[Benchmark] Support MATH-Vision

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Measuring Multimodal Mathematical Reasoning with the MATH-Vision🔥 Dataset

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👀 Introduction

Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista. However, we observe significant limitations in the diversity of questions and breadth of subjects covered by these benchmarks. To address this issue, we present the MATH-Vision (MATH-V) dataset, a meticulously curated collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. Spanning 16 distinct mathematical disciplines and graded across 5 levels of difficulty, our dataset provides a comprehensive and diverse set of challenges for evaluating the mathematical reasoning abilities of LMMs.

📈 Evaluation

# MATH-Vision
torchrun --nproc-per-node=1  run.py --data MATH_V --model your_model --verbose

# MATH-Vision tesimini
torchrun --nproc-per-node=1  run.py --data MATH_V_MINI --model your_model --verbose