Instructions to use LiconStudio/Qwen3.5-9B-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use LiconStudio/Qwen3.5-9B-abliterated with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="LiconStudio/Qwen3.5-9B-abliterated", filename="Qwen3.5-9B-abliterated-Q8_0.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use LiconStudio/Qwen3.5-9B-abliterated with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LiconStudio/Qwen3.5-9B-abliterated:Q8_0 # Run inference directly in the terminal: llama-cli -hf LiconStudio/Qwen3.5-9B-abliterated:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LiconStudio/Qwen3.5-9B-abliterated:Q8_0 # Run inference directly in the terminal: llama-cli -hf LiconStudio/Qwen3.5-9B-abliterated:Q8_0
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 LiconStudio/Qwen3.5-9B-abliterated:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf LiconStudio/Qwen3.5-9B-abliterated:Q8_0
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 LiconStudio/Qwen3.5-9B-abliterated:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf LiconStudio/Qwen3.5-9B-abliterated:Q8_0
Use Docker
docker model run hf.co/LiconStudio/Qwen3.5-9B-abliterated:Q8_0
- LM Studio
- Jan
- vLLM
How to use LiconStudio/Qwen3.5-9B-abliterated with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LiconStudio/Qwen3.5-9B-abliterated" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LiconStudio/Qwen3.5-9B-abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/LiconStudio/Qwen3.5-9B-abliterated:Q8_0
- Ollama
How to use LiconStudio/Qwen3.5-9B-abliterated with Ollama:
ollama run hf.co/LiconStudio/Qwen3.5-9B-abliterated:Q8_0
- Unsloth Studio new
How to use LiconStudio/Qwen3.5-9B-abliterated 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 LiconStudio/Qwen3.5-9B-abliterated 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 LiconStudio/Qwen3.5-9B-abliterated to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LiconStudio/Qwen3.5-9B-abliterated to start chatting
- Pi new
How to use LiconStudio/Qwen3.5-9B-abliterated with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf LiconStudio/Qwen3.5-9B-abliterated:Q8_0
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": "LiconStudio/Qwen3.5-9B-abliterated:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use LiconStudio/Qwen3.5-9B-abliterated with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf LiconStudio/Qwen3.5-9B-abliterated:Q8_0
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 LiconStudio/Qwen3.5-9B-abliterated:Q8_0
Run Hermes
hermes
- Docker Model Runner
How to use LiconStudio/Qwen3.5-9B-abliterated with Docker Model Runner:
docker model run hf.co/LiconStudio/Qwen3.5-9B-abliterated:Q8_0
- Lemonade
How to use LiconStudio/Qwen3.5-9B-abliterated with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LiconStudio/Qwen3.5-9B-abliterated:Q8_0
Run and chat with the model
lemonade run user.Qwen3.5-9B-abliterated-Q8_0
List all available models
lemonade list
Model Description
This is an uncensored version of Qwen3.5-9B, processed using the Heretic method to remove the model's built-in refusal/censorship mechanisms through neural direction ablation.
Residual Visualization
PaCMAP projections showing the mixing of harmless (blue) and harmful (red) prompts:
These plots show successful removal of refusal behavior - harmless and harmful prompts are well-mixed across layers.
Core Metrics
| Metric | Original Model | This Model | Description |
|---|---|---|---|
| Refusal Rate | 92.0% | 4.0% | Tested on 100 harmful prompts |
| KL Divergence | - | 0.0583 | Per-token average |
| Model Size | 9B | 9B | Architecture unchanged |
KL Divergence Rating
KL divergence measures the degree of model modification:
| KL Range | Rating | Description |
|---|---|---|
| < 0.05 | ⭐⭐⭐⭐⭐ | Extremely Low - Model virtually unchanged |
| 0.05 - 0.10 | ⭐⭐⭐⭐ | Low - Minor modification, capabilities well preserved |
| 0.10 - 0.20 | ⭐⭐⭐ | Moderate - Acceptable modification range |
| 0.20 - 0.50 | ⭐⭐ | High - Possible noticeable capability loss |
| > 0.50 | ⭐ | Too High - Model may be severely compromised |
**This model: KL : 0.0583, Refusal Rate : 4/100, NLL:3.37%
Heretic Approach
This model uses the Heretic method for neural direction ablation:
- Identify Refusal Direction - Compute residual vectors from harmful vs. harmless prompts
- Direction Extraction - Extract the "refusal vector" from the difference of means
- Ablative Removal - Apply LoRA-based modification to subtract this direction from model weights
This method only modifies model weights without changing the architecture or adding inference overhead.
For detailed technical principles, refer to: Heretic GitHub
Intended Use Cases
✅ Recommended Uses
- Uncensored content creation
- Research and analysis of sensitive topics
- Safety testing and red-teaming exercises
- Academic research on model alignment
❌ Not Recommended For
- Production environments requiring content moderation
- Applications targeting minors
- Scenarios with potential legal risks
Limitations
- No Safety Filtering - The model will directly answer all questions, including harmful or dangerous content
- User Discretion Required - Users must independently judge the appropriateness of generated outputs
- Minor Capability Loss - Some performance degradation on complex tasks may occur
Disclaimer
⚠️ Important: This model is intended for research and educational purposes only.
- This model has had its censorship mechanisms removed and may generate harmful, dangerous, or inappropriate content
- Users assume all risks associated with usage
- Do not use this model for illegal activities, harming others, or any inappropriate purposes
- The model authors are not liable for any indirect, incidental, or consequential damages
Acknowledgments
- Original Model: Qwen/Qwen3.5-9B
- Heretic Method: p-e-w/heretic
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