stablecode-instruct-alpha-3b
Maintainer: stabilityai
301
🗣️
| 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
StableCode-Instruct-Alpha-3B is a 3 billion parameter decoder-only instruction tuned code model pre-trained on a diverse set of programming languages that topped the StackOverflow developer survey. It builds upon the StableCode-Completion-Alpha-3B model, with additional fine-tuning on code instruction datasets. This model demonstrates strong performance across a range of programming languages, outperforming some larger models like CodeLLama and Wizard Coder on the MultiPL-E benchmark.
Model inputs and outputs
Inputs
- Text instructions for generating code
Outputs
- Generated code based on the provided instructions
Capabilities
StableCode-Instruct-Alpha-3B is capable of generating code based on natural language instructions. It can handle a wide variety of programming languages and tasks, from simple utility functions to more complex algorithms. The model's strong performance on the MultiPL-E benchmark suggests it is a capable code generation tool across many domains.
What can I use it for?
StableCode-Instruct-Alpha-3B can be used as a foundation for building applications that require code generation from natural language, such as programming assistants, code editors with intelligent autocomplete, and even low-code/no-code platforms. Developers can fine-tune the model further on their own datasets and use cases to create custom code generation tools tailored to their needs.
Things to try
One interesting aspect of StableCode-Instruct-Alpha-3B is its ability to generate code in multiple programming languages. Developers can experiment with providing instructions in natural language and observe how the model generates code in different languages, potentially discovering new ways to leverage this cross-language capability. Additionally, exploring the model's performance on more complex programming tasks, such as implementing algorithms or building full applications, can provide valuable insights into 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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