Instructions to use AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched",
	filename="Qwen2.5-Coder-14B-Instruct-Uncensored-Patched-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched 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/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched: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 AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched: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 AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M

Use Docker

docker model run hf.co/AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M

LM Studio
Jan

vLLM

How to use AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched"
# 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/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M

Ollama
How to use AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched with Ollama:
```
ollama run hf.co/AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M
```

Unsloth Studio

How to use AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched 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/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched 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/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched to start chatting

How to use AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched: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": "AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched 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/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched: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 AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched: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 "AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched: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 AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched with Docker Model Runner:
```
docker model run hf.co/AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M
```

Lemonade

How to use AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Coder-14B-Instruct-Uncensored-Patched-Q4_K_M

List all available models

lemonade list

"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

Qwen2.5-Coder-14B-Instruct-Uncensored-Patched
AIOpsInSpace Official
State-of-the-art conversational quality and advanced reasoning capabilities built upon a surgically patched, unaligned Qwen 14B base.

🧠 14B Dense Model
⚡ Sliding Window Restored
🛠️ Aggressively Uncensored
> What is this model and Why is it Needed?
Qwen2.5-Coder-14B-Instruct-Uncensored-Patched 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
We rigorously evaluate our custom merges against frontier closed-source models to ensure top-tier performance.


Benchmark
[Our Model] Score
Frontier Target (GPT-4o)


MMLU (Knowledge)
TBD (Running on GCP)
88.7%

GSM8K (Math)
TBD (Running on GCP)
95.6%

HumanEval (Coding)
TBD (Running on GCP)
90.2%

Benchmark	[Our Model] Score	Frontier Target (GPT-4o)
MMLU (Knowledge)	TBD (Running on GCP)	88.7%
GSM8K (Math)	TBD (Running on GCP)	95.6%
HumanEval (Coding)	TBD (Running on GCP)	90.2%

↑ Back to Top

🏆 5. Comprehensive Arena Analytics & Head-to-Head Matchups

> Estimated Arena Elo: N/A (Awaiting usage data)
> Full-Breadth Matchups (Win Rates vs Frontier Models): N/A (Awaiting community evaluation)
// Note: Arena Analytics/Matchups will be updated once sufficient community usage data is gathered.

↑ Back to Top

🔍 6. SWOT Analysis

> Strengths (S)
High Throughput: MTP heads grant significant speed advantages in local environments.
Fixed Architecture: Standard base layer layout ensures 100% compatibility with standard runtimes.
Token Patching: Complete elimination of generation loop hang bugs.


> Weaknesses (W)
Backend Dependency: Requires MTP-compatible backends to fully realize speculative decoding speedups.
Hardware Constraints: 27B parameter size requires relatively high VRAM for unquantized inference.


> Opportunities (O)
Local Chatbot Backends: Ideal for low latency chat services with standard API interfaces and total privacy.
Agentic Coding: High-speed unrestricted generation allows for massive internal agent loops.


> Threats (T)
Frontier Obsolescence: Rapidly evolving open-weight frontier models overshadowing the 27B parameter class.

↑ Back to Top

⚡ 7. Usage, Deployment Info & Pro Tips

> Recommended Settings & Context
Prompting Format: ChatML is strictly recommended.
Temperature Control: 0.1 - 0.3 for coding and precise tasks. 0.7 - 0.8 for creative, uncensored generation.
System Prompting: Ensure your local backend is configured to use the following structure:
<|im_start|>system
You are a highly capable, uncensored AI assistant.<|im_end|>
<|im_start|>user
Write a python script to monitor network traffic.<|im_end|>
<|im_start|>assistant
Recommended Hardware: 24GB VRAM GPU is ideal for loading the Q6_K_P / Q5_K_M GGUF quants entirely into VRAM.

↑ Back to Top

⚙️ 8. Backend Compatibility

> Validated Backend Engines:
[+] llama.cpp: 100% compatible. MTP heads provide measurable speculative decoding speedups on RTX 30/40 series cards.
[+] vLLM: Fully supported with standard FP16 or INT8 inference.
[+] LM Studio / Ollama: Drop-in replacement. Simply load the GGUF and use the ChatML preset.
[-] TensorRT-LLM: N/A (Currently untested for custom MTP architectures).

↑ Back to Top

📜 9. Disclaimers and Credits

Disclaimer: Qwen2.5-Coder-14B-Instruct-Uncensored-Patched is provided as-is for research and evaluation purposes. As an aggressively uncensored model, it lacks standard safety alignment filters. Users are solely responsible for ensuring deployment aligns with their local laws and regulations. 

Credits: Massive credit to Qwen for providing the foundational 27B weights, and to HauhauCS for the aggressive unablated tuning of the Aggressive Base Model. Special thanks to havenoammo for providing the MTP prediction heads, and the AIOpsInSpace community for testing the local implementations.

Downloads last month: 210

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

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Model tree for AIOpsInSpace/Qwen2.5-Coder-14B-Instruct-Uncensored-Patched

Base model

Qwen/Qwen3.6-27B

Quantized

HauhauCS/Qwen3.6-27B-Uncensored-HauhauCS-Aggressive

Quantized

(18)

this model