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LAVIS is a multimodal large language model framework and vision-language model library. It provides tools for training and evaluating models that integrate visual, textual, and audio data, serving as a cross-modal feature extractor and a zero-shot visual reasoning engine. The framework distinguishes itself by using frozen-backbone integration, where pretrained encoders remain non-trainable while lightweight adapter layers are updated. It employs cross-modal feature alignment to map different representations into a shared embedding space and utilizes a modular model wrapper to swap vision and

Qwen3-Omni is an omni-modal large language model designed to process and generate text, audio, images, and video within a single unified neural architecture. It functions as a real-time voice assistant and multimodal AI agent capable of reasoning across different media types and executing external tool-calling functions via APIs. The system supports low-latency conversational AI through autoregressive token streaming and natural turn-taking. It enables multilingual speech translation and generation across dozens of languages, featuring customizable speaker profiles and tones. The model's cap