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Mmmamba Linear

Developed by hustvl
mmMamba-linear is the first pure decoder multimodal state space model to achieve quadratic-to-linear distillation with moderate academic computing resources, featuring efficient multimodal processing capabilities.
Downloads 16
Release Time : 2/14/2025

Model Overview

mmMamba-linear is an innovative multimodal state space model that achieves a transition from quadratic to linear complexity through a unique distillation strategy while maintaining robust multimodal understanding capabilities.

Model Features

Linear Complexity Distillation
Transfers knowledge from quadratic complexity models to linear complexity models through an innovative three-stage distillation scheme
Efficient Multimodal Processing
Directly processes multimodal inputs without relying on independent visual encoders
Hybrid Architecture Flexibility
Supports strategic combinations of Transformer and Mamba layers to balance computational efficiency and performance
Long Context Processing Advantage
Significantly improves efficiency in long-context scenarios of 103K tokens compared to traditional models

Model Capabilities

Image understanding
Text generation
Multimodal dialogue
Long context processing

Use Cases

Intelligent Assistant
Image Caption Generation
Generates detailed descriptions based on input images
Produces accurate and contextually appropriate image captions
Multimodal Q&A
Answers complex questions about image content
Provides accurate and contextually relevant answers
Content Analysis
Long Document Analysis
Processes and analyzes documents containing large amounts of text and images
Efficiently extracts key information and generates summaries
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