LongVU_Qwen2_7B
| Property | Value |
|---|---|
| Parameter Count | 7.67B |
| Model Type | Video-Text-to-Text |
| Architecture | Qwen2-based with Spatiotemporal Adaptive Compression |
| Paper | LongVU Paper |
| Tensor Type | BF16 |
What is LongVU_Qwen2_7B?
LongVU_Qwen2_7B is an advanced video-language understanding model that implements spatiotemporal adaptive compression for processing long-form video content. Built on the Qwen2-7B architecture, it demonstrates impressive performance across multiple video understanding benchmarks, including EgoSchema (67.6% accuracy), MLVU (65.4% accuracy), and MVBench (66.9% accuracy).
Implementation Details
The model utilizes a sophisticated architecture designed for efficient video processing and understanding. It's implemented using PyTorch and supports BF16 precision for optimal performance.
- Built on the Qwen2-7B base model architecture
- Incorporates spatiotemporal adaptive compression techniques
- Supports video frame processing with customizable FPS
- Includes comprehensive video tokenization and processing capabilities
Core Capabilities
- Long-form video understanding and analysis
- Detailed video description generation
- Multi-benchmark performance with >60% accuracy across major evaluation datasets
- Efficient processing of video frames with adaptive compression
Frequently Asked Questions
Q: What makes this model unique?
LongVU stands out for its spatiotemporal adaptive compression capability, which allows it to efficiently process and understand long-form video content while maintaining high accuracy across various video understanding tasks.
Q: What are the recommended use cases?
The model is particularly well-suited for video description generation, video content analysis, and general video understanding tasks. It's especially effective for applications requiring detailed comprehension of long-form video content.
