Instructions to use VECTORVV1/Qwen3VL-8B-Balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use VECTORVV1/Qwen3VL-8B-Balanced 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 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 serve -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
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
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 serve -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": "VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M" } ] } } }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
- Hermes Agent
How to use VECTORVV1/Qwen3VL-8B-Balanced with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf VECTORVV1/Qwen3VL-8B-Balanced: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 VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use VECTORVV1/Qwen3VL-8B-Balanced with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M
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 "VECTORVV1/Qwen3VL-8B-Balanced:Q4_K_M" \ --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"
Duplicate from HauhauCS/Qwen3VL-8B-Uncensored-HauhauCS-Balanced
Browse filesCo-authored-by: HauHau <HauhauCS@users.noreply.huggingface.co>
- .gitattributes +40 -0
- Qwen3VL-8B-Uncensored-HauhauCS-Balanced-BF16.gguf +3 -0
- Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf +3 -0
- Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q6_K.gguf +3 -0
- Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q8_0.gguf +3 -0
- Qwen3VL-8B-Uncensored-HauhauCS-Balanced-mmproj-f16.gguf +3 -0
- README.md +60 -0
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Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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Qwen3VL-8B-Uncensored-HauhauCS-Balanced-BF16.gguf filter=lfs diff=lfs merge=lfs -text
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---
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license: apache-2.0
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tags:
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- uncensored
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- qwen3
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- vision
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- multimodal
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language:
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- en
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- zh
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---
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# Qwen3VL-8B-Uncensored-HauhauCS-Balanced
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> **[Join the Discord](https://discord.gg/SZ5vacTXYf)** for updates, roadmaps, projects, or just to chat.
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Qwen3VL-8B uncensored by HauhauCS.
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## About
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No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals.
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These are meant to be the best lossless uncensored models out there.
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## Balanced vs Aggressive
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This is the **Balanced** variant with moderate uncensoring. Best for agentic coding and reliability-critical tasks.
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For stronger uncensoring when this variant refuses too much, use the [Aggressive](https://huggingface.co/HauhauCS/Qwen3VL-8B-Uncensored-HauhauCS-Aggressive) variant instead.
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## Downloads
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| File | Quant | Size |
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| 34 |
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|------|-------|------|
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| Qwen3VL-8B-Uncensored-HauhauCS-Balanced-BF16.gguf | BF16 | 16 GB |
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| Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q8_0.gguf | Q8_0 | 8.2 GB |
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| Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q6_K.gguf | Q6_K | 6.3 GB |
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| Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf | Q4_K_M | 4.7 GB |
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| Qwen3VL-8B-Uncensored-HauhauCS-Balanced-mmproj-f16.gguf | mmproj | 1.1 GB |
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## Specs
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- 8B parameters
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- 256K context
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- Vision-language model (requires mmproj file for image input)
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- Based on [Qwen3-VL-8B](https://huggingface.co/Qwen/Qwen3-VL-8B)
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## Usage
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| 49 |
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Works with llama.cpp, LM Studio, koboldcpp, etc.
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**For vision capabilities**, load both the main model and the mmproj file.
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llama.cpp example:
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```bash
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./llama-cli -m Qwen3VL-8B-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf \
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--mmproj Qwen3VL-8B-Uncensored-HauhauCS-Balanced-mmproj-f16.gguf \
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--image your_image.jpg \
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-p "Describe this image"
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```
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