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Multimodal large language models (MLLMs) have gained significant attentiondue to their strong multimodal understanding capability. However, existingworks rely heavily on modality-specific encoders, which usually differ inarchitecture and are limited to common modalities. In this paper, we presentOneLLM, an MLLM that aligns eight modalities to language using a unifiedframework. We achieve this through a unified multimodal encoder and aprogressive multimodal alignment pipeline. In detail, we first train an imageprojection module to connect a vision encoder with LLM. Then, we build auniversal projection module (UPM) by mixing multiple image projection modulesand dynamic routing. Finally, we progressively align more modalities to LLMwith the UPM. To fully leverage the potential of OneLLM in followinginstructions, we also curated a comprehensive multimodal instruction dataset,including 2M items from image, audio, video, point cloud, depth/normal map, IMUand fMRI brain activity. OneLLM is evaluated on 25 diverse benchmarks,encompassing tasks such as multimodal captioning, question answering andreasoning, where it delivers excellent performance. Code, data, model andonline demo are available at https://github.com/csuhan/OneLLM

OneLLM: One Framework to Align All Modalities with Language