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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

CodeQwen serves as the coding counterpart to Qwen, which is a series of large language models created by the Qwen team at Alibaba Cloud. Built on a transformer architecture that functions solely as a decoder, this model has undergone extensive pre-training using a vast dataset of code. It showcases robust code generation abilities and demonstrates impressive results across various benchmarking tests. With the capacity to comprehend and generate long contexts of up to 64,000 tokens, CodeQwen accommodates 92 programming languages and excels in tasks such as text-to-SQL queries and debugging. Engaging with CodeQwen is straightforward—you can initiate a conversation with just a few lines of code utilizing transformers. The foundation of this interaction relies on constructing the tokenizer and model using pre-existing methods, employing the generate function to facilitate dialogue guided by the chat template provided by the tokenizer. In alignment with our established practices, we implement the ChatML template tailored for chat models. This model adeptly completes code snippets based on the prompts it receives, delivering responses without the need for any further formatting adjustments, thereby enhancing the user experience. The seamless integration of these elements underscores the efficiency and versatility of CodeQwen in handling diverse coding tasks.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Python
Alibaba Cloud
AtCoder
C
C++
Code Llama
Conda
DeepSeek Coder
GPT-3.5
GPT-4
Go
Hugging Face
Java
JavaScript
LangChain
LeetCode
ModelScope
Ollama
PyTorch
Qwen Chat

Integrations

Python
Alibaba Cloud
AtCoder
C
C++
Code Llama
Conda
DeepSeek Coder
GPT-3.5
GPT-4
Go
Hugging Face
Java
JavaScript
LangChain
LeetCode
ModelScope
Ollama
PyTorch
Qwen Chat

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

AMiner

Country

China

Website

codegeex.cn/

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

github.com/QwenLM/CodeQwen1.5

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