Multimodal LLMs contain an encoder, LLM, and a “connector” between the multiple modalities. The LLM is typically pre-trained. For instance, LLaVA uses the CLIP ViT-L/14 for an image encoder and Vicuna ...

The MLP connector then re-projects these image features to match the LLM’s embedding space, using two linear layers with a GELU activation, outputting a tensor of shape (N, 2048). The core LLM is the ...

NExT-GPT, an end-to-end MM-LLM, overcomes limitations of input-only multimodal understanding by integrating multimodal adaptors and diffusion decoders. This allows content processing and generation ...

The main purpose of multimodal machine translation (MMT) is to improve the quality of translation results by taking the corresponding visual context as an additional input. Recently many studies in ...

This document provides a detailed, educational guide to designing and training an 88 billion parameter (88B) multimodal LLM capable of processing text, images, audio, PDFs, and other file types. We'll ...