Most of the existing multi-modal models, hindered by their incapacity to adeptly manage interleaved image-and-text inputs in multi-image, multi-round dialogues, face substantial constraints in resource allocation for training and data accessibility, impacting their adaptability and scalability across varied interaction realms.
To address this, we present the DeepSpeed-VisualChat framework, designed to optimize Large Language Models (LLMs)  by incorporating multi-modal capabilities, with a focus on enhancing the proficiency of Large Vision and Language Models in handling interleaved inputs.
Our framework is notable for (1)  its open-source support for multi-round and multi-image dialogues, (2)  introducing an innovative multi-modal casual attention mechanism, and (3)  utilizing data blending techniques on existing datasets to assure seamless interactions in multi-round, multi-image conversations.
Compared to existing frameworks, DeepSpeed-VisualChat shows superior scalability up to 70B parameter language model size, representing a significant advancement in multi-modal language models and setting a solid foundation for future explorations.111Code will be released soon as a part ofhttps://github.com/microsoft/DeepSpeedExample