Baichuan-Omni-1.5 - Baichuan Intelligence's open-source full-modal understanding model

What is Baichuan-Omni-1.5?

Baichuan-Omni-1.5 is an open-source, multimodal model from Baichuan Intelligence. It supports multimodal understanding of text, images, audio, and video, and possesses bimodal generation capabilities for both text and audio. The model excels in vision, speech, and multimodal streaming processing, showing significant advantages, particularly in the multimodal medical field. It employs an end-to-end audio solution, supporting multilingual dialogue and real-time audio-video interaction. The training data is massive, containing 340 million high-quality image/video-text data points and nearly 1 million hours of audio data. In the SFT stage, performance was further optimized using 17 million multimodal data points. Baichuan-Omni-1.5 surpasses GPT-4o-mini in several capabilities, demonstrating powerful multimodal inference and cross-modal transfer capabilities.

Main functions of Baichuan-Omni-1.5

• Full Modal Understanding and GenerationIt supports full-modal understanding of text, images, audio, and video, and has the ability to generate bimodal text and audio.
• Multimodal interactionIt supports diverse interactions at both input and output ends, enabling real-time audio and video interaction and providing a smooth and natural user experience.
• audio technologyIt adopts an end-to-end solution, supporting multilingual dialogue, end-to-end audio synthesis, automatic speech recognition (ASR), and text-to-speech (TTS) functions.
• Video UnderstandingThrough optimization of the encoder, training data, and training methods, the video understanding capability significantly surpasses that of GPT-4o-mini.
• Multimodal reasoning and transferIt possesses powerful multimodal reasoning and cross-modal transfer capabilities, enabling it to flexibly handle various complex scenarios.
• Advantages in the medical fieldIt performs exceptionally well in the field of multimodal medical applications, with a significantly leading score in medical image evaluation.

Technical Principles of Baichuan-Omni-1.5

• Multimodal architectureBaichuan-Omni-1.5 employs a multimodal architecture, supporting input and output of various modalities such as text, image, audio, and video. The model processes image and video data through a visual encoder, audio data through an audio encoder, and integrates and processes this information through a large language model (LLM). The input part supports various modalities being fed into the large language model through corresponding encoders/tokenizers, while the output part uses a text-audio interleaved output design.
• Multi-stage trainingThe model training process consists of multiple stages, including multimodal alignment pre-training for image-language, video-language, and audio-language, as well as multimodal supervised fine-tuning. During pre-training, effective interaction between different modalities is achieved through meticulous alignment of encoders and connectors for each modality. In the SFT stage, 17 million full-modal datasets were used for training, further improving the model's accuracy and robustness.
• Data Construction and OptimizationBaichuan-Omni-1.5 built a massive database containing 340 million high-quality image/video-text data points and nearly 1 million hours of audio data. During training, by optimizing the encoder, training data, and training methods, the model significantly outperformed GPT-4o-mini in tasks such as video understanding.
• Attention mechanismThe model uses an attention mechanism to dynamically calculate the weights for multimodal inputs, enabling it to better understand and respond to complex instructions. This allows the model to allocate computational resources more efficiently when processing multimodal data, improving overall performance.
• Audio and video processingIn audio processing, Baichuan-Omni-1.5 employs an end-to-end solution, supporting multilingual dialogue, end-to-end audio synthesis, automatic speech recognition (ASR), and text-to-speech (TTS) functions. The audio tokenizer is incrementally trained from the open-source speech recognition and translation model Whisper, possessing advanced semantic extraction and high-fidelity audio reconstruction capabilities. Regarding video understanding, through encoder optimization, the model outperforms GPT-4V on video understanding tasks.

Baichuan-Omni-1.5 project address

• GitHub repository:https://github.com/baichuan-inc/Baichuan-Omni-1.5
• HuggingFace model library:https://huggingface.co/baichuan-inc/Baichuan-Omni-1d5

Application scenarios of Baichuan-Omni-1.5

• Intelligent Interaction and Customer Service OptimizationBaichuan-Omni-1.5 can integrate multimodal data such as text, images, and audio, bringing a revolution to intelligent customer service. Users can send product images, text descriptions, or ask questions directly by voice. The model can accurately analyze the data and provide accurate answers instantly, significantly improving service efficiency and quality.
• Educational innovation to support learningThe model can serve as an intelligent learning companion for students, supporting the understanding and analysis of various learning materials such as text textbooks, images and charts, and audio explanations. It can provide in-depth yet easy-to-understand answers and explanations, analyze key knowledge points, adapt to different learning styles through multimodal interaction, and stimulate learning potential.
• Medical Intelligent Diagnostic AssistantIn the medical field, Baichuan-Omni-1.5 can receive patients' examination reports (text), medical images (images), and verbal symptoms (audio), and provide diagnostic ideas and treatment suggestions after comprehensive analysis to assist doctors in decision-making.
• Creative inspiration and design empowermentBaichuan-Omni-1.5 provides inspiration support for creative professionals. In fields such as advertising design and story creation, it can generate unique creative content based on creative themes (text) and image materials, expand plots based on voice descriptions, or create related images, thus helping to foster creative output.
• Multimodal content generation and understandingThe model supports full-modal input including text, images, audio, and video, and can generate high-quality text and speech output. It performs exceptionally well in video understanding and audio processing, and its audio tokenizer supports high-quality real-time bilingual (Chinese and English) dialogue.