Instructions to use a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-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 a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-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 a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-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 a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-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 a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-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 a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M

Use Docker

docker model run hf.co/a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF with Ollama:
```
ollama run hf.co/a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M
```
Unsloth Desktop

How to use a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-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": "a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF with Docker Model Runner:
```
docker model run hf.co/a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M
```

Lemonade

How to use a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-VL-8B-Contracts-OCR-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-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 a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-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 a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-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 "a-mo-yehia/Qwen3-VL-8B-Contracts-OCR-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"

Qwen3-VL-8B Contracts OCR (GGUF)

This repository provides GGUF quantizations of Qwen3-VL-8B-Contracts-OCR, a fine-tuned vision-language model specialized in extracting structured information from scanned Egyptian company incorporation contracts written in Arabic.

The full Transformers version, model card, usage example, training details, and documentation here:

👉 Qwen3-VL-8B-Contracts-OCR

About

These GGUF files are intended for inference with:

llama.cpp
LM Studio (when vision support is available)
llama-server
compatible GGUF runtimes

The original fine-tuned model was trained using Unsloth on top of Qwen3-VL-8B.

Available Quantizations

File	Description
`model_f16.gguf`	Full FP16 precision
`model_q8_0.gguf`	8-bit quantization
`model_q4_k_m.gguf`	Recommended balance between quality and speed
`mmproj_f16.gguf`	Vision projector (required for image inference)

Which Quantization Should I Use?

Quantization	Recommendation
Q4_K_M	Best overall choice for most users
Q8_0	Better quality with higher memory usage
F16	Maximum quality and accuracy

Requirements

For vision inference you must load:

one GGUF model
mmproj_f16.gguf

The vision projector is required for processing images.

Model Purpose

The model extracts structured information from scanned Egyptian company incorporation contracts, including:

Company name
Company type
Business activities
Company address
Capital information
Partners and shareholders
Contract articles
Tables (returned as Markdown)
Other legal fields

The output is a structured JSON document.

Language

Arabic

License

Apache-2.0

Downloads last month: 207

GGUF

Model size

8B params

Architecture

qwen3vl

Hardware compatibility

4-bit

8-bit

16-bit