stablecode-completion-alpha-3b
Maintainer: stabilityai
113
👁️
| 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-Completion-Alpha-3B is a 3 billion parameter decoder-only code completion model developed by Stability AI. It was pre-trained on a diverse set of programming languages that were the top used languages based on the 2023 Stack Overflow developer survey. This model can be compared to the StableCode-Instruct-Alpha-3B model, which is the instruction-tuned version, and the Stable Code 3B model, which is a larger 3 billion parameter decoder-only language model pre-trained on code and text.
Model Inputs and Outputs
StableCode-Completion-Alpha-3B is a code generation model designed to provide single or multi-line code completions from a long context window of up to 16,000 tokens. The model takes in code context as input and generates relevant code completions as output.
Inputs
- Code context of up to 16,000 tokens
Outputs
- Single or multi-line code completions relevant to the provided context
Capabilities
StableCode-Completion-Alpha-3B demonstrates strong performance on code generation tasks, outperforming other similarly sized models on benchmarks like MultiPL-E across multiple programming languages. The model can be used to assist developers by providing intelligent code suggestions and completions based on the context.
What Can I Use It For?
StableCode-Completion-Alpha-3B can be integrated into a variety of developer tools and applications to enhance the coding experience. For example, it could be used to power intelligent code editors that provide real-time code completions, or integrated into chatbots and virtual assistants to help developers with coding tasks. The model's broad language support also makes it useful for cross-language development and collaboration.
Things to Try
One interesting aspect of StableCode-Completion-Alpha-3B is its ability to generate code from a long context window. This allows the model to understand and continue complex coding patterns, which could be useful for tasks like implementing algorithms, refactoring code, or expanding on existing functionality. Developers could experiment with providing the model with partially completed code snippets or pseudocode to see how it continues the logic.
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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