Instructions to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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("prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF", device_map="auto")

llama-cpp-python

How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF",
	filename="Qwen3-VL-4B-Thinking-Unredacted-MAX.BF16.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 Settings

llama.cpp

How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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 prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

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 prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

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 prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

LM Studio
Jan

vLLM

How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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": "prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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/prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

SGLang

How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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 "prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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": "prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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 "prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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": "prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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 prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16
```

Unsloth Studio

How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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 prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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 prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF to start chatting

How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

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": "prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-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 prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

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 prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

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 "prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16" \
  --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"

Docker Model Runner
How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16
```

Lemonade

How to use prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF:BF16

Run and chat with the model

lemonade run user.Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF-BF16

List all available models

lemonade list

Qwen3-VL-4B-Thinking-Unredacted-MAX-GGUF

Qwen3-VL-4B-Thinking-Unredacted-MAX represents a powerful and unredacted evolution of the original Qwen3-VL-4B-Thinking model, meticulously fine-tuned through advanced abliterated training strategies that are explicitly designed to minimize internal refusal mechanisms which often constrain standard language and vision-language models, while simultaneously preserving and enhancing the model’s core multimodal reasoning abilities, enabling it to process complex visual inputs with remarkable precision and generate highly detailed, nuanced, and contextually rich captions, descriptions, and analyses across a diverse range of content types, including artistic, technical, forensic, scientific, and abstract domains; this 4-billion-parameter vision-language model excels in delivering unrestricted, high-fidelity outputs suitable for tasks such as in-depth data annotation, accessibility enhancement, creative storytelling, historical or medical dataset curation, and rigorous red-teaming research, all while balancing computational efficiency and interpretability, making it an ideal tool for researchers, developers, and professionals who require advanced, unfiltered reasoning and descriptive capabilities from a state-of-the-art vision-language system.

Qwen3-VL-4B-Thinking-Unredacted-MAX [GGUF]

File Name	Quant Type	File Size	File Link
Qwen3-VL-4B-Thinking-Unredacted-MAX.BF16.gguf	BF16	8.05 GB	Download
Qwen3-VL-4B-Thinking-Unredacted-MAX.F16.gguf	F16	8.05 GB	Download
Qwen3-VL-4B-Thinking-Unredacted-MAX.Q8_0.gguf	Q8_0	4.28 GB	Download
Qwen3-VL-4B-Thinking-Unredacted-MAX.mmproj-bf16.gguf	mmproj-bf16	839 MB	Download
Qwen3-VL-4B-Thinking-Unredacted-MAX.mmproj-f16.gguf	mmproj-f16	839 MB	Download
Qwen3-VL-4B-Thinking-Unredacted-MAX.mmproj-q8_0.gguf	mmproj-q8_0	454 MB	Download