

Longva
Overview :
LongVA is a long context transformer model capable of processing over 2000 frames or 200K visual tokens. It achieves leading performance in Video-MME among 7B models. The model is tested on CUDA 11.8 and A100-SXM-80G and can be quickly deployed and used through the Hugging Face platform.
Target Users :
LongVA targets researchers and developers, particularly those in the fields of image and video processing, multi-modal learning, and natural language processing, seeking innovative solutions. LongVA is suitable for them because it provides a powerful tool to explore and implement complex vision and language tasks.
Use Cases
Researchers use the LongVA model for automatic video content description generation.
Developers utilize LongVA for developing multimodal chat applications involving images and videos.
Educational institutions adopt the LongVA model to develop auxiliary tools for visual and language teaching.
Features
Process long videos and large quantities of visual tokens, enabling zero-shot language-to-vision conversion.
Achieve outstanding performance in Video Multimodal Evaluation (Video-MME).
Support CLI (command-line interface) and gradio UI-based multimodal chat demos.
Provide quick-start code examples on the Hugging Face platform.
Support custom generation parameters, such as sampling, temperature, and top_p.
Offer evaluation scripts for V-NIAH and LMMs-Eval to test model performance.
Support long-text training and efficient training in multi-GPU environments.
How to Use
1. Install the necessary dependencies, including CUDA 11.8 and PyTorch 2.1.2.
2. Install the LongVA model and its dependencies via pip.
3. Download and load the pre-trained LongVA model.
4. Prepare input data, which can be image or video files.
5. Interact with and test the model using CLI or gradio UI.
6. Adjust generation parameters as needed to achieve optimal results.
7. Run the evaluation scripts to assess model performance on various tasks.
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