeX, an impressive multilingual code generation model equipped with 13 billion parameters that has been pre-trained on a vast array of code from more than 20 programming languages. Utilizing CodeGeeX's capabilities, we have developed a VS Code extension (search for 'CodeGeeX' in the Extension Marketplace) to aid programmers across diverse languages. Beyond its ability to generate and translate code in multiple languages, CodeGeeX also functions as a tailored programming assistant thanks to its few-shot learning feature. By simply providing a few examples as prompts, CodeGeeX can replicate the demonstrated patterns to create code that is consistent with those examples. This opens the door to a range of exciting functionalities, including code explanation, summarization, and generation that cater to individual coding styles. Users, for example, can input snippets that reflect their personal coding preferences, and CodeGeeX will produce analogous code. Additionally, by trying out various prompt structures, users can encourage CodeGeeX to acquire new programming techniques and boost its adaptability. Consequently, CodeGeeX emerges as an essential tool for developers seeking to optimize their coding workflows and enhance their productivity in software development. Its innovative features truly make it a game-changer in the realm of coding assistance.

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Integrations Supported

C
C++
Go
Java
JavaScript
Python
Visual Studio Code

Integrations Supported

C
C++
Go
Java
JavaScript
Python
Visual Studio Code

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Huawei

Date Founded

1987

Company Location

China

Company Website

arxiv.org/abs/2104.12369

Company Facts

Organization Name

AMiner

Company Location

China

Company Website

codegeex.cn/

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