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 causal 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.

翻译：现有的大多数多模态模型由于无法灵活处理多轮多图像对话中的交织图像与文本输入，在训练资源分配和数据可获取性方面面临严重制约，从而影响了其在多样化交互场景中的适应性和可扩展性。为解决这一问题，我们提出了DeepSpeed-VisualChat框架，该框架通过整合多模态能力来优化大规模语言模型，重点提升大型视觉与语言模型在处理交织输入时的表现。本框架的主要特点包括：（1）开源支持多轮多图像对话；（2）引入创新的多模态因果注意力机制；（3）利用现有数据集的数据混合技术，确保多轮多图像对话中的无缝交互。与现有框架相比，DeepSpeed-VisualChat在高达700亿参数的语言模型规模上展现出卓越的可扩展性，标志着多模态语言模型的重大进步，并为未来探索奠定了坚实基础。