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
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

9d1ce98

verified ·

1 Parent(s): 1f75cd6

Update README.md

Browse files

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README.md +2 -1

README.md CHANGED Viewed

@@ -10,6 +10,7 @@ tags:
 - TurboQuant
 - gguf
 - IQ4_XS
 ---
 # Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF(Smaller)
@@ -163,4 +164,4 @@ llama-server.exe ^
 - **[cHunter789](https://huggingface.co/cHunter789)** — for the `attn_qkv → IQ4_XS` fix and the original fully optimized IQ4_XS GGUF
 - **[mradermacher](https://huggingface.co/mradermacher)** — for the base abliterated model and imatrix
 - **[TheTom](https://github.com/TheTom)** — for [llama-cpp-turboquant](https://github.com/TheTom/llama-cpp-turboquant), the TurboQuant KV cache implementation
-- **[llama.cpp](https://github.com/ggml-org/llama.cpp)** — the ggml/llama.cpp team for the base quantization framework

 - TurboQuant
 - gguf
 - IQ4_XS
+pipeline_tag: image-text-to-text
 ---
 # Qwen3.6-27B-uncensored-abliterated-i1-IQ4_XS-GGUF(Smaller)
 - **[cHunter789](https://huggingface.co/cHunter789)** — for the `attn_qkv → IQ4_XS` fix and the original fully optimized IQ4_XS GGUF
 - **[mradermacher](https://huggingface.co/mradermacher)** — for the base abliterated model and imatrix
 - **[TheTom](https://github.com/TheTom)** — for [llama-cpp-turboquant](https://github.com/TheTom/llama-cpp-turboquant), the TurboQuant KV cache implementation
+- **[llama.cpp](https://github.com/ggml-org/llama.cpp)** — the ggml/llama.cpp team for the base quantization framework