Instructions to use amad-iq/amad-vlm6-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 amad-iq/amad-vlm6-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 amad-iq/amad-vlm6-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf amad-iq/amad-vlm6-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf amad-iq/amad-vlm6-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf amad-iq/amad-vlm6-GGUF:Q8_0

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 amad-iq/amad-vlm6-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf amad-iq/amad-vlm6-GGUF:Q8_0

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 amad-iq/amad-vlm6-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf amad-iq/amad-vlm6-GGUF:Q8_0

Use Docker

docker model run hf.co/amad-iq/amad-vlm6-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use amad-iq/amad-vlm6-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amad-iq/amad-vlm6-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": "amad-iq/amad-vlm6-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/amad-iq/amad-vlm6-GGUF:Q8_0

Ollama
How to use amad-iq/amad-vlm6-GGUF with Ollama:
```
ollama run hf.co/amad-iq/amad-vlm6-GGUF:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use amad-iq/amad-vlm6-GGUF with Docker Model Runner:
```
docker model run hf.co/amad-iq/amad-vlm6-GGUF:Q8_0
```

Lemonade

How to use amad-iq/amad-vlm6-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull amad-iq/amad-vlm6-GGUF:Q8_0

Run and chat with the model

lemonade run user.amad-vlm6-GGUF-Q8_0

List all available models

lemonade list

Atomic Chat

amad-vlm6 — GGUF

GGUF builds of amad-iq/amad-vlm6, a TIES merge of two Arabic OCR fine-tunes of Qwen2.5-VL-7B-Instruct.

📄 Technical report · 🧠 Merged weights

File	Format	Size
`amad-vlm6-q8_0.gguf`	Q8_0	8.10 GB
`mmproj-amad-vlm6-f16.gguf`	F16 vision projector	1.35 GB

The mmproj file is required alongside the model file — it carries the vision encoder. Download both into the same folder.

llama.cpp

llama-mtmd-cli -m amad-vlm6-q8_0.gguf --mmproj mmproj-amad-vlm6-f16.gguf \
  --image page.png -p "Extract the text in the image. Give me the final text, nothing else." \
  -n 4096 --temp 0 --repeat-penalty 1.05

LM Studio

Download a model file plus the mmproj into the same folder, load it, attach an image. Set context to at least 8192 and max output tokens to 4096 — the model may emit <think>…</think> first; keep only the text after the last </think>.

License

Apache 2.0.

Author: Murtadha · amad-iq

Downloads last month: 170

GGUF

Model size

8B params

Architecture

qwen2vl

Hardware compatibility

8-bit

Model tree for amad-iq/amad-vlm6-GGUF

Base model

amad-iq/amad-vlm6

Quantized

(1)

this model