Multimodal

Emu2-Chat is a conversational AI model designed for engaging and context-aware chat interactions, optimized for natural language understanding and generating human-like responses across various domains.

CogVLM is an advanced open-source vision-language model developed by Tsinghua University, capable of handling various multimodal AI tasks.

DeepSeek-VL is a cutting-edge open-source multimodal AI model that integrates vision and language processing to enable tasks like image captioning, semantic search, and cross-modal retrieval.

ERNIE-ViL is a powerful multimodal AI model developed by Baidu that integrates vision and language understanding into a unified framework.

CLIP (Contrastive Language–Image Pretraining) is an open-source multimodal model developed by OpenAI that learns visual concepts from natural language supervision.

LLaVA-NeXT is a next-generation multimodal large language model developed by the University of Wisconsin–Madison, building upon the LLaVA framework. It excels in visual perception and language understanding.

Chameleon 7B is a multimodal foundation model developed by Meta AI that unifies text, image, and code understanding within a single early-fusion transformer architecture.

Kosmos-2.5 is Microsoft’s multimodal AI model that integrates text, image, and audio understanding in a unified architecture.