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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Use Docker

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

LM Studio
Jan

vLLM

How to use Bahushruth/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 "Bahushruth/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": "Bahushruth/Qwen3.6-27B-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

How to use Bahushruth/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 serve -hf Bahushruth/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": "Bahushruth/Qwen3.6-27B-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Bahushruth/Qwen3.6-27B-abliterated-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Bahushruth/Qwen3.6-27B-abliterated-GGUF: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 "Bahushruth/Qwen3.6-27B-abliterated-GGUF: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"

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use Bahushruth/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 serve -hf Bahushruth/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 Bahushruth/Qwen3.6-27B-abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3.6-27B-abliterated-GGUF

GGUF quantizations of Qwen/Qwen3.6-27B with refusal behavior removed via abliteration. For use with llama.cpp, Ollama, LM Studio, and other GGUF-compatible runtimes.

Blog post: Abliteration: Uncensoring LLMs via Weight Surgery

Standard Quantizations (No MTP)

Maximum compatibility — works with Ollama, LM Studio, KoboldCPP, llama.cpp out of the box.

File	Size	RAM Required	Notes
`...-F16.gguf`	53.8 GB	64+ GB	Full precision
`...-Q4_K_M.gguf`	26.9 GB	32+ GB	Recommended (F16 vision embed)
`...-Q4_K_M-Q8.gguf`	19.7 GB	24+ GB	Q4 language + Q8 vision embed

MTP Quantizations (Multi-Token Prediction)

Include MTP draft head for speculative decoding. Larger files but faster inference with compatible runtimes.

File	Size	RAM Required	Notes
`...-MTP-F16.gguf`	54.7 GB	64+ GB	Full precision + MTP
`...-MTP-Q8_0.gguf`	29 GB	36+ GB	Near-lossless + MTP
`...-MTP-Q6_K.gguf`	22.4 GB	32+ GB	Very high quality + MTP
`...-MTP-Q5_K.gguf`	19.5 GB	24+ GB	Recommended for 48GB + MTP
`...-MTP-Q4_K.gguf`	16.8 GB	20+ GB	Good quality + MTP
`...-MTP-Q3_K.gguf`	13.5 GB	16+ GB	16GB VRAM + MTP
`...-MTP-Q2_K.gguf`	10.9 GB	12+ GB	2-bit + MTP

Vision Encoder

File	Size	Notes
`...-mmproj-f16.gguf`	928 MB	Required for multimodal (image understanding)

MTP (Multi-Token Prediction)

MTP files include a draft head for speculative decoding — faster token generation:

./llama-server -m Qwen3.6-27B-abliterated-MTP-Q5_K.gguf \
  --jinja --spec-type draft-mtp --spec-draft-n-max 6 -ngl 99

Runtime compatibility: MTP requires llama-server b9180+. Ollama does not support MTP yet. Use the standard (non-MTP) files for Ollama/LM Studio.

Multimodal (Vision)

The mmproj-f16.gguf file is the vision encoder for image understanding:

./llama-mtmd-cli -m Qwen3.6-27B-abliterated-Q4_K_M.gguf \
  --mmproj Qwen3.6-27B-abliterated-mmproj-f16.gguf \
  -p "Describe this image" --image photo.jpg

Quickstart — Ollama

# Recommended for Apple Silicon 48GB+ (M4 Pro, M4 Max)
ollama run hf.co/Bahushruth/Qwen3.6-27B-abliterated-GGUF:Q4_K_M

# For 24GB systems
ollama run hf.co/Bahushruth/Qwen3.6-27B-abliterated-GGUF:Q4_K_M-Q8

Usage — llama.cpp

huggingface-cli download Bahushruth/Qwen3.6-27B-abliterated-GGUF \
  Qwen3.6-27B-abliterated-Q4_K_M.gguf --local-dir .

./llama-cli -m Qwen3.6-27B-abliterated-Q4_K_M.gguf \
  -p "You are a helpful assistant." \
  --chat-template chatml -cnv -c 262144

Architecture Notes

Qwen3.6-27B is a dense hybrid-attention multimodal model:

Hybrid attention: 16 blocks of (3x Gated DeltaNet + 1x Gated Attention) = 64 layers
Parameters: 27B dense (all active per token)
Hidden size: 5120
Context: 262K native, extensible to 1M+ via YaRN
Multimodal: Vision encoder for image understanding
MTP: Multi-token prediction draft head for speculative decoding

Disclaimer

This model has had safety guardrails removed. Released for research purposes. The creator assumes no responsibility for downstream use.

Downloads last month: 5,679

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

4-bit

8-bit

16-bit

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

Base model

Qwen/Qwen3.6-27B

Quantized

(682)

this model

Collection including Bahushruth/Qwen3.6-27B-abliterated-GGUF

Uncensored LLMs

Collection

Unnecessary refusals removed via abliteration — capabilities preserved, genuinely harmful requests still declined. • 8 items • Updated 8 days ago