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RT @omarsar0: Not sure how I missed it but this is a great write-up on building effective agents. After building LLM applications to LLM-p….

To get maximum out of llm tools, make decision for them an duse them to fill in the gap between the decisions.

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For the 8B model, this adds 3B parameters, and for the 70B model, this adds 20 billion parameters.

The image encoder takes 224×224 resolution images, dividing them into a 14×14 grid of patches, with each patch sized at 16×16 pixels. The cross-attention layers add a substantial amount of parameters and are only added in every fourth transformer block.

instead of a pretrained model like CLIP. The vision transformer was pretrained over five epochs before being connected to the LLM.

The models undergo instruction and preference finetuning, similar to the Llama 3 model text-only training. The researchers used a vision transformer (ViT-H/14) with 630 million parameters, pretrained from scratch on a dataset of 2.5 billion image-text pairs,.

The training process involves multiple iterations, starting with the Llama 3.1 text models, followed by the addition of the image encoder and projection layers, and then pretraining on image-text data.

This approach is intentional, allowing the 11B and 90B multimodal models to be used as drop-in replacements for the Llama 3.1 8B and 70B text-only models on text tasks.

The released models focus only on image and text, despite the figure depicting video and speech as possible modalities. Unlike traditional multimodal LLM development, Llama 3.2 updates the image encoder but does not update the language model's parameters during pretraining.

LLAMA 3.2 Multimodal .The Llama 3.2 models come in two variants: 11-billion and 90-billion parameter versions, both of which are image-text models. The models use a cross-attention-based approach, as illustrated in the figure, which allows for the processing of image and text.

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