The open-source AI models you can fine-tune, distill and deploy anywhere. Choose from our collection of models: Llama 3.1, Llama 3.2, Llama 3.3.
Open-Source Accessibility
Llama models are open-source, allowing developers to fine-tune and deploy them across various platforms.
Multimodal Capabilities
Certain versions of Llama, such as Llama 3.2, support multimodal inputs, enabling interpretation of both text and images.
Extensive Documentation and Support
Llama provides comprehensive documentation, how-to guides, and community support to assist developers in model implementation and deployment.
Responsible Use Guidelines
Meta offers a Responsible Use Guide to help developers build products powered by Llama models in a responsible manner.
Llama is a suite of open-source large language models developed by Meta AI, offering models like Llama 3.1, 3.2, and 3.3. These models are designed for fine-tuning and deployment across various platforms, supporting tasks such as text generation and multimodal processing.
Llama models range from 1B to 405B parameters, support text and multimodal capabilities, and are accessible through Meta and partners like Hugging Face and Kaggle.
Developing AI-powered chatbots and virtual assistants.
Creating content generation tools for various industries.
Implementing multimodal applications that process text and images.
Enhancing research in natural language processing and understanding.
Llama is a family of open-source large language models (LLMs) developed by Meta AI, designed to be fine-tuned, distilled, and deployed across various platforms.
Llama offers models such as Llama 3.1, Llama 3.2, and Llama 3.3, each with unique features and enhancements.
Llama models can be obtained directly from Meta or through partners like Hugging Face and Kaggle.
Llama models are licensed under a bespoke commercial license that allows for broad commercial use, with specific terms outlined in the Llama Community License Agreement.
While primarily trained on English, Llama models include data from 27 other languages, though performance may vary across languages.
