Instructions to use CLLG/Qwen3VL-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CLLG/Qwen3VL-8B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="CLLG/Qwen3VL-8B-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("CLLG/Qwen3VL-8B-GGUF", dtype="auto")

llama-cpp-python

How to use CLLG/Qwen3VL-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CLLG/Qwen3VL-8B-GGUF",
	filename="Qwen3VL-8B-synth_real.Q4_K_M.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 CLLG/Qwen3VL-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CLLG/Qwen3VL-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf CLLG/Qwen3VL-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CLLG/Qwen3VL-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf CLLG/Qwen3VL-8B-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 CLLG/Qwen3VL-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CLLG/Qwen3VL-8B-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 CLLG/Qwen3VL-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CLLG/Qwen3VL-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/CLLG/Qwen3VL-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use CLLG/Qwen3VL-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CLLG/Qwen3VL-8B-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": "CLLG/Qwen3VL-8B-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/CLLG/Qwen3VL-8B-GGUF:Q4_K_M

SGLang

How to use CLLG/Qwen3VL-8B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CLLG/Qwen3VL-8B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CLLG/Qwen3VL-8B-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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CLLG/Qwen3VL-8B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CLLG/Qwen3VL-8B-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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use CLLG/Qwen3VL-8B-GGUF with Ollama:
```
ollama run hf.co/CLLG/Qwen3VL-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use CLLG/Qwen3VL-8B-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 CLLG/Qwen3VL-8B-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 CLLG/Qwen3VL-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CLLG/Qwen3VL-8B-GGUF to start chatting

Pi new

How to use CLLG/Qwen3VL-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf CLLG/Qwen3VL-8B-GGUF:Q4_K_M

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": "CLLG/Qwen3VL-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use CLLG/Qwen3VL-8B-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 CLLG/Qwen3VL-8B-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 CLLG/Qwen3VL-8B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use CLLG/Qwen3VL-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/CLLG/Qwen3VL-8B-GGUF:Q4_K_M
```

Lemonade

How to use CLLG/Qwen3VL-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CLLG/Qwen3VL-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3VL-8B-GGUF-Q4_K_M

List all available models

lemonade list

CLLG/Qwen3VL-8B-GGUF

Quantized GGUF versions of the CLLG Qwen3-VL 8B model, fine-tuned for ancient Greek document understanding and TEI XML encoding.

This model is part of the Corpus Liberatum Linguae Graecae (CLLG) project, which aims to build a freely accessible, high-quality corpus of ancient Greek texts. It is supported by the ANR within the Programme Inria Quadrant (PIQ).

Model Description

This vision-language model is trained to process images of critical editions of ancient Greek (and Latin) texts and produce structured TEI XML output. It handles the complex page layouts typical of scholarly editions, including:

Main text in polytonic Greek
Canonical references (section, paragraph, line numbers)
Titles and headings
Footnotes and apparatus criticus markers

GGUF Files

You need both the language model file and the multimodal projector to run this model.

Filename	Quantization	Size	Use case
`Qwen3VL-8B-synth_real.Q4_K_M.gguf`	Q4_K_M	~5 GB	Recommended — good balance of size and quality
`Qwen3VL-8B-synth_real.Q5_K_M.gguf`	Q5_K_M	~6 GB	Higher quality
`Qwen3VL-8B-synth_real.Q8_0.gguf`	Q8_0	~9 GB	Near-lossless
`mmproj-BF16.gguf`	BF16	—	Vision projector — required for all variants

Usage

llama-cli \
  --model Qwen3VL-8B-synth_real.Q4_K_M.gguf \
  --mmproj mmproj-BF16.gguf \
  --image page.jpg \
  --prompt "Encode the following page in TEI XML."

Training Data

Fine-tuned on synthetic page images generated by the CLLG pipeline, covering approximately 175,000 Greek pages and 10,000 Latin pages drawn from 4,582 works, with over 5,000 typographic style combinations, complemented by real annotated document pages (synth_real suffix).

Intended Uses

Automatic TEI XML encoding of ancient Greek critical editions
Layout analysis and canonical reference detection in scholarly documents
Research in digital philology and computational humanities

Out of Scope

Modern Greek text
Non-document image understanding tasks
Apparatus criticus and complex critical apparatus (current focus is prose text)
Poetry and Drama

Project & Funding

This model is developed as part of the CLLG project, funded by the ANR within the PIQ initiative. Institutional partners include Persée and Biblissima.

Citation

If you use this model in your research, please cite the CLLG project and acknowledge ANR/PIQ funding.

License

Apache 2.0 — see LICENSE.

Downloads last month: 443

GGUF

Model size

8B params

Architecture

qwen3vl

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for CLLG/Qwen3VL-8B-GGUF

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

(286)

this model