LongVA

Long Contextual Transformer Model from Language to Vision

CommonProductImageLong ContextVisual Model

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.

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.

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.

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.

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

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