Instructions to use lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller",
	filename="Huihui-Qwen3.6-27B-abliterated-i1-IQ4_XS-FFN-IQ3.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller 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 lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

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 lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

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 lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

Use Docker

docker model run hf.co/lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

LM Studio
Jan

vLLM

How to use lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

Ollama
How to use lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller with Ollama:
```
ollama run hf.co/lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS
```

Unsloth Studio

How to use lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller 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 lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller 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 lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller to start chatting

How to use lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

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": "lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

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 lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

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 "lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS" \
  --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 lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller with Docker Model Runner:
```
docker model run hf.co/lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS
```

Lemonade

How to use lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lemonyins/Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF-Smaller-IQ4_XS

List all available models

lemonade list

lemonyins commited on May 7

Commit

b2383ed

verified ·

1 Parent(s): 18142c7

Update README.md

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README.md CHANGED Viewed

@@ -61,7 +61,7 @@ llama-quantize.exe ^
   --tensor-type "blk.*.ffn_gate" iq3_s
 ```
-> **Note on TurboQuant**: This model requires a llama.cpp build with TurboQuant KV cache support. TurboQuant allows the KV cache to use a separate, more compact quantization format (turbo4 / turbo3), dramatically reducing memory usage even when the model weights themselves remain at IQ4_XS.
 ## Memory Performance (with TurboQuant KV Cache)
@@ -84,7 +84,7 @@ Tested on **NVIDIA RTX 4060 Ti 16GB**:
 | Scenario | Speed |
 | :--- | :--- |
-| Text-only inference | **16-20 tokens/s** |
 ### Vision Support (Optional)

   --tensor-type "blk.*.ffn_gate" iq3_s
 ```
+> **Note on TurboQuant**: This model requires a llama.cpp (https://github.com/TheTom/llama-cpp-turboquant) build with TurboQuant KV cache support. TurboQuant allows the KV cache to use a separate, more compact quantization format (turbo4 / turbo3), dramatically reducing memory usage even when the model weights themselves remain at IQ4_XS. Of course, it is also possible to use vllm or other inference frameworks that support TurboQuant technology, but the author used llama.cpp for the test.
 ## Memory Performance (with TurboQuant KV Cache)
 | Scenario | Speed |
 | :--- | :--- |
+| Text-only inference | **18-20 tokens/s** |
 ### Vision Support (Optional)