Company
Date Published
Author
Conor Bronsdon
Word count
1293
Language
English
Hacker News points
None

Summary

Multimodal Large Language Models (MLLMs) are reshaping how we process and integrate text, images, audio, and video. To effectively build, evaluate, and monitor a Multimodal LLM, it's essential to understand the architecture of these models, which typically follow one of two primary approaches: alignment-focused or early-fusion architectures. The alignment architecture uses pretrained vision models connected to pretrained LLMs through specialized alignment layers, while the early-fusion architecture processes mixed visual and text tokens together in a unified transformer. MLLMs have seen rapid advancement, with both closed and open-source models pushing the boundaries of what's possible. However, addressing challenges such as hallucinations, data quality, and monitoring strategies is crucial to ensure optimal real-world performance. Evaluating your multimodal LLMs effectively requires specialized metrics for cross-modal performance, consistency, and bias detection, which can be handled by platforms like Galileo's Luna Evaluation Foundation Models.