CodeLlama-13b-Instruct-hf

Maintainer: codellama

Total Score

136

Last updated 5/28/2024

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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

CodeLlama-13b-Instruct-hf is a 13 billion parameter version of the Code Llama family of large language models developed by Meta. The Code Llama models are designed for general code synthesis and understanding, with variants focused on Python and instruction-following. This 13B instruct-tuned model is optimized for safe deployment as a code assistant.

The CodeLlama-7b-Instruct-hf and CodeLlama-70b-hf are similar models in the Code Llama family, with 7 billion and 70 billion parameters respectively. Like this 13B model, they offer capabilities in code completion, infilling, instructions/chat, and Python-specific tasks.

Model inputs and outputs

Inputs

  • Text input only

Outputs

  • Generates text only, including code

Capabilities

CodeLlama-13b-Instruct-hf can assist with a variety of code-related tasks, such as code completion, infilling, and following natural language instructions. It is particularly adept at understanding and generating Python code. The model has been carefully trained to provide safe and helpful responses.

What can I use it for?

The CodeLlama-13b-Instruct-hf model can be used for a range of commercial and research applications involving code generation and understanding. This includes building code assistants, code completion tools, and programming tutors. The model's strong performance on Python-specific tasks makes it well-suited for Python-focused applications.

When deploying the model, it's important to test for safety and align it with your specific use case, as with any large language model. The Responsible Use Guide provides guidance on best practices.

Things to try

Try using CodeLlama-13b-Instruct-hf to generate code completions, refine existing code snippets, or provide natural language instructions for coding tasks. Experiment with different prompting techniques and generation parameters to see how the model responds. Remember to thoroughly test for safety and alignment with your use case.



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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