Llava-v1.5-7b-m3 Open-source Multimodal Model - Freely Control Visual Granularity and Measure Image Complexity

Llava V1.5 7b M3

Developed by mucai

M3 is a multimodal model that allows explicit control of visual granularity at runtime and can serve as a metric for image/dataset complexity. It is fine-tuned from LLaMA/Vicuna.

Text-to-Image

Transformers

Open Source License:Apache-2.0 #Dynamic Visual Granularity Control #Multimodal Chatbot #Visual Token Efficiency Optimization

Downloads 33

Release Time : 5/28/2024

Model Overview

The Matryoshka Multimodal Model (M3) is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on visual dialogue data. It supports dynamic adjustment of the number of visual tokens and can be used as a tool for evaluating image complexity.

Model Features

Dynamic Visual Granularity Control

Allows explicit control of the number of visual tokens per sample at runtime

Complexity Measurement Standard

The model itself can serve as a metric for image/dataset complexity

Efficient Visual Processing

Maintains strong performance even with only 1 or 9 visual tokens per image

Model Capabilities

Multimodal Dialogue

Image Caption Generation

Visual Question Answering

Image Complexity Evaluation

Use Cases

Research Applications

Multimodal Model Research

Used to study the behavior and performance of large multimodal models

Visual Representation Learning

Investigates the effects of representation learning under different visual granularities

Educational Applications

AI Educational Tool

Serves as a teaching tool to demonstrate how multimodal models work

🚀 Matryoshka Multimodal Models (M3) Model Card

Matryoshka Multimodal Models (M3) offer a novel approach to multimodal processing, enabling explicit control of visual granularities and serving as a metric for image/dataset complexity. It's an open - source chatbot based on fine - tuned LLaMA/Vicuna, suitable for research on large multimodal models and chatbots.

🚀 Quick Start

This section provides an overview of the Matryoshka Multimodal Models (M3) model, including its details, license, intended use, training dataset, and evaluation results.

✨ Features

Allows explicit control of visual granularities.
Serves as a metric for image/dataset complexity.
An open - source chatbot trained by fine - tuning LLaMA/Vicuna on visual conversation data.
Based on the transformer architecture.

📚 Documentation

Model details

Property	Details
Model Type	Matryoshka Multimodal Models (M3) allow users to explicitly control visual granularities (the number of visual tokens per sample) at the same time. Also, the model itself serves as a metric for image/dataset complexity. M3s is an open - source chatbot trained by fine - tuning LLaMA/Vicuna on visual conversation data. It is an auto - regressive language model, based on the transformer architecture.
Model Date	llava - v1.5 - 7b - m3 was trained in May 2024. Paper
Paper or resources for more information	https://matryoshka-mm.github.io/

License

Where to send questions or comments about the model: [https://github.com/mu - cai/matryoshka - mm/issues](https://github.com/mu - cai/matryoshka - mm/issues)

Intended use

Primary intended uses: The primary use of M3 is research on large multimodal models and chatbots.

Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

Training dataset

558K filtered image - text pairs from LAION/CC/SBU, captioned by BLIP.
665K image level instruction data from LLaVA - 1.5.

Evaluation dataset

Matryoshka Multimodal Models (M3) achieves strong performance even using 1 or 9 visual tokens per image.

📄 License

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