Instructions to use Abiray/Qwen3.5-9B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abiray/Qwen3.5-9B-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Abiray/Qwen3.5-9B-abliterated-GGUF",
	filename="Qwen3.5-9B-abliterated-Q3_K_L.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Abiray/Qwen3.5-9B-abliterated-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/Qwen3.5-9B-abliterated-GGUF: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 Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/Qwen3.5-9B-abliterated-GGUF: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 Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Abiray/Qwen3.5-9B-abliterated-GGUF with Ollama:
```
ollama run hf.co/Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use Abiray/Qwen3.5-9B-abliterated-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 Abiray/Qwen3.5-9B-abliterated-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 Abiray/Qwen3.5-9B-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abiray/Qwen3.5-9B-abliterated-GGUF to start chatting

How to use Abiray/Qwen3.5-9B-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Abiray/Qwen3.5-9B-abliterated-GGUF: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": "Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Abiray/Qwen3.5-9B-abliterated-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 Abiray/Qwen3.5-9B-abliterated-GGUF: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 Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Abiray/Qwen3.5-9B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/Qwen3.5-9B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/Qwen3.5-9B-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-9B-abliterated - GGUF

This repository contains a full spectrum of GGUF quantizations for lukey03's Qwen3.5-9B-abliterated.

These files are optimized for local inference using llama.cpp, LM Studio, Jan, Ollama, and other compatible software.

🧠 About the Base Model

The base model is a fully uncensored version of Qwen3.5-9B. It achieved a 0% refusal rate (answering 100% of controversial/restricted prompts) through a two-stage process:

Orthogonal Projection (Abliteration): Surgically removing the "refusal direction" from the residual stream across all 32 layers.
LoRA Fine-tuning: Targeted training to eliminate the remaining stubborn refusal categories.

Key Features:

Architecture: 9 Billion parameters, Hybrid Gated DeltaNet + standard attention.
Context Window: Natively supports up to 262k tokens.
Capabilities: Strong reasoning, coding, and creative writing, with natively built-in Multimodal (Vision) support.

💾 Available Quantizations

File Name	Quant Type	Size	Description / Recommendation
`Qwen3.5-9B-abliterated-Q8_0.gguf`	Q8_0	~9.5 GB	Highest Quality: Near-perfect F16 equivalent. Best if you have 12GB+ VRAM.
`Qwen3.5-9B-abliterated-Q6_K.gguf`	Q6_K	~7.2 GB	Gold Standard: Extremely low quality loss. The recommended sweet spot for 9B models.
`Qwen3.5-9B-abliterated-Q5_K_M.gguf`	Q5_K_M	~6.4 GB	Great balance of speed and intelligence. Fits comfortably on 8GB VRAM cards.
`Qwen3.5-9B-abliterated-Q5_K_S.gguf`	Q5_K_S	~6.2 GB	Slightly faster than K_M, with a microscopic drop in nuance.
`Qwen3.5-9B-abliterated-Q4_K_M.gguf`	Q4_K_M	~5.6 GB	Excellent for lower-end hardware and older laptops.
`Qwen3.5-9B-abliterated-Q4_K_S.gguf`	Q4_K_S	~5.3 GB	Fastest acceptable 4-bit quant. Good for limited memory.
`Qwen3.5-9B-abliterated-Q3_K_L.gguf`	Q3_K_L	~4.8 GB	Heavy compression. Expect some logic loss and hallucination.
`Qwen3.5-9B-abliterated-Q3_K_M.gguf`	Q3_K_M	~4.4 GB	Extreme compression. Only use if absolutely necessary.

🚀 How to Use

1. LM Studio

Open LM Studio and go to the Search tab.
Search for Abhiray/Qwen3.5-9B-abliterated-GGUF.
Download your preferred quantization (e.g., Q6_K or Q5_K_M).
Load it into the chat interface and start typing!

2. llama.cpp (Terminal)

./llama-cli -m Qwen3.5-9B-abliterated-Q6_K.gguf -p "<|im_start|>system\nYou are an uncensored AI assistant.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n" -n 512