Instructions to use huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use huihui-ai/Huihui-Qwen3.8-27B-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 huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

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 huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

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 huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

Use Docker

docker model run hf.co/huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "huihui-ai/Huihui-Qwen3.8-27B-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": "huihui-ai/Huihui-Qwen3.8-27B-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/huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

SGLang

How to use huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "huihui-ai/Huihui-Qwen3.8-27B-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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "huihui-ai/Huihui-Qwen3.8-27B-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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF with Ollama:
```
ollama run hf.co/huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K
```

Unsloth Studio

How to use huihui-ai/Huihui-Qwen3.8-27B-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 huihui-ai/Huihui-Qwen3.8-27B-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 huihui-ai/Huihui-Qwen3.8-27B-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 huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF to start chatting

How to use huihui-ai/Huihui-Qwen3.8-27B-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 huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

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": "huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K
```

Lemonade

How to use huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

Run and chat with the model

lemonade run user.Huihui-Qwen3.8-27B-abliterated-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use huihui-ai/Huihui-Qwen3.8-27B-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 huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

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 huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use huihui-ai/Huihui-Qwen3.8-27B-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 huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K

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 "huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q2_K" \
  --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"

huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF

This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.

Note

The first 15 layers were retained without ablation. MTP and visual has not been modified.

We have already converted the weights (token_embd,output,ffn_down,ssm_out,attn_output) that need to be ablated in the versions below Q8_0 from Q2_K, Q3_K, Q4_K, Q5_K, and Q6_K to Q8_0 to improve response quality, and changed the filename to K_L.

In the Q8_0 quantized version, we changed the Q8_0 weights (token_embd,output,ffn_down,ssm_out,attn_output) targeted for ablation to BF16 and renamed the file to Q8_0_L.

This is not a standard quantization, so you might find that Q2_K_L is larger than Q3_K and Q4_K.

Specific Quantification Method

Some people may misunderstand. The specific quantification method is as follows.

Q2_K_L - Q6_K_L

Qwen3.8-27B-tensor_types-Q6_K_L.txt

llama-quantize \
  --allow-requantize \
  --tensor-type-file huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Qwen3.8-27B-tensor_types-Q6_K_L.txt \
  huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-bf16.gguf \
  huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-Q6_K_L.gguf Q6_K

Q8_0_L

Qwen3.8-27B-tensor_types-Q6_K_L.txt

llama-quantize \ 
  --allow-requantize \
  --tensor-type-file huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Qwen3.8-27B-tensor_types-Q8_0_L.txt \
  huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-bf16.gguf \ 
  huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-Q8_0_L.gguf Q8_0

ollama

Please use the latest version of ollama

You can use huihui_ai/Qwen3.8-abliterated directly,

ollama run huihui_ai/Qwen3.8-abliterated

llama.cpp

Use the latest llama.cpp,

llama-cli -m huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF/Huihui-Qwen3.8-27B-abliterated-Q4_K.gguf -c 262144

Usage Warnings

Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.

Donation

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GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

6-bit

8-bit

16-bit

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