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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp",
	filename="Qwen3.6-27B-DFlash-BF16.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 Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp 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 Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q4_K_M

LM Studio
Jan

vLLM

How to use Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q4_K_M

Ollama
How to use Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp with Ollama:
```
ollama run hf.co/Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q4_K_M
```

Unsloth Studio

How to use Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp 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 Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp 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 Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp: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 Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp with Docker Model Runner:
```
docker model run hf.co/Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q4_K_M
```

Lemonade

How to use Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-DFlash-GGUF-llama.cpp-Q4_K_M

List all available models

lemonade list

Qwen3.6-27B-DFlash

GGUF quantizations of z-lab/Qwen3.6-27B-DFlash.

Converted to BF16 using convert_hf_to_gguf.py, then quantized using llama-quantize from llama.cpp.

Available quants

Quant	Bits	Size	Notes
Q4_K_M	4	~1.03 GB	Average quality
Q5_K	5	~1.22 GB	High quality
Q6_K	6	~1.43 GB	Very high quality
Q8_0	8	~1.84 GB	Highest quality, near lossless, Recommended
BF16	16	~3.47 GB	Full precision, reference file

Usage

Use in conjunction with existing Qwen3.6 Quants, example config if using llama-server:

[Qwen3.6-27B-Q8_0-DFlash]
sm = layer
model = /mnt/gguf/Qwen3.6-27B/Qwen3.6-27B-Q8_0.gguf
model-draft = /mnt/gguf/Qwen3.6-27B/Qwen3.6-27B-DFlash-Q8_0.gguf
spec-type = draft-dflash
spec-draft-n-max = 6

(Note: For some reason I cannot get sm = tensor to work, it crashes on launch, pretty sure this is an issue in llama.cpp)

Original model

See the original model card for details on capabilities, benchmarks, and license.

Downloads last month: 15

GGUF

Model size

2B params

Architecture

dflash

Hardware compatibility

4-bit

6-bit

8-bit

16-bit

Model tree for Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp

Base model

z-lab/Qwen3.6-27B-DFlash

Quantized

(10)

this model

Collection including Alittlehammmer/Qwen3.6-27B-DFlash-GGUF-llama.cpp

llama.cpp DFlash Draft Models

Collection

Quantized GGUF's of z-lab's DFlash Draft Models for use in llama.cpp • 4 items • Updated 1 day ago