Instructions to use richardyoung/WeVisDoc-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use richardyoung/WeVisDoc-4B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use richardyoung/WeVisDoc-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "richardyoung/WeVisDoc-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "richardyoung/WeVisDoc-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

Ollama
How to use richardyoung/WeVisDoc-4B-GGUF with Ollama:
```
ollama run hf.co/richardyoung/WeVisDoc-4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use richardyoung/WeVisDoc-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "richardyoung/WeVisDoc-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use richardyoung/WeVisDoc-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/richardyoung/WeVisDoc-4B-GGUF:Q4_K_M
```

Lemonade

How to use richardyoung/WeVisDoc-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WeVisDoc-4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use richardyoung/WeVisDoc-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use richardyoung/WeVisDoc-4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf richardyoung/WeVisDoc-4B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "richardyoung/WeVisDoc-4B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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)

Quant Quality

Each quant compared with the F16 model using llama-perplexity --kl-divergence on the wikitext-2 test set (40 × 512-token chunks). KL divergence measures how far the quant's next-token probabilities drift from F16; lower is better. Top-token match is how often the quant picks the same next token as F16. As a rough guide, a KLD under 0.01 is hard to tell from F16, and from about 0.05 differences start to show in longer outputs. These numbers cover the text decoder only, on English prose. For OCR accuracy, see Verified below.

Quant	Size	KL divergence	Top-token match	Perplexity (F16: 11.47)
IQ3_M	2.1 GB	0.3594	74.0%	13.77
IQ4_XS	2.5 GB	0.1279	83.8%	12.06
Q4_K_M	2.7 GB	0.1011	86.1%	11.72
Q5_K_M	3.2 GB	0.0406	91.0%	11.61
Q6_K	3.6 GB	0.0218	93.4%	11.52
Q8_0	4.7 GB	0.0042	97.1%	11.44

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 | Muse-Glimmer-30B | Hemmingway-1
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

Downloads last month: 751

GGUF

Model size

4B params

Architecture

qwen3vl

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for richardyoung/WeVisDoc-4B-GGUF

Base model

tencent/WeVisDoc-4B

Quantized

(4)

this model

Paper for richardyoung/WeVisDoc-4B-GGUF

When Models Can't Follow: Testing Instruction Adherence Across 256 LLMs

Paper • 2510.18892 • Published Oct 18, 2025 • 1