Instructions to use sphaela/Qwen3.6-27B-AutoRound-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sphaela/Qwen3.6-27B-AutoRound-GGUF",
	filename="Qwen3.6-27B-Q2_K_MIXED.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 sphaela/Qwen3.6-27B-AutoRound-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Ollama:
```
ollama run hf.co/sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
```

Unsloth Studio new

How to use sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sphaela/Qwen3.6-27B-AutoRound-GGUF to start chatting

Pi new

How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sphaela/Qwen3.6-27B-AutoRound-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": "sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-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 sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Docker Model Runner:
```
docker model run hf.co/sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
```

Lemonade

How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-AutoRound-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-27B GGUF (AutoRound Quantized)

This repository contains GGUF quantized versions of Qwen/Qwen3.6-27B created using Intel's AutoRound quantization method.

Quantization Details

The models were quantized using various schemes provided by the auto-round tool. For better compatibility and smaller size, we provide unified multimodal projector (mmproj) files in F16, BF16, and F32 formats.

Files and Sizes

File Name	Quant Type	Size	Description
`Qwen3.6-27B-Q2_K_S.gguf`	Q2_K_S	8.9 GB	Extremely high compression, significant quality loss.
`Qwen3.6-27B-Q2_K_MIXED.gguf`	Q2_K_MIXED	16 GB	Recommended high-compression option. Fast inference.
`Qwen3.6-27B-Q3_K_S.gguf`	Q3_K_S	12 GB	Very high compression, notable quality loss.
`Qwen3.6-27B-Q3_K_M.gguf`	Q3_K_M	12 GB	Balanced 3-bit quantization.
`Qwen3.6-27B-Q3_K_L.gguf`	Q3_K_L	12 GB	High quality 3-bit quantization.
`Qwen3.6-27B-Q4_0.gguf`	Q4_0	15 GB	Standard 4-bit quantization, good balance.
`Qwen3.6-27B-Q4_1.gguf`	Q4_1	16 GB	Higher quality 4-bit quantization than Q4_0.
`Qwen3.6-27B-Q4_K_S.gguf`	Q4_K_S	15 GB	Small 4-bit K-quant, good efficiency.
`Qwen3.6-27B-Q4_K_M.gguf`	Q4_K_M	15 GB	Recommended 4-bit K-quant, excellent balance.
`Qwen3.6-27B-Q5_0.gguf`	Q5_0	18 GB	Standard 5-bit quantization, very high quality.
`Qwen3.6-27B-Q5_1.gguf`	Q5_1	19 GB	Higher quality 5-bit quantization than Q5_0.
`Qwen3.6-27B-Q5_K_S.gguf`	Q5_K_S	18 GB	Small 5-bit K-quant, very high quality.
`Qwen3.6-27B-Q5_K_M.gguf`	Q5_K_M	18 GB	Recommended 5-bit K-quant, near-lossless.
`Qwen3.6-27B-Q6_K.gguf`	Q6_K	21 GB	6-bit K-quant, virtually indistinguishable from F16.
`Qwen3.6-27B-Q8_0.gguf`	Q8_0	29 GB	8-bit quantization, near-lossless.
`mmproj-model-f16.gguf`	F16	885 MB	Unified Projector in Float16 format.
`mmproj-model-bf16.gguf`	BF16	889 MB	Unified Projector in BFloat16 format.
`mmproj-model-f32.gguf`	F32	1.8 GB	Unified Projector in Float32 format.

Generate the Model

The models were generated using Intel's AutoRound with the following command:

auto-round --model Qwen/Qwen3.6-27B --output_dir ./quantized/ --scheme <SCHEME> --iters 0

Usage with llama.cpp

These models can be used with llama.cpp. For multimodal usage, you must specify the projector file:

./llama-cli -m Qwen3.6-27B-Q4_K_M.gguf --mmproj mmproj-model-f16.gguf --image your_image.jpg -p "Describe this image."

About AutoRound

AutoRound is an advanced quantization technique from Intel that aims to minimize accuracy loss through automated rounding optimization.

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GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Base model

Qwen/Qwen3.6-27B

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