CodeShell-7B
Maintainer: WisdomShell
81
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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
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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