---

base_model: tencent/WeVisDoc-4B

base_model_relation: quantized

license: apache-2.0

pipeline_tag: image-text-to-text

tags: [gguf, llama.cpp, ollama, imatrix, vision, multimodal, ocr, document-understanding]

---

WeVisDoc-4B (GGUF)

GGUF quantizations of tencent/WeVisDoc-4B — Tencent's 4B 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 — optimized for local inference with llama.cpp and Ollama, with its vision projector. All quants use an importance matrix.

Quick Start

With Ollama

ollama run richardyoung/wevisdoc-4b
# or straight from Hugging Face
ollama run hf.co/richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

With llama.cpp

huggingface-cli download richardyoung/WeVisDoc-4B-GGUF \
    --include "*Q4_K_M*" "mmproj*" --local-dir ./models

./llama-mtmd-cli -m ./models/WeVisDoc-4B-Q4_K_M.gguf \
    --mmproj ./models/mmproj-WeVisDoc-4B-F16.gguf \
    --image page.png -p "Parse this document page into Markdown."

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.

Why This Model?

• OmniDocBench v1.6 Overall 95.38, first among the end-to-end parsers Tencent compared
• Page image in, Markdown out: formulas as LaTeX, tables as HTML
• English and Chinese
• Apache-2.0

Use Cases

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

Available Quantizations

| File | Size | Use Case |

|---|---|---|

| WeVisDoc-4B-IQ3_M.gguf | 2.1 GB | Smallest |

| WeVisDoc-4B-IQ4_XS.gguf | 2.5 GB | Great quality-size balance |

| WeVisDoc-4B-Q4_K_M.gguf | 2.7 GB | Recommended — good balance |

| WeVisDoc-4B-Q5_K_M.gguf | 3.2 GB | Higher quality |

| WeVisDoc-4B-Q6_K.gguf | 3.6 GB | Very high quality |

| WeVisDoc-4B-Q8_0.gguf | 4.7 GB | Near-lossless |

| mmproj-WeVisDoc-4B-F16.gguf | 0.84 GB | Vision projector (required for images) |

| WeVisDoc-4B-imatrix.gguf | – | Importance matrix used for the quants (calibration_datav3) |

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'.

Also Available

• WeVisDoc-2B GGUF — smaller and faster

Other Models by richardyoung

• New Heretic models (reproducible, listed in the Heretic Grimoire): LFM2.5-1.2B | Granite-4.2-3B | Granite-4.2-8B | Olmo-3-7B-Think | Phi-4-mini | DeepSeek-R1-0528-Qwen3-8B | Agents-A1-4B | FrogNano-4B
• Abliterated/Uncensored models: Qwen2.5-7B | Qwen3-14B | DeepSeek-R1-32B | Qwen3-8B
• OCR & Vision: MiniCPM-V-4.6 GGUF | dots.mocr GGUF | Unlimited-OCR GGUF | olmOCR GGUF | LightOnOCR-3-4B GGUF | WeVisDoc-4B GGUF | Xiaomi-OCR-0 GGUF
• MLX quantizations (Apple Silicon): Kimi-K2 series | olmOCR MLX
• Healthcare/Medical: Synthea 575K patients dataset | CardioEmbed
• Research: LLM Instruction-Following Evaluation (arxiv:2510.18892)

---

Built & maintained by Richard Young · DeepNeuro