Llama 3.2 has been introduced as a significant advancement in edge AI and vision technology, featuring a range of open and customizable models designed for various applications. This release includes small and medium-sized vision large language models (LLMs) with 11 billion and 90 billion parameters, as well as lightweight text-only models with 1 billion and 3 billion parameters. These models are optimized for deployment on edge and mobile devices, making them suitable for tasks such as summarization, instruction following, and rewriting, all while supporting a context length of 128,000 tokens. The vision models are designed to excel in image understanding tasks, providing capabilities such as document-level comprehension, image captioning, and visual grounding. They can process both text and image inputs, allowing for complex reasoning and interaction with visual data. For instance, users can query the model about sales data represented in graphs or seek navigational assistance based on maps. The lightweight models, on the other hand, focus on multilingual text generation and tool-calling functionalities, enabling developers to create privacy-focused applications that operate entirely on-device. Llama 3.2 is supported by a robust ecosystem, with partnerships established with major technology companies like AWS, Databricks, and Qualcomm, ensuring that the models can be easily integrated into various platforms. The release also includes the Llama Stack, a set of tools designed to simplify the development process across different environments, including on-premises, cloud, and mobile devices. The models have undergone extensive evaluation, demonstrating competitive performance against leading foundation models in both image recognition and language tasks. The architecture of the vision models incorporates new adapter weights that allow for seamless integration of image processing capabilities into the existing language model framework. This innovative approach ensures that the models maintain their text-based functionalities while expanding their capabilities to include visual reasoning. In addition to the technical advancements, Llama 3.2 emphasizes responsible AI development. New safety measures, such as Llama Guard, have been introduced to filter inappropriate content and ensure safe interactions with the models. The lightweight versions of the models have been optimized for efficiency, making them more accessible for deployment in constrained environments. Overall, Llama 3.2 represents a significant leap forward in the field of AI, promoting openness and collaboration within the developer community. The models are available for download and immediate development, encouraging innovation and the creation of new applications that leverage the power of generative AI. The commitment to responsible AI practices and the continuous engagement with partners and the open-source community highlight the potential for Llama 3.2 to drive meaningful advancements in technology and society.