codegeex4-all-9b
Maintainer: THUDM
172
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
codegeex4-all-9b is an open-source multilingual code generation model developed by THUDM. It is the latest version of the CodeGeeX model series, which have been continually trained on the GLM-4-9B model to enhance their code generation capabilities. The codegeex4-all-9b model can perform a variety of tasks such as code completion, generation, interpretation, web search, and function calls, covering diverse software development scenarios.
Compared to previous versions, codegeex4-all-9b has achieved highly competitive performance on public benchmarks like BigCodeBench and NaturalCodeBench, surpassing much larger general-purpose models while maintaining efficient inference speed and low model size (less than 10B parameters).
Model Inputs and Outputs
Inputs
- Code Prompts: The model can accept code prompts in various programming languages to generate, complete, or interpret code.
- Natural Language Prompts: The model can also accept natural language prompts to perform tasks like code search, summarization, and translation.
Outputs
- Generated Code: The model can output generated code in response to code or natural language prompts.
- Interpreted Code: The model can provide interpretations and explanations of existing code.
- Search Results: The model can return relevant code snippets or functions based on natural language queries.
Capabilities
The codegeex4-all-9b model excels at a wide range of code-related tasks, demonstrating strong performance on benchmarks across multiple programming languages. It can effectively generate, complete, interpret, and translate code, making it a powerful tool for software developers. The model's multilingual capabilities allow it to support diverse programming languages, enabling global collaboration and code sharing.
What Can I Use It For?
Developers can leverage the codegeex4-all-9b model to streamline their workflows and increase productivity. Some potential use cases include:
- Code Generation: Automatically generate boilerplate code, implement algorithms, or create new functionality based on natural language descriptions.
- Code Completion: Complete partially written code by suggesting the most likely next steps or missing components.
- Code Interpretation: Gain insights and explanations about existing code, facilitating debugging and code understanding.
- Code Search: Quickly find relevant code snippets or functions based on natural language queries, enabling efficient code reuse.
- Multilingual Code Support: Create, translate, and work with code in various programming languages, fostering global collaboration.
Things to Try
Experiment with the codegeex4-all-9b model to explore its capabilities in different scenarios. Try providing code prompts in various languages to see how the model generates or completes the code. Additionally, test the model's natural language understanding by asking it to summarize, translate, or explain existing code. Observe how the model's performance compares to your expectations or previous experiences with code generation tools.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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