Instructions to use dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF",
	filename="MonkeyOCR-pro-1.2B-Recognition.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF
# Run inference directly in the terminal:
llama-cli -hf dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF
# Run inference directly in the terminal:
llama-cli -hf dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF

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 dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF
# Run inference directly in the terminal:
./llama-cli -hf dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF

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 dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF

Use Docker

docker model run hf.co/dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF

LM Studio
Jan

vLLM

How to use dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dinhquangson/MonkeyOCR-pro-1.2B-Vision-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": "dinhquangson/MonkeyOCR-pro-1.2B-Vision-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/dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF

Ollama
How to use dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF with Ollama:
```
ollama run hf.co/dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF
```

Unsloth Studio new

How to use dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF to start chatting

Pi new

How to use dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF

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 dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF

Run Hermes

hermes

Docker Model Runner
How to use dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF with Docker Model Runner:
```
docker model run hf.co/dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF
```

Lemonade

How to use dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dinhquangson/MonkeyOCR-pro-1.2B-Vision-GGUF

Run and chat with the model

lemonade run user.MonkeyOCR-pro-1.2B-Vision-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

MonkeyOCR-pro-1.2B Vision GGUF

A high-performance vision-language model specialized for Optical Character Recognition (OCR) and document analysis. This repository contains GGUF format models optimized for both general use and LM Studio compatibility.

🎯 Model Capabilities

✅ Vision-Language Processing: Understand and process images with text
✅ OCR (Optical Character Recognition): Extract text from images and documents
✅ Document Structure Analysis: Analyze layout, tables, and formatting
✅ Multi-turn Conversations: Chat about images and documents
✅ Multiple Languages: Support for English, Chinese, and more

📁 Files Included

Standard GGUF Models

MonkeyOCR-pro-1.2B-Recognition.gguf (1.26 GB) - Complete vision-enabled model
MonkeyOCR-pro-1.2B.gguf (1.26 GB) - Alternative version

LM Studio Optimized

MonkeyOCR-pro-1.2B-Text-LMStudio.gguf (1.23 GB) - Text model for LM Studio
mmproj-MonkeyOCR-pro-1.2B-Vision-LMStudio.gguf (1.25 GB) - Vision projection for LM Studio

🎨 LM Studio Setup (Recommended)

Step 1: Download Files

Download both LM Studio files:

MonkeyOCR-pro-1.2B-Text-LMStudio.gguf
mmproj-MonkeyOCR-pro-1.2B-Vision-LMStudio.gguf

Step 2: Install in LM Studio

Copy both files to your LM Studio models directory
Keep them in the same folder
Open LM Studio and load the text model: MonkeyOCR-pro-1.2B-Text-LMStudio.gguf
LM Studio will automatically detect and load the vision projection

Step 3: Enable Vision

Start a new chat session
Look for the camera/image icon in the chat interface
Upload an image and start asking questions!

🔧 Usage Examples

OCR Tasks

User: [uploads image] Extract all text from this document
Assistant: I can see text in the image. Here's what I extracted:
[Provides accurate OCR results]

Document Analysis

User: [uploads form] Analyze the structure of this form
Assistant: This appears to be a [form type] with the following structure:
- Header section with title
- Main content area with fields for...
- Footer with signature area

Vision Q&A

User: [uploads chart] What does this chart show?
Assistant: This chart displays [detailed analysis of the visual content]

⚙️ Technical Specifications

Architecture: Qwen2.5-VL (qwen2vl)
Model Size: 1.2B parameters
Quantization: Q8_0 (8-bit)
Context Length: 8,196 tokens
Vision Encoder: CLIP-based projection
Input Support: Images, text, multimodal conversations

🔧 Configuration Details

The model includes proper vision token configuration:

Image Token ID: 151655 (<|image_pad|>)
Video Token ID: 151656 (<|video_pad|>)
Vision Start: 151652 (<|vision_start|>)
Vision End: 151653 (<|vision_end|>)

🚀 Performance

Fast inference with Q8_0 quantization
High accuracy OCR capabilities
Efficient memory usage (~1.2-1.3 GB VRAM)
Multi-language support for global documents

📝 License

Apache 2.0 - Free for commercial and research use

🙏 Credits

Based on the MonkeyOCR model architecture with optimizations for GGUF format and LM Studio compatibility.

For best vision performance, use the LM Studio optimized files! 🎯

Downloads last month: 591

GGUF

Model size

1B params

Architecture

qwen2vl

Hardware compatibility

We're not able to determine the quantization variants.

View all variants