LongCat-Flash-Omni - Meituan's open-source real-time interactive full-modal large model

What is LongCat-Flash-Omni?

LongCat-Flash-Omni is an open-source, multimodal language model from Meituan's LongCat team. Based on the efficient architecture of the LongCat-Flash series, it innovatively integrates multimodal perception and speech reconstruction modules, boasting 560 billion total parameters (27 billion activation parameters) to achieve low-latency real-time audio and video interaction capabilities. The model employs a progressive multimodal fusion training strategy, possessing powerful text, image, audio, and video understanding and generation capabilities, achieving state-of-the-art (SOTA) performance in full-modal benchmark tests. The model provides developers with efficient technology options, driving the development of multimodal application scenarios.

Main functions of LongCat-Flash-Omni

• Multimodal interactionIt supports multimodal input and output of text, voice, images and video, enabling cross-modal understanding and generation to meet diverse interaction needs.
• Real-time audio and video interactionIt features low-latency real-time audio and video interaction capabilities, providing a smooth and natural voice dialogue and video understanding experience, suitable for multi-turn dialogue scenarios.
• Long context processingIt supports ultra-long context windows of up to 128K tokens, can handle complex reasoning tasks and long text interactions, and is suitable for multi-turn dialogues and long-term memory scenarios.
• End-to-end interactionEnd-to-end processing capabilities from multimodal input to text and speech output enable efficient and natural interactive experiences, and support continuous audio feature processing.

The technical principles of LongCat-Flash-Omni

• High-efficiency architecture design:
  • Shortcut-Connected MoE (ScMoE)The model adopts a hybrid expert (MoE) architecture with zero computational experts, which optimizes the allocation of computing resources and improves inference efficiency.
  • Lightweight codecBoth the visual encoder and audio codec are lightweight components with approximately 600 million parameters, achieving an optimal balance between performance and inference efficiency.
• Multimodal fusionThe model achieves efficient processing of multimodal inputs through a visual encoder and an audio encoder. A lightweight audio decoder reconstructs the generated speech tokens into natural speech waveforms.
• Progressive multimodal trainingA progressive multimodal fusion training strategy is adopted, gradually incorporating text, audio, image, and video data to ensure strong full-modal performance without single-modal performance degradation. By balancing the data distribution of different modalities, the training process is optimized to improve the model's multimodal fusion capability.
• Low-latency interactionAll modules are designed based on efficient streaming inference, supporting real-time audio and video interaction. Low-latency, high-quality audio and video processing is achieved through a segmented audio and video feature interleaving mechanism.
• Long context supportIt supports context windows of up to 128K tokens and improves long context processing capabilities through dynamic frame sampling and hierarchical token aggregation strategies.

LongCat-Flash-Omni project address

• GitHub repositoryhttps://github.com/meituan-longcat/LongCat-Flash-Omni
• HuggingFace model libraryhttps://huggingface.co/meituan-longcat/LongCat-Flash-Omni
• Technical Papers: https://github.com/meituan-longcat/LongCat-Flash-Omni/blob/main/tech_report.pdf

How to use LongCat-Flash-Omni

• Use through open source platformsVisit Hugging Face or GitHub to directly load the model for testing or download the code for local deployment.
• Use through the official experience platformLog in to the LongCat official website to experience the image and file upload and voice call functions.
• Use via the official appDownload the official LongCat App and open it to use the online search and voice call functions.
• Local deployment and useAccording to the GitHub documentation, download the model code, configure the local environment, and prepare hardware resources (such as GPU) to run the model.
• Integrate into existing systemsExtend multimodal interaction functionality by calling the LongCat-Flash-Omni API or integrating it into the code.

Application scenarios of LongCat-Flash-Omni

• Intelligent Customer ServiceIt provides 24/7 intelligent customer service through text, voice, and image interaction, answering questions in real time and improving user experience.
• Video content creationAutomatically generates video scripts, subtitles, and content, improving creation efficiency.
• Smart EducationIt provides personalized learning content, supports voice explanations, image displays, and text interactions, and meets diverse teaching needs.
• Smart OfficeIt supports voice conference recording, document generation, and image recognition, improving office efficiency and collaboration capabilities.
• Intelligent drivingIt provides driving assistance by understanding and analyzing road conditions in real time through images and videos.