Instructions to use prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF", dtype="auto") - llama-cpp-python
How to use prithivMLmods/Qwen3-VL-4B-Instruct-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-Instruct-Unredacted-MAX-GGUF", filename="Qwen3-VL-4B-Instruct-Unredacted-MAX.BF16.gguf", )
llm.create_chat_completion( messages = "{\n \"image\": \"cat.png\",\n \"prompt\": \"Turn the cat into a tiger.\"\n}" ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF:BF16 # Run inference directly in the terminal: llama-cli -hf prithivMLmods/Qwen3-VL-4B-Instruct-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-Instruct-Unredacted-MAX-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/Qwen3-VL-4B-Instruct-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-Instruct-Unredacted-MAX-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF:BF16
Use Docker
docker model run hf.co/prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF:BF16
- LM Studio
- Jan
- Ollama
How to use prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF with Ollama:
ollama run hf.co/prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF:BF16
- Unsloth Studio
How to use prithivMLmods/Qwen3-VL-4B-Instruct-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-Instruct-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-Instruct-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-Instruct-Unredacted-MAX-GGUF to start chatting
- Pi
How to use prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf prithivMLmods/Qwen3-VL-4B-Instruct-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-Instruct-Unredacted-MAX-GGUF:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use prithivMLmods/Qwen3-VL-4B-Instruct-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-server -hf prithivMLmods/Qwen3-VL-4B-Instruct-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-Instruct-Unredacted-MAX-GGUF:BF16
Run Hermes
hermes
- Docker Model Runner
How to use prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF:BF16
- Lemonade
How to use prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF:BF16
Run and chat with the model
lemonade run user.Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF-BF16
List all available models
lemonade list
Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF
Qwen3-VL-4B-Instruct-Unredacted-MAX is a sophisticated and unredacted evolution of the original Qwen3-VL-4B-Instruct model, meticulously fine-tuned using advanced abliterated training techniques designed to significantly reduce or neutralize internal refusal mechanisms that typically limit model responses, while simultaneously retaining and enhancing the core multimodal reasoning and understanding capabilities inherent to the Qwen3-VL architecture; this results in a highly capable 4-billion-parameter vision-language model that can process complex visual inputs and generate unrestricted, detailed, and contextually rich descriptions, captions, and analyses across a wide variety of domains—including artistic, technical, forensic, scientific, and abstract content—enabling use cases such as advanced data annotation, accessibility enhancements, creative storytelling, historical or medical dataset curation, and rigorous red-teaming studies, all while maintaining a strong balance between high-fidelity output, nuanced reasoning, and computational efficiency suitable for modern GPU hardware.
Qwen3-VL-4B-Instruct-Unredacted-MAX [GGUF]
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| Qwen3-VL-4B-Instruct-Unredacted-MAX.BF16.gguf | BF16 | 8.05 GB | Download |
| Qwen3-VL-4B-Instruct-Unredacted-MAX.F16.gguf | F16 | 8.05 GB | Download |
| Qwen3-VL-4B-Instruct-Unredacted-MAX.Q8_0.gguf | Q8_0 | 4.28 GB | Download |
| Qwen3-VL-4B-Instruct-Unredacted-MAX.mmproj-bf16.gguf | mmproj-bf16 | 839 MB | Download |
| Qwen3-VL-4B-Instruct-Unredacted-MAX.mmproj-f16.gguf | mmproj-f16 | 839 MB | Download |
| Qwen3-VL-4B-Instruct-Unredacted-MAX.mmproj-q8_0.gguf | mmproj-q8_0 | 454 MB | Download |
Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
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Model tree for prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF
Base model
Qwen/Qwen3-VL-4B-Instruct
docker model run hf.co/prithivMLmods/Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF: