Instructions to use andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="andyjack/Huihui-Qwen3.5-4B-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("andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andyjack/Huihui-Qwen3.5-4B-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": "andyjack/Huihui-Qwen3.5-4B-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/andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M

SGLang

How to use andyjack/Huihui-Qwen3.5-4B-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 "andyjack/Huihui-Qwen3.5-4B-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": "andyjack/Huihui-Qwen3.5-4B-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 "andyjack/Huihui-Qwen3.5-4B-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": "andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF with Ollama:
```
ollama run hf.co/andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF to start chatting

How to use andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Huihui-Qwen3.5-4B-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use andyjack/Huihui-Qwen3.5-4B-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 andyjack/Huihui-Qwen3.5-4B-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 "andyjack/Huihui-Qwen3.5-4B-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"

This is an uncensored version of Qwen/Qwen3.5-4B 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.
I needed an uncensored multi-modal GGUF in Q4_K_M to fit on a single Radeon R9 M295X / M390X with 4GB of VRAM. This model was tested with llama.cpp compiled with VULKAN driver

cmake -B build -DGGML_VULKAN=ON -DCMAKE_BUILD_TYPE=Release

The model quant was tested with Huihui-Qwen3.5-4B-mmproj-F16.gguf for multi-modal image use.
It generate 12 tokens/sec with the mmproj loaded and 25 tokens/sec without mmproj loaded.
Testing performed on a graphics card released in 2014.

llama.cpp

llama.cpp/build/bin/llama-server \
  -m Huihui-Qwen3.5-4B-Q4_K_M.gguf \
  --mmproj Huihui-Qwen3.5-4B-mmproj-F16.gguf \
  --host 0.0.0.0 \
  --port 11434 \
  --flash-attn on \
  --cache-type-k q4_0 \
  --cache-type-v q4_0 \
  --n-gpu-layers 99 \
  --temp 0.7 \
  --top-p 0.95 \
  --top-k 20 \
  --ctx-size = 57344

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 nor I bear any responsibility for any consequences arising from its use.

Donation

Your donation helps us continue our further development and improvement, a cup of coffee can do it.

bitcoin:

bc1q6nvh39fcmy0de0ezepnn2z0rn4dme9yjal77ah

Downloads last month: 228

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for andyjack/Huihui-Qwen3.5-4B-abliterated-GGUF

Base model

Qwen/Qwen3.6-27B

Quantized

(714)

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