Instructions to use John1604/Qwen3-VL-30B-A3B-Thinking-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use John1604/Qwen3-VL-30B-A3B-Thinking-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="John1604/Qwen3-VL-30B-A3B-Thinking-gguf",
	filename="Qwen3-VL-30B-A3B-Thinking-q3_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 John1604/Qwen3-VL-30B-A3B-Thinking-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf John1604/Qwen3-VL-30B-A3B-Thinking-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf John1604/Qwen3-VL-30B-A3B-Thinking-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 John1604/Qwen3-VL-30B-A3B-Thinking-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf John1604/Qwen3-VL-30B-A3B-Thinking-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 John1604/Qwen3-VL-30B-A3B-Thinking-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf John1604/Qwen3-VL-30B-A3B-Thinking-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 John1604/Qwen3-VL-30B-A3B-Thinking-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf John1604/Qwen3-VL-30B-A3B-Thinking-gguf:Q4_K_M

Use Docker

docker model run hf.co/John1604/Qwen3-VL-30B-A3B-Thinking-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use John1604/Qwen3-VL-30B-A3B-Thinking-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "John1604/Qwen3-VL-30B-A3B-Thinking-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": "John1604/Qwen3-VL-30B-A3B-Thinking-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/John1604/Qwen3-VL-30B-A3B-Thinking-gguf:Q4_K_M

Ollama
How to use John1604/Qwen3-VL-30B-A3B-Thinking-gguf with Ollama:
```
ollama run hf.co/John1604/Qwen3-VL-30B-A3B-Thinking-gguf:Q4_K_M
```

Unsloth Studio new

How to use John1604/Qwen3-VL-30B-A3B-Thinking-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 John1604/Qwen3-VL-30B-A3B-Thinking-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 John1604/Qwen3-VL-30B-A3B-Thinking-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for John1604/Qwen3-VL-30B-A3B-Thinking-gguf to start chatting

Pi new

How to use John1604/Qwen3-VL-30B-A3B-Thinking-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf John1604/Qwen3-VL-30B-A3B-Thinking-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": "Qwen3-VL-30B-A3B-Thinking-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use John1604/Qwen3-VL-30B-A3B-Thinking-gguf with Docker Model Runner:
```
docker model run hf.co/John1604/Qwen3-VL-30B-A3B-Thinking-gguf:Q4_K_M
```

Lemonade

How to use John1604/Qwen3-VL-30B-A3B-Thinking-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull John1604/Qwen3-VL-30B-A3B-Thinking-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-VL-30B-A3B-Thinking-gguf-Q4_K_M

List all available models

lemonade list

Qwen3 VL 30B thinking gguf

Image to text, and text to text.

quantized models Comparison

Type	Bits	Quality	Description
IQ1	1-bit	very Low	Minimal footprint; worse than Q2/IQ2
Q2/IQ2	2-bit	🟥 Low	Minimal footprint; only for tests
Q3/IQ3	3-bit	🟧 Low–Med	“Medium” variant
Q4/IQ4	4-bit	🟩 Med–High	“Medium” — 4-bit
Q5	5-bit	🟩🟩 High	Excellent general-purpose quant
Q6_K	6-bit	🟩🟩🟩 Very High	Almost FP16 quality, larger size
Q8	8-bit	🟩🟩🟩🟩	Near-lossless baseline

Downloads last month: 109

GGUF

Model size

31B params

Architecture

qwen3vlmoe

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for John1604/Qwen3-VL-30B-A3B-Thinking-gguf

Base model

Qwen/Qwen3-VL-30B-A3B-Thinking

Quantized

(31)

this model