starcoder2-7b

Maintainer: bigcode

Total Score

138

Last updated 5/28/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The starcoder2-7b model is a 7B parameter AI model trained by bigcode on 17 programming languages from The Stack v2 dataset. The model uses advanced techniques like Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on over 3.5 trillion tokens.

The starcoder2-7b model is comparable to other large language models like starcoder2-15b, starcoder, and starcoderbase in terms of its scale and capabilities, but was trained on a more focused set of programming languages.

Model inputs and outputs

The starcoder2-7b model is a text-to-text transformer model, meaning it takes in text as input and generates text as output. The model can be used for a variety of text generation tasks, such as code completion, commenting, and summarization.

Inputs

  • Text prompts: The model accepts arbitrary text prompts as input, which can be used to guide the model's generation.

Outputs

  • Generated text: The model outputs generated text, which can be code, comments, or other forms of text.

Capabilities

The starcoder2-7b model is capable of generating high-quality code in 17 programming languages, including Python, Java, and JavaScript. The model can be used for tasks like code completion, where the model can suggest the next few lines of code based on a given prompt. The model can also be used for code summarization, where the model can generate a concise summary of a given code snippet.

What can I use it for?

The starcoder2-7b model can be used for a variety of applications in the software development and AI research domains. Some potential use cases include:

  • Code generation: The model can be used to generate boilerplate code, implement algorithms, or complete partially written functions.
  • Code summarization: The model can be used to generate concise summaries of code snippets, which can be useful for documentation or code review.
  • Code translation: The model can be used to translate code between different programming languages.
  • Code refactoring: The model can be used to suggest improvements or optimizations to existing code.

Things to try

One interesting thing to try with the starcoder2-7b model is using the Fill-in-the-Middle (FIM) technique, which allows the model to generate text by filling in the middle of a provided prefix and suffix. This can be useful for tasks like code completion, where the user provides the function signature and the model generates the function body.

Another interesting thing to try is fine-tuning the model on a specific domain or task. Since the starcoder2-7b model was trained on a broad dataset, fine-tuning it on a more specialized dataset could improve its performance on certain tasks.



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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