Step3's multimodal generation capability is characterized by three main dimensions:
- Cross-modal content understanding:Ability to process images, text and voice input simultaneously, e.g. analyze images to generate descriptive text or create content in conjunction with voice commands
- Composite output generation:Generate convergent content based on multimodal inputs, e.g., new picture descriptions based on textual cues and reference images
- Application Scenario Expansion:Supports composite tasks such as intelligent customer service (speech + text), educational assistance (image + text interpretation), video content analysis (frame sequence + subtitle generation), etc.
In terms of technical implementation, the input data of different modalities are processed uniformly by AutoProcessor, and the MoE architecture inside the model can dynamically allocate computational resources to process various types of data, which is the key to its efficient multimodal processing.
This answer comes from the articleStep3: Efficient generation of open source big models for multimodal contentThe
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