What is LongCat-Flash-Omni
LongCat-Flash-Omni is an omni-modal large language model open-sourced by the Meituan LongCat team, designed based on the efficient architecture of the LongCat-Flash series. It innovatively integrates multi-modal perception and speech reconstruction modules, with 560 billion total parameters (27 billion activated parameters), enabling low-latency real-time audio-visual interaction capabilities. The model adopts a progressive multi-modal fusion training strategy and possesses powerful text, image, audio, and video understanding and generation capabilities, achieving open-source state-of-the-art (SOTA) on omni-modal benchmarks. The model provides developers with an efficient technical option, promoting the development of multi-modal application scenarios.
Main Features of LongCat-Flash-Omni
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Multi-modal Interaction: Supports multi-modal input and output of text, speech, images, and video, enabling cross-modal understanding and generation to meet diverse interaction needs.
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Real-Time Audio-Visual Interaction: Possesses low-latency real-time audio-visual interaction capabilities, providing smooth and natural voice dialogue and video understanding experience, suitable for multi-turn dialogue scenarios.
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Long Context Processing: Supports an ultra-long context window of 128K tokens, capable of handling complex reasoning tasks and long-text interactions, suitable for multi-turn dialogue and long-term memory scenarios.
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End-to-End Interaction: End-to-end processing capability from multi-modal input to text and speech output, achieving efficient and natural interaction experience, supporting continuous audio feature processing.
Technical Principles of LongCat-Flash-Omni
- Efficient Architecture Design：
- Shortcut-Connected MoE（ScMoE）: The model adopts a Mixture-of-Experts (MoE) architecture with zero-computation experts, optimizing computing resource allocation and improving inference efficiency.
- Lightweight Codecs: The visual encoder and audio codec are both lightweight components, with approximately 600 million parameters, achieving an optimal balance between performance and inference efficiency.
- Multi-modal Fusion: The model achieves efficient processing of multi-modal input through a visual encoder and an audio encoder. Through a lightweight audio decoder, the generated speech tokens are reconstructed into natural speech waveforms.
- Progressive Multi-modal Training: Adopts a progressive multi-modal fusion training strategy, gradually incorporating text, audio, image, and video data to ensure strong omni-modal performance without degradation in single-modal performance. By balancing the data distribution across different modalities, the training process is optimized, enhancing the model's multi-modal fusion capability.
- Low-Latency Interaction: All modules are designed based on efficient streaming inference, supporting real-time audio-visual interaction. Through a chunked audio-visual feature interleaving mechanism, low-latency, high-quality audio-visual processing is achieved.
- Long Context Support: Supports a context window of 128K tokens, enhancing long context processing capability through dynamic frame sampling and hierarchical token aggregation strategies.
Project Address of LongCat-Flash-Omni
- GitHub Repository：https://github.com/meituan-longcat/LongCat-Flash-Omni
- HuggingFace Model Library：https://huggingface.co/meituan-longcat/LongCat-Flash-Omni
- Technical Paper：https://github.com/meituan-longcat/LongCat-Flash-Omni/blob/main/tech_report.pdf
How to Use LongCat-Flash-Omni
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Use via Open-Source Platforms: Visit Hugging Face or GitHub to directly load the model for testing or download the code for local deployment.
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Use via the Official Experience Platform: Log in to theLongCat official website to experience image and file upload and voice call features.
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Use via the Official App: Download the official LongCat App, and once opened, you can use the online search and voice call features.
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Local deployment: According to the GitHub documentation, download the model code, configure the local environment, and prepare hardware resources (such as a GPU) to run the model.
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Integrate into existing systems: Call the LongCat-Flash-Omni API or integrate it into code to extend multimodal interaction capabilities.
Application scenarios of LongCat-Flash-Omni
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Intelligent customer service: Through text, voice, and image interaction, provide 24/7 intelligent customer service, answer questions in real time, and improve user experience.
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Video content creation: Automatically generate video scripts, subtitles, and content to improve creative efficiency.
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Intelligent education: Provide personalized learning content, support voice explanations, image displays, and text interaction to meet diverse teaching needs.
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Intelligent office: Support voice meeting notes, document generation, and image recognition to improve office efficiency and collaboration capabilities.
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Intelligent driving: Analyze road conditions in real time through image and video understanding, and provide driving assistance.