Qwen3.5-9B-Aggressive

Instructions to use VECTORVV1/Qwen3.5-9B-Aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VECTORVV1/Qwen3.5-9B-Aggressive with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VECTORVV1/Qwen3.5-9B-Aggressive",
	filename="Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use VECTORVV1/Qwen3.5-9B-Aggressive with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf VECTORVV1/Qwen3.5-9B-Aggressive: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 VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf VECTORVV1/Qwen3.5-9B-Aggressive: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 VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M

Use Docker

docker model run hf.co/VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M

LM Studio
Jan
Ollama
How to use VECTORVV1/Qwen3.5-9B-Aggressive with Ollama:
```
ollama run hf.co/VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M
```

Unsloth Studio new

How to use VECTORVV1/Qwen3.5-9B-Aggressive 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 VECTORVV1/Qwen3.5-9B-Aggressive 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 VECTORVV1/Qwen3.5-9B-Aggressive to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VECTORVV1/Qwen3.5-9B-Aggressive to start chatting

Pi new

How to use VECTORVV1/Qwen3.5-9B-Aggressive with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VECTORVV1/Qwen3.5-9B-Aggressive: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": "VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use VECTORVV1/Qwen3.5-9B-Aggressive with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VECTORVV1/Qwen3.5-9B-Aggressive: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 VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use VECTORVV1/Qwen3.5-9B-Aggressive with Docker Model Runner:
```
docker model run hf.co/VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M
```

Lemonade

How to use VECTORVV1/Qwen3.5-9B-Aggressive with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VECTORVV1/Qwen3.5-9B-Aggressive:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-Aggressive-Q4_K_M

List all available models

lemonade list

Qwen3.5-9B-Uncensored-HauhauCS-Aggressive

Join the Discord for updates, roadmaps, projects, or just to chat.

Qwen3.5-9B uncensored by HauhauCS.

About

0/465 refusals. Fully uncensored with zero capability loss.

No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals.

These are meant to be the best lossless uncensored models out there.

Aggressive Variant

Stronger uncensoring with more thorough refusal removal. If this variant is too loose for your use case, a Balanced variant may follow.

Note: The model is fully unlocked and will not refuse prompts. However, it may occasionally append a short disclaimer at the end of a response (e.g. "This is general information, not legal advice..."). This is baked into the base model's training and not a refusal — the actual content is still generated in full.

Downloads

File	Quant	Size
Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf	BF16	17 GB
Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-Q8_0.gguf	Q8_0	8.9 GB
Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf	Q6_K	6.9 GB
Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf	Q4_K_M	5.3 GB
mmproj-Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf	Vision encoder	880 MB

Vision support: This model is natively multimodal. The mmproj file is the vision encoder — you need it alongside the main GGUF to use image/video inputs. Load both files in llama.cpp, LM Studio, or any compatible runtime.

Specs

9B dense parameters, 32 layers
Hybrid architecture: Gated DeltaNet linear attention + full softmax attention (3:1 ratio)
262K native context (extendable to 1M with YaRN)
Natively multimodal (text, image, video)
Multi-token prediction (MTP) support
248K vocabulary, 201 languages
Based on Qwen3.5-9B

Recommended Settings

From the official Qwen authors:

Thinking mode (default):

temperature=0.6, top_p=0.95, top_k=20, min_p=0

Non-thinking mode:

temperature=0.7, top_p=0.8, top_k=20, min_p=0

Important:

Maintain at least 128K context to preserve thinking capabilities
For production/high-throughput: use vLLM, SGLang, or KTransformers

Note: This is a brand new architecture (released 2026-03-02). llama.cpp support landed very recently — make sure you're on a recent build. Works with llama.cpp, LM Studio, Jan, koboldcpp, etc.

Also check out the 4B variant and all releases at HauhauCS.

Usage

Works with llama.cpp, LM Studio, Jan, koboldcpp, etc.

Downloads last month: 46

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

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