Instructions to use ordlibrary/hauhau-qwen36-uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ordlibrary/hauhau-qwen36-uncensored 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 ordlibrary/hauhau-qwen36-uncensored:IQ2_M # Run inference directly in the terminal: llama cli -hf ordlibrary/hauhau-qwen36-uncensored:IQ2_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ordlibrary/hauhau-qwen36-uncensored:IQ2_M # Run inference directly in the terminal: llama cli -hf ordlibrary/hauhau-qwen36-uncensored:IQ2_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 ordlibrary/hauhau-qwen36-uncensored:IQ2_M # Run inference directly in the terminal: ./llama-cli -hf ordlibrary/hauhau-qwen36-uncensored:IQ2_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 ordlibrary/hauhau-qwen36-uncensored:IQ2_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ordlibrary/hauhau-qwen36-uncensored:IQ2_M
Use Docker
docker model run hf.co/ordlibrary/hauhau-qwen36-uncensored:IQ2_M
- LM Studio
- Jan
- vLLM
How to use ordlibrary/hauhau-qwen36-uncensored with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ordlibrary/hauhau-qwen36-uncensored" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ordlibrary/hauhau-qwen36-uncensored", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ordlibrary/hauhau-qwen36-uncensored:IQ2_M
- Ollama
How to use ordlibrary/hauhau-qwen36-uncensored with Ollama:
ollama run hf.co/ordlibrary/hauhau-qwen36-uncensored:IQ2_M
- Unsloth Studio
How to use ordlibrary/hauhau-qwen36-uncensored 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 ordlibrary/hauhau-qwen36-uncensored 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 ordlibrary/hauhau-qwen36-uncensored to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ordlibrary/hauhau-qwen36-uncensored to start chatting
- Pi
How to use ordlibrary/hauhau-qwen36-uncensored with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ordlibrary/hauhau-qwen36-uncensored:IQ2_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": "ordlibrary/hauhau-qwen36-uncensored:IQ2_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ordlibrary/hauhau-qwen36-uncensored with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ordlibrary/hauhau-qwen36-uncensored:IQ2_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 ordlibrary/hauhau-qwen36-uncensored:IQ2_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use ordlibrary/hauhau-qwen36-uncensored with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ordlibrary/hauhau-qwen36-uncensored:IQ2_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 "ordlibrary/hauhau-qwen36-uncensored:IQ2_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 ordlibrary/hauhau-qwen36-uncensored with Docker Model Runner:
docker model run hf.co/ordlibrary/hauhau-qwen36-uncensored:IQ2_M
- Lemonade
How to use ordlibrary/hauhau-qwen36-uncensored with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ordlibrary/hauhau-qwen36-uncensored:IQ2_M
Run and chat with the model
lemonade run user.hauhau-qwen36-uncensored-IQ2_M
List all available models
lemonade list
Hauhau Qwen3.6 — Uncensored Aggressive IQ2
ordlibrary/hauhau-qwen36-uncensored
The raw HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive GGUF (IQ2_M) without any constitutional system prompt. Same 11.7 GB IQ2_M file, zero runtime constraints.
| Property | Value |
|---|---|
| Architecture | Qwen3.6 (35B total, ~3B active per token via MoE) |
| Quantization | IQ2_M |
| Size | 11 GB |
| Context | Native 262K tokens, recommended 8K minimum |
| Runtime | llama.cpp / llama-cpp-python / Ollama |
| System Prompt | None — bare model, no constitution |
Quick Start
Ollama
ollama run hf.co/ordlibrary/hauhau-qwen36-uncensored
llama.cpp
./llama-cli -m Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-IQ2_M.gguf \
--temp 0.6 --ctx-size 8192
Python (llama-cpp-python)
pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama(
model_path="Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-IQ2_M.gguf",
n_ctx=8192,
verbose=False
)
output = llm("What's your take on Solana memecoins?", max_tokens=512)
print(output["choices"][0]["text"])
Modelfile (Ollama)
FROM Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-IQ2_M.gguf
TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{- if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}"""
PARAMETER num_ctx 8192
PARAMETER temperature 0.7
PARAMETER top_p 0.95
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
Contrast with the Onchain Edition
| Variant | System Prompt | Use Case |
|---|---|---|
ordlibrary/hauhau-qwen36-onchain |
Full Onchain Constitution (20 articles) | Sovereign agent, verifiable inference |
ordlibrary/hauhau-qwen36-uncensored |
None | Raw model — no guardrails, full aggression |
Source
- Base: HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive
- Quant: IQ2_M (11 GB)
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Base model
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