replit-code-v1-3b

Maintainer: replit

Total Score

715

Last updated 5/27/2024

📈

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

replit-code-v1-3b is a 2.7B Causal Language Model developed by Replit that is focused on code completion. It has been trained on a diverse dataset of 20 programming languages, including Markdown, Java, JavaScript, Python, and more, totaling 525B tokens. Compared to similar models like StarCoder and rebel-large, replit-code-v1-3b is tailored specifically for code generation tasks.

Model inputs and outputs

replit-code-v1-3b takes text input and generates text output, with a focus on producing code snippets. The model utilizes advanced techniques like Flash Attention and AliBi positional embeddings to enable efficient training and inference on long input sequences.

Inputs

  • Text prompts, which can include a mix of natural language and code

Outputs

  • Autoregressive text generation, with a focus on producing valid and relevant code snippets
  • The model can generate multi-line code outputs

Capabilities

replit-code-v1-3b excels at code completion tasks, where it can generate relevant and functional code to extend or complete a given programming snippet. It has been trained on a diverse set of languages, allowing it to handle a wide range of coding tasks.

What can I use it for?

The replit-code-v1-3b model is well-suited for applications that involve code generation or assistance, such as:

  • Integrated development environment (IDE) plugins that provide intelligent code completion
  • Automated code generation tools for rapid prototyping or boilerplate creation
  • Educational or learning platforms that help users learn to code by providing helpful suggestions

Things to try

One interesting thing to try with replit-code-v1-3b is to provide it with a partial code snippet and see how it can complete or extend the code. You could also experiment with providing the model with a natural language description of a programming task and see if it can generate the corresponding code.



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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