Ola-7b
| Property | Value |
|---|---|
| Model Type | Multi-modal Language Model |
| Architecture | Pre-trained Oryx-ViT + Qwen2.5-7B |
| Training Hardware | 64 NVIDIA Tesla A100 GPUs |
| Paper | arXiv:2502.04328 |
| Languages | English, Chinese |
What is Ola-7b?
Ola-7b is an advanced multi-modal language model developed through collaboration between Tencent, Tsinghua University, and Nanyang Technological University. Built upon the Qwen2.5 architecture, it represents a significant advancement in unified multi-modal processing, capable of handling text, images, video, and audio inputs with a generous 32K token context window.
Implementation Details
The model utilizes a sophisticated architecture combining Pre-trained Oryx-ViT with Qwen2.5-7B as its foundation. It's trained on over 5M multi-modal data points across three distinct stages, operating at BFloat16 precision. A key technical innovation is its ability to process visual inputs with arbitrary spatial sizes and temporal lengths using an on-demand approach.
- Flexible visual processing capability for varying input dimensions
- Integrated speech processing with mel spectrogram conversion
- Advanced video frame sampling and processing
- Seamless handling of multi-modal inputs including audio extraction from videos
Core Capabilities
- Multi-modal conversation with support for images, videos, and audio
- Dynamic resolution handling for visual inputs
- Efficient processing of long-form content with 32K context window
- Bilingual support for English and Chinese
- Real-time audio processing and integration
Frequently Asked Questions
Q: What makes this model unique?
Ola-7b stands out for its ability to handle multiple modalities simultaneously while maintaining flexibility in input dimensions. Its on-demand processing approach for visual inputs and integrated audio capabilities make it particularly versatile for real-world applications.
Q: What are the recommended use cases?
The model is well-suited for applications requiring multi-modal understanding, such as content analysis, video description, audio-visual question answering, and general-purpose AI assistance requiring understanding of multiple input types.
