codegeex2-6b-int4
Maintainer: THUDM
46
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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
codegeex2-6b-int4 is the INT4 quantized version of the second-generation multilingual code generation model CodeGeeX2, which was developed by THUDM. CodeGeeX2 is an improvement over the original CodeGeeX model, with enhanced coding capabilities that surpass even larger models like StarCoder-15B for some tasks.
Model inputs and outputs
codegeex2-6b-int4 is a text-to-text model, primarily designed for generating code in response to natural language prompts. It can handle both Chinese and English prompts.
Inputs
- Natural language prompts for code generation, often including a language tag for better performance.
Outputs
- Generated code in the target language, such as Python, C++, Java, JavaScript, Go, or Rust.
Capabilities
The key advantage of codegeex2-6b-int4 is its significantly improved coding capabilities compared to the previous generation CodeGeeX model. On the HumanEval-X benchmark, the model demonstrated substantial performance gains across all six supported languages, ranging from 54% to 321% improvement. In Python, it achieved a 35.9% one-time pass rate, surpassing the larger StarCoder-15B model.
What can I use it for?
codegeex2-6b-int4 can be used as a powerful AI coding assistant for a variety of software development tasks. Some potential use cases include:
- Code generation: Automatically generating code snippets or complete functions based on natural language descriptions.
- Code translation: Translating code between different programming languages.
- Code completion: Suggesting and completing partially written code.
- Code summarization: Generating concise summaries of existing code.
- Debugging assistance: Helping to identify and fix issues in code.
Things to try
One interesting aspect of codegeex2-6b-int4 is its ability to handle code generation in multiple programming languages using a single model. This makes it a versatile tool for developers working across different languages. Additionally, the model's low memory footprint due to INT4 quantization allows for efficient deployment on resource-constrained devices, opening up possibilities for lightweight local AI applications.
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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