Quantization made by Richard Erkhov.
This repository contains a quantized version of the model presented in Visually Descriptive Language Model for Vector Graphics Reasoning.
Project page: https://mikewangwzhl.github.io/VDLM/ Code: https://github.com/MikeWangWZHL/VDLM
PVD-160k-Mistral-7b - GGUF
- Model creator: https://huggingface.co/mikewang/
- Original model: https://huggingface.co/mikewang/PVD-160k-Mistral-7b/
| Name | Quant method | Size |
|---|---|---|
| PVD-160k-Mistral-7b.Q2_K.gguf | Q2_K | 2.53GB |
| PVD-160k-Mistral-7b.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| PVD-160k-Mistral-7b.IQ3_S.gguf | IQ3_S | 2.96GB |
| PVD-160k-Mistral-7b.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| PVD-160k-Mistral-7b.IQ3_M.gguf | IQ3_M | 3.06GB |
| PVD-160k-Mistral-7b.Q3_K.gguf | Q3_K | 3.28GB |
| PVD-160k-Mistral-7b.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| PVD-160k-Mistral-7b.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| PVD-160k-Mistral-7b.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| PVD-160k-Mistral-7b.Q4_0.gguf | Q4_0 | 3.83GB |
| PVD-160k-Mistral-7b.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| PVD-160k-Mistral-7b.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| PVD-160k-Mistral-7b.Q4_K.gguf | Q4_K | 4.07GB |
| PVD-160k-Mistral-7b.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| PVD-160k-Mistral-7b.Q4_1.gguf | Q4_1 | 4.24GB |
| PVD-160k-Mistral-7b.Q5_0.gguf | Q5_0 | 4.65GB |
| PVD-160k-Mistral-7b.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| PVD-160k-Mistral-7b.Q5_K.gguf | Q5_K | 4.78GB |
| PVD-160k-Mistral-7b.Q5_K_M.gguf | Q5_K_M | 1.7GB |
| PVD-160k-Mistral-7b.Q5_1.gguf | Q5_1 | 5.07GB |
| PVD-160k-Mistral-7b.Q6_K.gguf | Q6_K | 5.53GB |
| PVD-160k-Mistral-7b.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
license: apache-2.0 datasets: - mikewang/PVD-160K
Text-Based Reasoning About Vector Graphics
🌐 Homepage • 📃 Paper • 🤗 Data (PVD-160k) • 🤗 Model (PVD-160k-Mistral-7b) • 💻 Code
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.
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