Instructions to use richardyoung/dots.mocr-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/dots.mocr-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/dots.mocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf richardyoung/dots.mocr-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/dots.mocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf richardyoung/dots.mocr-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/dots.mocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf richardyoung/dots.mocr-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/dots.mocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf richardyoung/dots.mocr-GGUF:Q4_K_M

Use Docker

docker model run hf.co/richardyoung/dots.mocr-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use richardyoung/dots.mocr-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "richardyoung/dots.mocr-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/dots.mocr-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/dots.mocr-GGUF:Q4_K_M

Ollama
How to use richardyoung/dots.mocr-GGUF with Ollama:
```
ollama run hf.co/richardyoung/dots.mocr-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use richardyoung/dots.mocr-GGUF with Docker Model Runner:
```
docker model run hf.co/richardyoung/dots.mocr-GGUF:Q4_K_M
```

Lemonade

How to use richardyoung/dots.mocr-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull richardyoung/dots.mocr-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.dots.mocr-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

dots.mocr-GGUF

GGUF quantizations of dots-studio/dots.mocr with its vision projector, for llama.cpp and Ollama.

File	What
`dots.mocr-Q4_K_M.gguf`	Language model, Q4_K_M (recommended)
`dots.mocr-Q8_0.gguf`	Language model, Q8_0
`mmproj-dots.mocr-F16.gguf`	Vision projector (f16), required for images

Ollama

ollama run richardyoung/dots.mocr

Tags: Q4_K_M (default, latest), Q8_0. Image input works in the CLI (drag in a file path) and via the API images field.

llama.cpp

llama-mtmd-cli -m dots.mocr-Q4_K_M.gguf --mmproj mmproj-dots.mocr-F16.gguf --image page.png -p "Transcribe all the text in this image."

Verified

Tested before upload through Ollama with a synthetic invoice and a shapes image (Q4_K_M shown):

Invoice transcription: 'INVOICE 4471 / Date: 2026-09-28 / Widgets x3 ....... $42.00 / TOTAL: $86.20'
Shapes: 'The image contains two shapes: a red circle and a blue square. The red circle is on the left side, and the blue square is on the right side. Both shapes are set'

Converted with llama.cpp convert_hf_to_gguf.py (text and --mmproj), quantized with llama-quantize.

Downloads last month: 214

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

Model tree for richardyoung/dots.mocr-GGUF

Base model

dots-studio/dots.mocr

Quantized

(19)

this model