CodeLlama-70b-Python-hf
Maintainer: codellama
104
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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 CodeLlama-70b-Python-hf model is part of the Code Llama collection of generative text models ranging in scale from 7 billion to 70 billion parameters. This 70 billion parameter model is specialized for the Python programming language and designed for general code synthesis and understanding tasks. It can be used alongside other Code Llama models like the base 70B version or the 7B Python-specialized model.
Model inputs and outputs
The CodeLlama-70b-Python-hf model takes in text as input and generates text as output. It is optimized for code-related tasks like code completion, infilling, and instruction following, with a particular focus on the Python programming language.
Inputs
- Text prompts
Outputs
- Generated text, such as Python code completions or responses to instructions
Capabilities
The CodeLlama-70b-Python-hf model can be used for a variety of code-related tasks, including code completion, code synthesis, and following instructions to generate code. For example, it could be used to auto-complete a partially written Python function, or to generate a Python script based on a high-level description of its functionality.
What can I use it for?
The CodeLlama-70b-Python-hf model could be useful for developers looking to accelerate their coding workflows, data scientists automating routine data analysis tasks, or even hobbyists creating fun programming projects. The model's Python specialization makes it well-suited for applications that involve generating or understanding Python code, such as code assistants, automated programming tools, or educational applications.
Things to try
One interesting thing to try with the CodeLlama-70b-Python-hf model is giving it partially completed code snippets and seeing how it fills in the gaps. This could help identify areas where the model excels at understanding and generating Python syntax and logic. You could also try providing the model with high-level instructions or prompts and see the types of Python programs it generates in response.
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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