mikewang_-_PVD-160k-Mistral-7b-8bits

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Project page: https://mikewangwzhl.github.io/VDLM/

Code: https://github.com/MikeWangWZHL/VDLM

PVD-160k-Mistral-7b - bnb 8bits

• Model creator: https://huggingface.co/mikewang/
• Original model: https://huggingface.co/mikewang/PVD-160k-Mistral-7b/

Original model description:

license: apache-2.0 datasets:

• mikewang/PVD-160K

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<h1 align="center"> Text-Based Reasoning About Vector Graphics </h1> <p align="center"> <a href="https://mikewangwzhl.github.io/VDLM">🌐 Homepage</a> • <a href="https://arxiv.org/abs/2404.06479">📃 Paper</a> • <a href="https://huggingface.co/datasets/mikewang/PVD-160K" >🤗 Data (PVD-160k)</a> • <a href="https://huggingface.co/mikewang/PVD-160k-Mistral-7b" >🤗 Model (PVD-160k-Mistral-7b)</a> • <a href="https://github.com/MikeWangWZHL/VDLM" >💻 Code</a> </p>

We observe that current large multimodal models (LMMs) still struggle with seemingly straightforward reasoning tasks that require precise perception of low-level visual details, such as identifying spatial relations or solving simple mazes. In particular, this failure mode persists in question-answering tasks about vector graphics—images composed purely of 2D objects and shapes.

To solve this challenge, we propose Visually Descriptive Language Model (VDLM), a visual reasoning framework that operates with intermediate text-based visual descriptions—SVG representations and learned Primal Visual Description, which can be directly integrated into existing LLMs and LMMs. We demonstrate that VDLM outperforms state-of-the-art large multimodal models, such as GPT-4V, across various multimodal reasoning tasks involving vector graphics. See our paper for more details.