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

Use Docker

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

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

Unsloth Studio

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

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

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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

Lemonade

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

Pull the model

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

Run Hermes

hermes

Atomic Chat

Auto-Quantized GGUF Model

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

The calibration data for the imatrix is targeted at Chinese novels and role-playing (RP), while preserving logic and common sense.
imatrix 的校准数据以中文的小说、角色扮演为目标，同时保留逻辑和常识。

If the perplexity drops after quantization compared to the original precision, it might not actually be an improvement. Instead, it could be caused by differences in how llama.cpp quantization and perplexity tools handle special tokens. I will update the README generation code in the future. 如果困惑度在量化之后与原精度相比变低，可能并不是真的提升，而是llamacpp量化工具和困惑度计算工具处理特殊token行为不同导致的，我将在未来修改生成readme的代码。