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 ...

In addition to the LLM tuning, we also fine-tune the decoding end of NExT-GPT. We align the modal signal token representation encoded by the output projection with the gold multimodal caption ...

Recently many studies in neural machine translation have attempted to obtain high-quality multimodal representation of encoder or decoder via attention mechanism. However, attention mechanism does not ...

LLaVa emerged as a prominent open-source framework, innovating by using text-only GPT models to expand multimodal datasets. Its architecture, featuring a pre-trained image encoder ... core LLM is the ...

This document provides a detailed, educational guide to designing and training an 88 billion parameter (88B) multimodal LLM capable of processing text ... transformer-based model with ...

It has 7 billion parameters and can process images up to 1024×1024 resolution, which is one of the highest among multimodal models ... which combines a frozen visual encoder with a frozen LLM called ...