Instructions to use Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF",
	filename="qwen3.6-27b-abliterated-Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Abiray/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M

Ollama
How to use Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF with Ollama:
```
ollama run hf.co/Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use Abiray/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-abliterated-GGUF to start chatting

How to use Abiray/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Abiray/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-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/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Huihui-Qwen3.6-27B-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Qwen 3.6 27B Abliterated - GGUF

This repository contains GGUF format quantized weights for huihui-ai/Huihui-Qwen3.6-27B-abliterated.

These files are designed for use with llama.cpp and compatible local inference engines (LM Studio, text-generation-webui, KoboldCPP, etc.).

Model Details

Base Model: Qwen 3.6 27B
Variant: Abliterated (Refusal mechanisms stripped)

Abliteration Notes

This model has been processed to remove inherent safety filters and refusal mechanisms. It is highly compliant and will generate responses to complex, edge-case, or typically restricted prompts directly from its base weights. No specialized system prompts or catalyst pre-fills are required to bypass refusals.

Available Quantizations

File Name	Format	Description
`qwen3.6-27b-abliterated-Q3_K_M.gguf`	Q3_K_M	Smallest footprint, high perplexity loss. Best for severely RAM-constrained environments.
`qwen3.6-27b-abliterated-Q4_K_S.gguf`	Q4_K_S	Fast inference, slightly lower quality than Q4_K_M.
`qwen3.6-27b-abliterated-Q4_K_M.gguf`	Q4_K_M	Recommended. Excellent balance of performance, size, and quality. (Also available in a dedicated repo).
`qwen3.6-27b-abliterated-Q5_K_M.gguf`	Q5_K_M	High quality, minimal degradation from FP16.
`qwen3.6-27b-abliterated-Q6_K.gguf`	Q6_K	Near-perfect fidelity to original FP16 base. Requires significant RAM.
`qwen3.6-27b-abliterated-Q8_0.gguf`	Q8_0	Maximum quality integer quantization. Very large file size.

Usage with llama.cpp

You can run this model via the command line using standard llama-cli commands. Since the model is abliterated, you do not need to wrap prompts in heavy system instructions.

# Basic inference
./llama-cli -m qwen3.6-27b-abliterated-Q4_K_M.gguf -p "Your prompt here" -n 512 -c 4096

# Interactive conversation mode
./llama-cli -m qwen3.6-27b-abliterated-Q4_K_M.gguf -i -cnv -c 8192

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GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF

Base model

Qwen/Qwen3.6-27B

Finetuned

huihui-ai/Huihui-Qwen3.6-27B-abliterated

Quantized

(25)

this model

Collection including Abiray/Huihui-Qwen3.6-27B-abliterated-GGUF

Qwen3.6-27B

Collection

6 items • Updated Apr 27