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  • Drop-in replacements: The new vision-capable models are fully compatible with existing LLaMA 3.1 setups, requiring no code changes.
  • Edge Devices: Meta has also released tiny 1 billion and 3 billion parameter text-only models optimized for edge devices, such as smartphones, computers, and Internet of Things (IoT) devices.
  • Pre-trained and Instruction-tuned: The smaller models are ready for use, offering state-of-the-art performance in tasks like summarization, instruction following, and rewriting.
  • LLaMA Stack: Meta's first LLaMA stack distribution provides developers with a comprehensive toolset for working with LLaMA models, simplifying the development process.

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Meta Unveils LLaMA 3.2: Revolutionizing AI Capabilities

In a significant breakthrough, Meta has released LLaMA 3.2, a cutting-edge AI model that boasts enhanced capabilities, including vision-based intelligence.
This latest iteration builds upon the impressive advancements of LLaMA 3.1, offering improved performance, efficiency, and versatility.

Key features of LLaMA 3.2

  • Vision Capabilities: LLaMA 3.2 introduces vision-based intelligence, enabling the model to process and understand images. This feature is supported by the 11 billion and 90 billion parameter versions.

Technical Advancements

  • Adapter Weights: Meta integrated image encoder into the language model using adapter weights, enabling image reasoning capabilities.
  • Cross-Attention layers: The adapter consists of cross-attention layers that feed image encoder representations into the language model.
    Alignment Training: Meta employed alignment training, supervised fine-tuning, rejection sampling, and direct preference optimization to refine the model.
  • Synthetic data Generation: LLaMA 3.1 was used to generate synthetic data for question-answer pairs on tOP of in-domain images.

Implications and Future Directions

  • Edge AI Compute: The release underscores Meta's commitment to pushing AI compute to edge devices, ensuring more efficient and localized processing.
  • Open-source ecosystem: - - Meta's investments in tooling, services, and partnerships foster a thriving open-source ecosystem.
  • Custom applications: developers can fine-tune and deploy LLaMA 3.2 using Torch Tune and Torch Chat.