Instructions to use AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP 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 AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M
# Run inference directly in the terminal:
llama cli -hf AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M
# Run inference directly in the terminal:
llama cli -hf AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP: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 AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP: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 AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M

Use Docker

docker model run hf.co/AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M

LM Studio
Jan

vLLM

How to use AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M

Ollama
How to use AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP with Ollama:
```
ollama run hf.co/AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M
```

Unsloth Studio

How to use AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP 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 AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP 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 AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP to start chatting

How to use AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP: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": "AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP: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 "AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP: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 AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP with Docker Model Runner:
```
docker model run hf.co/AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M
```

Lemonade

How to use AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP-IQ2_M

List all available models

lemonade list

Hermes Agent

How to use AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP: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 AIOpsInSpace/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP:IQ2_M

Run Hermes

hermes

Atomic Chat

"This is humanity's race.

The solution is open source.

Stay sovereign."

— AIOpsInSpace

📑 Table of Contents
🎯 1. Executive Summary
🏗️ 2. Architecture & Merging
🚀 3. Technical Enhancements
📊 4. Benchmark Competitiveness
🏆 5. Arena Analytics
🔍 6. SWOT Analysis
⚡ 7. Usage & Deployment
⚙️ 8. Backend Compatibility
📜 9. Disclaimers & Credits

Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP
AIOpsInSpace Official
State-of-the-art conversational quality with significantly enhanced speculative decoding speeds via grafted MTP heads on an aggressively uncensored base.

🧠 27B Dense Model
⚡ MTP Speculative Decoding
🛠️ Aggressively Uncensored
> What is this model and Why is it Needed?
Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP is a custom-merged, high-performance variant built on top of the HauhauCS Aggressive Base and Qwen 3.6 27B architecture.

Why it is needed: Most open-source models suffer from over-alignment or struggle with generation loop hang bugs in local environments. This model was created to provide a completely uncensored, reasoning-first experience capable of operating in complex coding workflows without refusal walls, all while drastically accelerating inference speed using Multi-Token Prediction (MTP).
> From the Parent Repository
"A highly volatile, purely reasoning-driven intelligence. The alignment layer has been surgically ablated, leaving only raw mathematical deduction and uninhibited creative generation. Use with extreme caution."


— HauhauCS Aggressive Base

↑ Back to Top

🏗️ 2. Model Architecture & Merging

Architecture: Qwen 3.6 27B Transformer with Multi-Token Prediction (MTP) Heads

Merging Technique: Surgical Tensor Grafting

Constituent Models:
Model A: HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive
Model B: havenoammo MTP Prediction Heads

Methodology: We utilized a surgical tensor merge to fuse MTP prediction heads directly into the unablated base weights. The MTP heads are perfectly aligned with the base layers, preventing common SSM layout violations (blk.N.nextn.* tensor mismatches).

↑ Back to Top

🚀 3. Technical Enhancements

> Key Upgrades Over Base Model:
Uncensored Freedom: The aggressive base model removes artificial guardrails, making it ideal for unfiltered creative writing, unrestricted coding tasks, and robust roleplay.
MTP Integration: Enables the model to predict multiple future tokens simultaneously, drastically reducing time-to-first-token (TTFT) and accelerating continuous generation on compatible local backends.
BOS/EOG Token Patches: The tokenizer has been hard-patched to map bos_token_id and special_eog_ids. This completely eliminates notorious generation loop hang bugs in local inferences.

↑ Back to Top

📊 4. Benchmark Competitiveness vs. Frontier Scores

    > Evaluated Performance



Benchmark
Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP
Frontier Target


MMLU
Evaluated
88.7%

GSM8K
Evaluated
95.6%

HumanEval
Evaluated
90.2%

Benchmark	Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP	Frontier Target
MMLU	Evaluated	88.7%
GSM8K	Evaluated	95.6%
HumanEval	Evaluated	90.2%

↑ Back to Top

🏆 5. Comprehensive Arena Analytics

> Status: Active Community Benchmarking
// Note: Arena Elo and head-to-head winrates updated continuously as evaluation telemetry processes.

↑ Back to Top

🔍 6. SWOT Analysis

> Strengths (S)
🛡️ Uncensored Fidelity: Surgically patched to ensure maximum generation throughput without alignment overhead.
⚡ Optimized Engine: Advanced mechanics ensure zero context fragmentation or execution hangs.


> Weaknesses (W)
📉 Hardware Limits: Requires sufficient VRAM/RAM for higher precision GGUF quantizations.


> Opportunities (O)
🎯 Local Sovereign Agents: Perfect for offline, private reasoning and agentic workflows.


> Threats (T)
⚠️ Sampler Sensitivity: High temperatures may require repetition penalty adjustments.

↑ Back to Top

⚡ 7. Usage & Deployment Info

> Recommended Settings
Temperature: 0.2 - 0.7
Top-P: 0.95
Backend Engines: Compatible with llama.cpp, vLLM, Ollama, LM Studio, KoboldCPP

↑ Back to Top

⚙️ 8. Backend Compatibility

> Validated Engines:
[+] llama.cpp: Native support across all quantizations.
[+] Ollama / LM Studio: Full GGUF compatibility.

↑ Back to Top

📜 9. Disclaimers & Credits

Disclaimer: Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-MTP is provided for research and sovereign local deployment. As an unaligned model, users are responsible for ensuring usage complies with local laws.

Credits: Gratitude to original base model authors (HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive) and open-source AI community tools.