Instructions to use tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-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 tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-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 tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-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 tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-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 tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-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 tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-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": "tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M

Ollama
How to use tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M
```
Unsloth Desktop

How to use tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-4B-Uncensored-Aggressive-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-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 tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-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 tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-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 "tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-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"

Qwen3.5-4B-Uncensored-Aggressive - GGUF

GGUF quantized versions of rodrigomt/Qwen3.5-4B-Uncensored-Aggressive, a 4.5 billion parameter language model based on the Qwen3.5-4B architecture, optimized for unrestricted text generation and direct instruction following.

Model Details

Base Model: Qwen/Qwen3.5-4B
Fine-tuned by: rodrigomt
Architecture: Qwen2 (28 layers, 28 attention heads)
Context Length: 32768 tokens
Vocabulary Size: 151936
Parameters: 4.5B

Quantization

Filename	Bits	Size	Use Case
`model_f16.gguf`	16	~8.4 GB	Maximum quality, high VRAM requirement
`model_q8_0.gguf`	8	~4.5 GB	High quality, moderate VRAM
`model_q6_k.gguf`	6	~3.4 GB	Good quality, balanced VRAM
`model_q5_k_m.gguf`	5	~2.8 GB	Recommended for most use cases
`model_q5_k_s.gguf`	5	~2.5 GB	Compact, minimal quality loss
`model_q4_k_m.gguf`	4	~2.1 GB	Good balance, low VRAM
`model_q4_k_s.gguf`	4	~1.9 GB	Compact model
`model_q3_k_l.gguf`	3	~1.7 GB	Very compact
`model_q3_k_m.gguf`	3	~1.5 GB	Minimal size
`model_q3_k_s.gguf`	3	~1.4 GB	Extreme compression
`model_q2_k.gguf`	2	~1.1 GB	Maximum compression

VRAM Requirements

Quantization	VRAM	Recommended Device
F16	9 GB	High-end GPU
Q8_0	5 GB	Mid-range GPU
Q6_K	3.5 GB	Mid-range GPU
Q5_K_M	3 GB	Standard GPU
Q4_K_M	2.5 GB	Standard GPU / Laptop GPU
Q3_K_M	2 GB	Laptop GPU / CPU
Q2_K	1.5 GB	CPU with sufficient RAM

Usage

llama.cpp

./main -m model_q5_k_m.gguf -n 256 -p "You are a helpful assistant."

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="model_q5_k_m.gguf",
    n_ctx=32768,
    n_threads=8
)

response = llm("What is machine learning?", max_tokens=256)
print(response["choices"][0]["text"])

LM Studio

Download the quantized GGUF file and load it in LM Studio's model picker.

Ollama

ollama pull tinyopsec/qwen3.5-4b-uncensored-aggressive-gguf:q5_k_m
ollama run tinyopsec/qwen3.5-4b-uncensored-aggressive-gguf:q5_k_m

Model Capabilities

This model excels at:

Unrestricted Generation: Optimized for direct, uncensored text generation without safety filters
Aggressive Instruction Following: Maximized compliance with user instructions and prompts
Diverse Content Generation: Capable of generating a wide range of content types
Lightweight Deployment: Efficient 4.5B parameter architecture suitable for local inference
Research & Experimentation: Designed for exploring model behavior without refusal patterns

License

This quantized version maintains the original model's Apache 2.0 license. See the original model card for full details.

Disclaimer

These are community quantizations. For official support and documentation, refer to the original model repository and llama.cpp documentation.

Downloads last month: -

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for tinyopsec/Qwen3.5-4B-Uncensored-Aggressive-GGUF

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

rodrigomt/Qwen3.5-4B-Uncensored-Aggressive

Quantized

(1)

this model