codegeex2-6b
Maintainer: THUDM
248
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| Property | Value |
|---|---|
| Model Link | View 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
codegeex2-6b is the second-generation model of the multilingual code generation model CodeGeeX (KDD23), which is implemented based on the ChatGLM2 architecture trained on more code data. Due to the advantage of ChatGLM2, codegeex2-6b has been comprehensively improved in coding capability, surpassing larger models like StarCoder-15B for some tasks. It has significantly better performance on the HumanEval-X benchmark, with 57% improvement in Python, 71% in C++, 54% in Java, 83% in JavaScript, 56% in Go, and 321% in Rust, compared to the previous version.
Model Inputs and Outputs
Inputs
- Text: The model takes text input, which could be natural language prompts or code.
Outputs
- Text: The model generates text, which could be code, natural language responses, or a combination of both.
Capabilities
codegeex2-6b is a highly capable multilingual code generation model that can handle a wide range of programming languages. It can assist with tasks such as code generation, code translation, code completion, and code explanation. The model's strong performance on the HumanEval-X benchmark demonstrates its ability to generate high-quality, idiomatic code across multiple languages.
What Can I Use It For?
codegeex2-6b can be leveraged for a variety of applications, including:
- Automated Code Generation: The model can be used to generate code snippets or entire programs based on natural language descriptions or requirements.
- Code Translation: The model can translate code from one programming language to another, making it easier to work with codebases in multiple languages.
- Code Completion: The model can suggest relevant code completions as users type, improving developer productivity.
- Code Explanation: The model can provide explanations or comments for existing code, helping with code understanding and maintenance.
Things to Try
One interesting thing to try with codegeex2-6b is to experiment with different prompting techniques. For example, you could try providing the model with a high-level description of a programming task and see how it generates the corresponding code. You could also try giving the model a partially completed code snippet and ask it to finish the implementation. By exploring the model's capabilities through diverse prompts, you can gain a better understanding of its strengths and limitations.
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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