incoder-6B
Maintainer: facebook
75
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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
The incoder-6B is a 6 billion parameter decoder-only Transformer model trained by Facebook on public open-source code repositories and StackOverflow data. It has the capability to insert and infill code as well as perform standard left-to-right code generation. The model was trained on a diverse set of 28 programming languages, with Python and JavaScript being the most prevalent. This expansive training data allows the incoder-6B to generate code across a wide range of domains.
In comparison, the DeciCoder-1b model is a smaller 1 billion parameter code completion model focused on Python, Java, and JavaScript, while the DeciLM-6b is a 5.7 billion parameter general-purpose language model. The StarCoder2 models are another set of large-scale code generation models, available in 3B, 7B, and 15B parameter sizes, trained on over 600 programming languages.
Model inputs and outputs
Inputs
- Natural language prompts: The
incoder-6Baccepts natural language descriptions or instructions as input, which it then uses to generate relevant code. - Partial code: The model can also take in existing code snippets and continue generating the rest of the code based on the context.
Outputs
- Generated code: The primary output of the
incoder-6Bmodel is synthesized code in any of the 28 supported programming languages. This can range from single lines to multi-function programs. - Inserted/infilled code: In addition to generating code from scratch, the model can also insert or infill code within a given context.
Capabilities
The incoder-6B model demonstrates impressive capabilities in generating coherent and functional code across a wide range of programming languages and domains. Given a natural language prompt, the model can produce relevant code snippets that often closely match what a human developer might write.
For example, providing the prompt "Write a function that calculates the factorial of a given number" results in the model generating a complete Python function to compute factorials. The generated code is not only syntactically correct, but also logically sound and efficient.
What can I use it for?
The incoder-6B model's versatility makes it a powerful tool for a variety of applications. Developers can leverage the model to accelerate their coding workflow by generating initial code templates or filling in missing pieces based on concise descriptions. This can be particularly useful for prototyping, exploration, or when working on unfamiliar domains.
Additionally, the model's ability to insert and infill code can aid in tasks like code refactoring, migration, or automation. By providing contextual information, users can have the model update or modify existing codebases in a consistent and scalable manner.
Things to try
One interesting aspect of the incoder-6B model is its capability to generate code in multiple programming languages. This opens up the possibility of exploring cross-language code generation, where a prompt in one language (e.g., "Write a function to sort a list in ascending order") could result in equivalent implementations in various target languages.
Another intriguing direction is to experiment with fine-tuning the model on domain-specific datasets, such as financial, scientific, or healthcare-related code. This could further enhance the model's ability to generate highly specialized and accurate code for particular applications.
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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