stablecode-completion-alpha-3b-4k
Maintainer: stabilityai
283
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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
StableCode-Completion-Alpha-3B-4K is a 3 billion parameter decoder-only code completion model pre-trained on a diverse set of programming languages that topped the 2023 StackOverflow Developer Survey. It was developed by Stability AI, a leading AI research company. The model is based on the GPT-NeoX library and utilizes techniques like Rotary Position Embeddings and LayerNorm bias terms.
Similar models include the StableCode-Completion-Alpha-3B, which is a 3 billion parameter model trained on a similar dataset but with a longer context length of 16,384 tokens. The StableCode-Instruct-Alpha-3B is an instruction-tuned version of the base completion model, and the stable-code-3b is a larger 3 billion parameter model trained on an even broader set of code and text data.
Model inputs and outputs
Inputs
- Code context: The model takes in a code context of up to 4,096 tokens and generates new code completions.
Outputs
- Code completions: The model generates new code completions based on the provided context, with a maximum of 48 new tokens.
Capabilities
StableCode-Completion-Alpha-3B-4K demonstrates strong performance on code completion tasks across a variety of programming languages, including Python, C++, JavaScript, Java, and PHP. The model can generate coherent and relevant code continuations based on the provided context, making it a useful tool for developers looking to boost their productivity.
What can I use it for?
The StableCode-Completion-Alpha-3B-4K model can be leveraged in a variety of applications, such as:
- Code editors and IDEs: Integrating the model into code editing tools to provide intelligent code completion suggestions, saving developers time and effort.
- Prototyping and experimentation: Exploring new ideas and quickly generating initial code implementations by relying on the model's generative capabilities.
- Educational resources: Developing interactive coding tutorials or exercises that utilize the model to help learners understand programming concepts.
Things to try
One interesting aspect of StableCode-Completion-Alpha-3B-4K is its ability to generate code based on a long context window of up to 4,096 tokens. This can be particularly useful for tasks like refactoring or extending existing code bases, where the model can leverage the broader context to generate coherent and relevant completions.
Another interesting capability to explore is the model's performance on specific programming languages or code domains. By testing the model on a range of tasks and benchmarks, developers can gain insights into the model's strengths and limitations, and identify areas for further fine-tuning or customization.
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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