Instructions to use Abiray/Huihui-Qwen3.5-9B-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 Abiray/Huihui-Qwen3.5-9B-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 Abiray/Huihui-Qwen3.5-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/Huihui-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 serve -hf Abiray/Huihui-Qwen3.5-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/Huihui-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/Huihui-Qwen3.5-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/Huihui-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/Huihui-Qwen3.5-9B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Huihui-Qwen3.5-9B-abliterated-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use Abiray/Huihui-Qwen3.5-9B-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-Qwen3.5-9B-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-Qwen3.5-9B-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

How to use Abiray/Huihui-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 serve -hf Abiray/Huihui-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/Huihui-Qwen3.5-9B-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

Huihui-Qwen3.5-9B-abliterated - GGUF

This repository contains GGUF format model files for huihui-ai/Huihui-Qwen3.5-9B-abliterated.

💾 Provided Files and Quantizations

We provide a spectrum of quantizations to suit different hardware capabilities. If you are unsure which to choose, Q4_K_M is the recommended standard for the best balance of speed, size, and reasoning retention.

Filename	Quant Type	File Size	Recommended RAM/VRAM	Use Case
`Huihui-Qwen3.5-9B-abliterated-Q4_K_S.gguf`	Q4_K_S	~5.1 GB	8 GB	Fastest inference, lowest memory footprint.
`Huihui-Qwen3.5-9B-abliterated-Q4_K_M.gguf`	Q4_K_M	~5.4 GB	8 GB	Recommended. Best balance of quality and size.
`Huihui-Qwen3.5-9B-abliterated-Q5_K_S.gguf`	Q5_K_S	~6.1 GB	10 GB	Slightly higher quality, needs more memory.
`Huihui-Qwen3.5-9B-abliterated-Q5_K_M.gguf`	Q5_K_M	~6.3 GB	10 GB	Excellent reasoning retention for 12GB+ GPUs.
`Huihui-Qwen3.5-9B-abliterated-Q6_K.gguf`	Q6_K	~7.4 GB	12 GB	Near-lossless quality.
`Huihui-Qwen3.5-9B-abliterated-Q8_0.gguf`	Q8_0	~9.6 GB	16 GB	Virtually indistinguishable from the F16 base model.

🚀 How to Use

These GGUF files are fully compatible with popular local AI software, including:

LM Studio
Ollama
Jan
text-generation-webui (Oobabooga)
llama.cpp

Prompt Format (ChatML)

Qwen models strictly use the ChatML format for prompting. Your system prompt and user inputs must be wrapped exactly like this:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Write a simple Python script.<|im_end|>
<|im_start|>assistant

Downloads last month: 2,150

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for Abiray/Huihui-Qwen3.5-9B-abliterated-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

huihui-ai/Huihui-Qwen3.5-9B-abliterated

Quantized

(16)

this model