LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture
Keywords: Efficient Multimodal Large Language Model, Transformer-Mamba Hybrid Architecture
TL;DR: The first hybrid MLLM, achieving a better balance between efficiency and effectiveness
Abstract: Expanding the long-context capabilities of Multi-modal Large Language Models (MLLMs) is crucial for video understanding, high-resolution image understanding, and multi-modal agents. This involves a series of systematic optimizations, including model architecture, data construction and training strategy, particularly addressing challenges such as \textit{degraded performance with more images} and \textit{high computational costs}. In this paper, we adapt the model architecture to a hybrid of Mamba and Transformer blocks, approach data construction with both temporal and spatial dependencies among multiple images and employ a progressive training strategy. The released model **LongLLaVA** (**Long**-Context **L**arge **L**anguage **a**nd **V**ision **A**ssistant) is the first hybrid MLLM, which achieved a better balance between efficiency and effectiveness. LongLLaVA not only achieves competitive results across various benchmarks, but also maintains high throughput and low memory consumption. Especially, it could process nearly a thousand images on a single A100 80GB GPU, showing promising application prospects for a wide range of tasks.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 10001
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