Meta introduces Llama 3.2, the LLM model, which adds support for image input, boasting capabilities similar to GPT-4o-mini. It also comes with a smaller 1B model with near-comparable abilities to other small-sized models.
The development approach for Llama 3.2’s image input model relies on creating an image encoder to convert data for the language model. During the initial training phase, only the image encoder is trained without modifying the language model to ensure its language capabilities remain constant. Subsequently, additional image-based knowledge is incorporated and training concludes with further emphasis on enhancing security.
The small-sized model utilizes pruning techniques, reducing a larger model to a smaller size while striving to retain maximum knowledge. Starting from the Llama 3.1 8B model, gradual model reduction occurs followed by distillation techniques to train the shrunken model to closely match the larger model’s capabilities.
Finally, Meta releases the Llama Stack Distribution toolkit for development, including the Llama CLI for model configuration and running, client code in various languages for developers, Docker for servers, and the Agent API Provider. Users can deploy this stack in multiple environments, from personal machines like Ollama, to cloud services offered by various providers, extending functionality to mobile devices as well.
TLDR: Meta unveils Llama 3.2 with image input support and a smaller model, along with a toolkit for development and deployment in various environments.
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