Meta's Code Llama model, available in three variations—Instruct, Code completion, and Python—offers versatile prompt structuring capabilities for different coding tasks. Released with options of 7 billion, 13 billion, and 34 billion parameters, Code Llama can be accessed via the open-source Ollama project and other model providers. The Instruct model mimics human-like responses and is user-friendly for generating natural language and code outputs, such as writing a Python function for Fibonacci numbers. The model aids in code reviews by identifying simple bugs and can streamline writing boilerplate code for unit tests. The Code completion variation excels in extending code snippets based on initial prompts, while the infill feature allows for completing code between existing blocks. The Python variation is fine-tuned with additional Python tokens, making it suitable for machine learning applications and Python-related tasks, such as rendering Django views. Moreover, tools like Cody and Continue leverage Code Llama for advanced coding solutions, with fine-tuned versions available from Phind and WizardLM teams, enhancing its utility in generating and executing functions locally.