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Description
Introducing CodeGeeX, a powerful multilingual code generation model boasting 13 billion parameters, which has been pre-trained on an extensive code corpus covering over 20 programming languages. Leveraging the capabilities of CodeGeeX, we have created a VS Code extension (search 'CodeGeeX' in the Extension Marketplace) designed to support programming in various languages. In addition to its proficiency in multilingual code generation and translation, CodeGeeX can serve as a personalized programming assistant through its few-shot learning capability. This means that by providing a handful of examples as prompts, CodeGeeX can mimic the showcased patterns and produce code that aligns with those examples. This functionality enables the implementation of exciting features such as code explanation, summarization, and generation tailored to specific coding styles. For instance, users can input code snippets reflecting their unique style, and CodeGeeX will generate similar code accordingly. Moreover, experimenting with different prompt formats can further inspire CodeGeeX to develop new coding skills and enhance its versatility. Thus, CodeGeeX stands out as a versatile tool for developers looking to streamline their coding processes.
Description
PanGu-α has been created using the MindSpore framework and utilizes a powerful setup of 2048 Ascend 910 AI processors for its training. The training process employs an advanced parallelism strategy that leverages MindSpore Auto-parallel, which integrates five different parallelism dimensions—data parallelism, operation-level model parallelism, pipeline model parallelism, optimizer model parallelism, and rematerialization—to effectively distribute tasks across the 2048 processors. To improve the model's generalization, we gathered 1.1TB of high-quality Chinese language data from diverse fields for pretraining. We conduct extensive tests on PanGu-α's generation capabilities across multiple situations, such as text summarization, question answering, and dialogue generation. Additionally, we examine how varying model scales influence few-shot performance across a wide array of Chinese NLP tasks. The results from our experiments highlight the exceptional performance of PanGu-α, demonstrating its strengths in handling numerous tasks even in few-shot or zero-shot contexts, thus showcasing its versatility and robustness. This comprehensive evaluation reinforces the potential applications of PanGu-α in real-world scenarios.
API Access
Has API
No
API Access
Has API
No
Screenshots View All
No images available
Integrations
C
Yes
C++
Yes
Go
Yes
Java
Yes
JavaScript
Yes
Python
Yes
Visual Studio Code
Yes
Integrations
C
No
C++
No
Go
No
Java
No
JavaScript
No
Python
No
Visual Studio Code
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
Yes
On-Premises
Yes
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
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
Yes
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
Yes
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
AMiner
Country
China
Website
codegeex.cn/
Vendor Details
Company Name
Huawei
Founded
1987
Country
China
Website
arxiv.org/abs/2104.12369