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

Libraries

How to use nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF",
	filename="Qwen3.5-9B-Uncensored-nothink-Q4_K_M.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 nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nandukmelath/Qwen3.5-9B-Uncensored-nothink-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": "nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF:Q4_K_M

Ollama
How to use nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF with Ollama:
```
ollama run hf.co/nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF:Q4_K_M
```

Unsloth Studio

How to use nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF 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 nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF 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 nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nandukmelath/Qwen3.5-9B-Uncensored-nothink-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 nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF with Docker Model Runner:
```
docker model run hf.co/nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF:Q4_K_M
```

Lemonade

How to use nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-Uncensored-nothink-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-9B Uncensored — No-Think Edition (GGUF)

⚡ Zero refusals. Zero thinking delay. 100% local.

This is a patched GGUF of HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive with one key modification: thinking is disabled at the GGUF template level, giving you instant responses without the 15–30 second reasoning delay.

What's different

Qwen3.5 is a thinking model. By default it outputs a <think>...</think> block before every response. This is great for hard problems but brutal for everyday use — you wait 20 seconds for a simple answer.

This model patches the embedded Jinja2 chat template to always output an empty think block:

Original flow:  <think> [400 tokens] </think> → answer    (~25s wait)
This model:     <think></think> → answer                  (<1s wait)

The model's intelligence is encoded in its weights, not the thinking trace. Quality is the same. Speed is 25x better for time-to-first-token.

Want reasoning on demand? Add /think to any message — the model will reason through it fully for that turn only.

Model details

Property	Value
Base	Qwen3.5-9B
Fine-tune	HauhauCS Uncensored Aggressive
Quantization	Q4_K_M
Context	Up to 65,536 tokens
Parameters	9B
Format	GGUF
Refusal rate	0%

Benchmarks (MacBook Pro M2 Pro, 16 GB)

Metric	Value
Generation speed	~22–25 tok/s
Time to first token	< 1 second
Context window	65,536 tokens
VRAM usage	~8.5 GB

How to use

LM Studio (recommended)

Download the Q4_K_M file below
Load in LM Studio with --context-length 65536 --gpu max
Done — no config needed, thinking is already patched off

Optimal sampling (Qwen3 official recommended)

Temperature: 0.6
Top-P: 0.95
Top-K: 20
Repeat penalty: 1.0
Max tokens: 4096

llama.cpp

./llama-cli -m Qwen3.5-9B-Uncensored-nothink-Q4_K_M.gguf \
  --ctx-size 65536 \
  --n-gpu-layers 99 \
  -p "Your prompt here"

Full automated setup for Mac

👉 github.com/nandukmelath/lmstudio-uncensored-setup

One command: VRAM boost + auto-start + model load + Hermes Agent config:

git clone https://github.com/nandukmelath/lmstudio-uncensored-setup
cd lmstudio-uncensored-setup && ./scripts/setup.sh

How the patch works

The Qwen3.5 GGUF contains an embedded Jinja2 chat template with this block:

{%- if enable_thinking is defined and enable_thinking is false %}
    {{- '<think>\n\n</think>\n\n' }}
{%- else %}
    {{- '<think>\n' }}
{%- endif %}

The patch replaces it with just:

{{- '<think>\n\n</think>\n\n' }}

Same file size (padded with spaces), same structure, zero thinking overhead. The patcher script is open source: patch_nothink.py

Credits

Base model: Qwen/Qwen3.5-9B by Alibaba Cloud (Apache 2.0)
Uncensored fine-tune: HauhauCS (Apache 2.0)
No-think patch & automated setup: @nandukmelath

License

Apache 2.0

Downloads last month: 105

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for nandukmelath/Qwen3.5-9B-Uncensored-nothink-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

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

Quantized

(8)

this model