WeVisDoc-2B

Tencent WeVisDoc-2B: document page to structured Markdown with LaTeX formulas and HTML tables. Images work.

🚀 Overview

GGUF build of tencent/WeVisDoc-2B, Tencent’s 2B WeVisDoc, an end-to-end document parser fine-tuned from Qwen3-VL that turns a page image into structured Markdown, with LaTeX formulas and HTML tables. It ships with its vision projector, so image input works in Ollama.

Prompts: send a page image and ask for Markdown (e.g. Parse this document page into Markdown.). Tencent’s own client and its exact prompt are in the WeVisDoc repository.

🎯 Key Features

• End-to-end document parsing in one model: page image in, Markdown out
• Formulas as LaTeX, tables as HTML
• English and Chinese
• Apache-2.0

🏷️ Available Versions

💻 Quick Start

ollama run richardyoung/wevisdoc-2b
# then drag an image file into the prompt, or send it in the API "images" field

🛠️ Use Cases

• Converting scanned pages and PDFs (rendered to images) into Markdown
• Formula-heavy papers (LaTeX output)
• Tables to HTML for RAG pipelines

✅ Verified

Tested through Ollama before upload (Q4_K_M and Q8_0) on a synthetic invoice with the prompt Parse this document page into Markdown.; Q4_K_M returned: ‘## INVOICE 4471 / Date: 2026-09-28 / Widgets x3 ….. \(42.00 / TOTAL: \)86.20’.

🔧 Technical Details

• Base Model: tencent/WeVisDoc-2B
• Parameters: 2.0B
• Context Length: 256K tokens
• Quantization: GGUF via llama.cpp with an importance matrix (calibration_datav3); vision projector at F16
• License: Apache-2.0 (inherited from the base model)

🙏 Acknowledgments

• Base Model: Tencent
• Quantization: llama.cpp

Built & maintained by Richard Young · DeepNeuro