CodeShell-7B

Maintainer: WisdomShell

Total Score

80

Last updated 5/17/2024

📉

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

CodeShell-7B is a multi-language code LLM developed by the Knowledge Computing Lab of Peking University. The model has 7 billion parameters and was trained on 500 billion tokens with a context window length of 8194. On authoritative code evaluation benchmarks (HumanEval and MBPP), CodeShell-7B achieves the best performance of its scale.

Compared to similar models like replit-code-v1-3b, CodeShell-7B is a larger 7B parameter model trained on more data (500B vs 525B tokens). It also provides a more comprehensive ecosystem with open-source IDE plugins, local C++ deployment, and a multi-task evaluation system.

Model inputs and outputs

CodeShell-7B is a text-to-text model designed for code generation. The model takes in text prompts and outputs generated code.

Inputs

  • Text prompts describing a coding task or providing context for the desired output

Outputs

  • Generated code in a variety of programming languages including C++, Python, JavaScript, and more
  • The generated code is intended to be a solution to the given prompt or to continue the provided context

Capabilities

CodeShell-7B demonstrates impressive code generation abilities, outperforming other models of its size on benchmarks like HumanEval and MBPP. It can generate functioning code across many languages to solve a wide range of programming problems.

What can I use it for?

The CodeShell-7B model can be used for a variety of software development tasks, such as:

  • Generating code snippets or entire functions based on natural language descriptions
  • Assisting with coding by providing helpful completions and suggestions
  • Automating repetitive coding tasks
  • Prototyping new ideas and quickly generating working code
  • Enhancing developer productivity by offloading mundane coding work

The model's strong performance and comprehensive ecosystem make it a powerful tool for both individual developers and teams working on software projects.

Things to try

One interesting aspect of CodeShell-7B is its ability to generate code in multiple programming languages. You could experiment with prompting the model to translate a code snippet from one language to another, or to generate implementations of the same algorithm in different languages.

Another compelling use case is to provide the model with high-level requirements or user stories and have it generate the corresponding working code. This could be a great way to rapidly prototype new features or explore different design approaches.

Overall, the robust capabilities and flexible deployment options of CodeShell-7B make it a valuable tool for advancing your software development workflows and boosting productivity.



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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