Instructions to use VECTORVV1/Qwen3VL-8B-Balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VECTORVV1/Qwen3VL-8B-Balanced with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VECTORVV1/Qwen3VL-8B-Balanced",
	filename="Qwen3VL-8B-Uncensored-HauhauCS-Balanced-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use VECTORVV1/Qwen3VL-8B-Balanced with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/Qwen3VL-8B-Balanced: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 VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf VECTORVV1/Qwen3VL-8B-Balanced: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 VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M

Use Docker

docker model run hf.co/VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M

LM Studio
Jan
Ollama
How to use VECTORVV1/Qwen3VL-8B-Balanced with Ollama:
```
ollama run hf.co/VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M
```

Unsloth Studio new

How to use VECTORVV1/Qwen3VL-8B-Balanced 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 VECTORVV1/Qwen3VL-8B-Balanced 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 VECTORVV1/Qwen3VL-8B-Balanced to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VECTORVV1/Qwen3VL-8B-Balanced to start chatting

Pi new

How to use VECTORVV1/Qwen3VL-8B-Balanced with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VECTORVV1/Qwen3VL-8B-Balanced: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": "Qwen3VL-8B-Balanced"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use VECTORVV1/Qwen3VL-8B-Balanced with Docker Model Runner:
```
docker model run hf.co/VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M
```

Lemonade

How to use VECTORVV1/Qwen3VL-8B-Balanced with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M

Run and chat with the model

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

List all available models

lemonade list

Qwen3VL-8B-Uncensored-HauhauCS-Balanced

Join the Discord for updates, roadmaps, projects, or just to chat.

Qwen3VL-8B uncensored by HauhauCS.

About

No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals.

These are meant to be the best lossless uncensored models out there.

Balanced vs Aggressive

This is the Balanced variant with moderate uncensoring. Best for agentic coding and reliability-critical tasks.

For stronger uncensoring when this variant refuses too much, use the Aggressive variant instead.

Downloads

File	Quant	Size
Qwen3VL-8B-Uncensored-HauhauCS-Balanced-BF16.gguf	BF16	16 GB
Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q8_0.gguf	Q8_0	8.2 GB
Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q6_K.gguf	Q6_K	6.3 GB
Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf	Q4_K_M	4.7 GB
Qwen3VL-8B-Uncensored-HauhauCS-Balanced-mmproj-f16.gguf	mmproj	1.1 GB

Specs

8B parameters
256K context
Vision-language model (requires mmproj file for image input)
Based on Qwen3-VL-8B

Usage

Works with llama.cpp, LM Studio, koboldcpp, etc.

For vision capabilities, load both the main model and the mmproj file.

llama.cpp example:

./llama-cli -m Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf \
  --mmproj Qwen3VL-8B-Uncensored-HauhauCS-Balanced-mmproj-f16.gguf \
  --image your_image.jpg \
  -p "Describe this image"

Downloads last month: 115

GGUF

Model size

8B params

Architecture

qwen3vl

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

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