stable-code-3b
Maintainer: stabilityai
613
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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
stable-code-3b is a 2.7B parameter decoder-only language model pre-trained on 1.3 trillion tokens of diverse textual and code datasets. Developed by Stability AI, stable-code-3b demonstrates state-of-the-art performance on the MultiPL-E metrics across multiple programming languages compared to models of similar size. It outperforms other code generation models like CodeLLama, Deepseek Coder, and Wizard Coder on tasks like Python, C++, and JavaScript.
Model inputs and outputs
stable-code-3b is a text-to-text model, taking in prompts as input and generating relevant code as output. It can handle long context, with the ability to generate code based on sequences up to 16,384 tokens. The model also supports a "Fill in Middle" (FIM) capability, where it can complete partially-written code snippets.
Inputs
- Text prompts for code generation, up to 16,384 tokens
- Partial code snippets for the "Fill in Middle" capability
Outputs
- Generated code in one of 18 programming languages the model was trained on, including Python, C++, JavaScript, Java, PHP, and Rust
Capabilities
stable-code-3b excels at generating high-quality, functional code across a variety of programming languages. It can be used to write entire programs from scratch, or fill in missing sections of existing code. The model's strong performance on the MultiPL-E benchmark suggests it can handle a wide range of coding tasks and produce code that is syntactically correct and logically sound.
What can I use it for?
stable-code-3b can be a valuable tool for developers, data scientists, and anyone working with code. It could be used to speed up prototyping and development by automatically generating boilerplate code or completing repetitive tasks. The model could also be fine-tuned on domain-specific datasets to create customized code generation models for specialized applications.
Things to try
Experiment with different prompting techniques to see how stable-code-3b responds. Try providing high-level descriptions of the functionality you want, or giving it partially-completed code snippets to fill in. You can also try adjusting parameters like temperature and top-k/top-p values during generation to control the creativity and diversity of the output. By exploring the model's capabilities, you can unlock new ways to streamline your coding workflows.
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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