LLaVA-Video-7B-Qwen2
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| Model Type | Video-Text-to-Text |
| Architecture | SO400M + Qwen2 |
| License | Apache 2.0 |
| Paper | View Paper |
What is LLaVA-Video-7B-Qwen2?
LLaVA-Video-7B-Qwen2 is a sophisticated multimodal model designed for video understanding and interaction. Built on the Qwen2 language model with a 32K token context window, it represents a significant advancement in video-language AI systems. The model can process up to 64 frames and has been trained on a comprehensive dataset combining LLaVA-Video-178K and LLaVA-OneVision Dataset.
Implementation Details
The model utilizes a BF16 precision format and was trained using 256 Nvidia Tesla A100 GPUs. It leverages the Huggingface Trainer framework and PyTorch for neural network operations. The training process involved a mixture of 1.6M single-image/multi-image/video data over one epoch.
- Supports both English and Chinese language processing
- Achieves impressive accuracy scores across multiple benchmarks (NextQA: 83.2%, MLVU: 70.8%)
- Implements advanced video frame sampling and processing techniques
Core Capabilities
- Video understanding and detailed description generation
- Multi-frame processing (up to 64 frames)
- Cross-modal interaction between video and text
- High performance on various video-text benchmarks
- Support for both single-image and multi-image processing
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its ability to process long video sequences with up to 64 frames and its strong performance across multiple video understanding benchmarks. It's built on the advanced Qwen2 architecture and trained on a diverse dataset of both image and video content.
Q: What are the recommended use cases?
The model is ideal for video description generation, video-based question answering, and general video understanding tasks. It can be particularly useful in applications requiring detailed video analysis, content description, and multimodal interaction.
