Several effective ways to optimize the performance of Qwen2.5-VL:
- Flash Attention 2:Installing and enabling Flash Attention 2 significantly speeds up the inference process
pip install -U flash-attn -no-build-isolation
python web_demo_mm.py -flash-attn2 - Resolution Adjustment:Control the size range of the processed images (e.g. 256-1280) by setting min_pixels and max_pixels to strike a balance between speed and memory usage
- Model quantification:4-bit or 8-bit quantization can be used to reduce memory consumption for models with large number of parameters.
- Batch optimization:Improve GPU utilization by using batch processing for a large number of similar tasks
- Hardware Options:Reasonable configuration of hardware according to the size of the model, such as 7B model recommended 16GB video memory
Video processing is specially optimized:
- Accelerating video frame extraction with the decord library
- Adjust the sampling rate of keyframes to increase the sampling rate for clips with large changes in movement
- Enable dynamic frame rate mode to allow the model to automatically adapt to video content complexity
System-level recommendations:
- Using the latest version of CUDA and cuDNN
- Ensure sufficient memory swap space
- For large models, consider using model parallelism techniques
This answer comes from the articleQwen2.5-VL: an open source multimodal grand model supporting image-video document parsingThe
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