> For the complete documentation index, see [llms.txt](https://docs.zontroy.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zontroy.com/zontroy-ai-models/supported-ai-models/llama.md). # Llama Llama models provide strong open-source alternatives with particular strengths in\ systems programming and performance optimization.\ **Supported Models:** - **Llama3.3-70b:** A large, powerful model with comprehensive\ capabilities across multiple domains. - **Llama3.2-3b:** A compact, efficient model suitable for simpler tasks and quick interactions. - **Llama3.1-405b:** An extremely large model offering state-of-the-art performance for complex tasks. - **Llama3.1-70b:** A balanced model providing strong performance across most development scenarios. - **Llama3.1-8b:** A more efficient model that balances performance with resource utilization. - **Llama3-8b:** A compact model focused on efficiency while maintaining good output quality.\ **Strengths:** - Excellent performance for systems programming languages like C, C++, and Rust - Strong capabilities for performance optimization and low-level programming - Good understanding of memory management and resource utilization - Effective for embedded systems and IoT development - Balanced performance across multiple programming paradigms. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.zontroy.com/zontroy-ai-models/supported-ai-models/llama.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.