codellama-13b-python
Maintainer: meta
15.2K
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Model overview
codellama-13b-python is a 13 billion parameter Llama language model fine-tuned by Meta for coding with Python. It is part of the Code Llama family of models, which also includes variants like Code Llama - Python and Code Llama - Instruct. These models leverage the state-of-the-art Llama 2 architecture and provide capabilities such as code generation, infilling, and zero-shot instruction following for programming tasks.
Model inputs and outputs
codellama-13b-python takes text prompts as input and generates continuations or completions of that text. The model is particularly adept at generating and completing Python code based on the provided context. Its outputs can range from short code snippets to longer programs, depending on the input prompt.
Inputs
- Prompt: The text that the model will use as a starting point to generate output.
Outputs
- Generated text: The model's continuation or completion of the input prompt, which may include Python code.
Capabilities
The codellama-13b-python model is capable of generating high-quality Python code based on the provided context. It can understand and complete partial code snippets, write entire functions or classes, and even generate complex programs from a high-level description. The model also demonstrates strong code understanding and can be used for tasks like code summarization, translation, and refactoring.
What can I use it for?
codellama-13b-python can be a valuable tool for a variety of software development and data science tasks. Developers can use it to boost productivity by automating repetitive coding tasks, generating boilerplate code, or prototyping new ideas. Data scientists can leverage the model to generate custom data processing scripts, model training pipelines, or visualization code. Educators and students can also use the model to aid in learning programming concepts and syntax.
Things to try
One interesting aspect of codellama-13b-python is its ability to perform code infilling, where it can generate missing parts of a code snippet based on the surrounding context. This can be useful for tasks like fixing bugs, implementing new features, or exploring alternative solutions to a problem. You can also try prompting the model with high-level descriptions of programming tasks and see how it translates those into working 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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