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MAVIS

Mathematical Visual Instruction Tuning Model

CommonProductProductivityMachine LearningMultimodal Learning

MAVIS is a mathematical visual instruction tuning model designed for multimodal large language models (MLLMs). It enhances MLLMs' capabilities in visual mathematical problem-solving by improving visual encoding of mathematical graphs, graph-language alignment, and mathematical reasoning skills. The model includes two newly curated datasets, a mathematical visual encoder, and a mathematical MLLM, achieving leading performance in the MathVerse benchmark test through a three-phase training paradigm.

MAVIS

MAVIS-Caption: Contains 588K high-quality graph-title pairs covering geometry and functions.
MAVIS-Instruct: Contains 834K instruction tuning data
utilizing a text lightweight version for rationale.
Math-CLIP: A view encoder designed specifically for understanding mathematical graphs in MLLMs.
MAVIS-7B: An MLLM that achieved leading performance in the MathVerse benchmark test through a three-phase training paradigm.

\The MAVIS model is primarily aimed at researchers and developers in the fields of machine learning and artificial intelligence
especially those specialists focusing on mathematical problem-solving and multimodal learning models. It is suitable for researchers in need of enhancing their mathematical visual problem-solving capabilities and developers who wish to leverage advanced machine learning techniques to enhance educational tools.\

Researchers use the MAVIS model to enhance their visual recognition and solving capabilities for mathematical problems.
Educational software developers utilize MAVIS to enhance the interactivity and teaching effectiveness of mathematics education applications.
Data scientists use MAVIS for in-depth analysis and visualization representation of mathematical graphs.

1. Visit the MAVIS GitHub page to access the model and related datasets.
2. Download and install the necessary dependencies and tools to ensure the model runs correctly.
3. Read the MAVIS documentation and usage instructions to understand the model's working principles and how to configure it.
4. Use the MAVIS-Caption or MAVIS-Instruct datasets for model training or tuning.
5. Utilize the Math-CLIP view encoder to enhance the model's understanding of mathematical graphs.

MAVIS Visit Over Time

Monthly Visits

515580771

Bounce Rate

37.20%

Page per Visit

5.8

Visit Duration

00:06:42

MAVIS Visit Trend

MAVIS Visit Geography

MAVIS Traffic Sources

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