#multimodal AI

MMMU and MMMU-Pro provide robust benchmarks essential for evaluating multimodal models, focusing on tasks that require college-level knowledge and reasoning across diverse disciplines. These benchmarks consist of 11.5K questions covering six core fields, designed to test the models' proficiency in integrating visual and textual information. Specifically, MMMU-Pro introduces more stringent assessments by implementing vision-only input and expanding candidate options. Despite the challenging nature of these tasks, even advanced models like GPT-4V achieve only moderate accuracy, indicating significant potential for improvement in multimodal AI, moving towards expert AGI. For detailed evaluations, visit EvalAI, and access the datasets through Hugging Face.

Discover a versatile AI library designed for multimodal content understanding and generation. This solution addresses image, text, and multilingual applications, offering embedding models of up to 768 dimensions for efficient searches in different languages. It includes support for ONNX, CoreML, and PyTorch for deployment on various devices. Optimize performance with quantization-aware capabilities whether on servers or smartphones, utilizing compact transformer models for diverse applications.

An open-source tool that combines Stable Diffusion, GPT, and TTS for creating animated stories from text prompts. It offers narrative, visual, and audio outputs, all customizable through CLI and Python interfaces. Installable via PyPI or GitHub, it serves as a versatile platform for those exploring AI-driven storytelling.

SEED-X, a unified foundation model, serves multimodal AI needs with advanced comprehension and generation, from image design to personal assistance. Features include SEED-Story for story creation and SEED-Data-Edit for precise image editing. Experience enhanced models and fast demos.