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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Anbeeld/Qwen3.6-27B-DFlash-GGUF",
	filename="Qwen3.6-27B-DFlash-IQ4_XS.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 Anbeeld/Qwen3.6-27B-DFlash-GGUF with llama.cpp:

Install from brew

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

Use Docker

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

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

Unsloth Studio new

How to use Anbeeld/Qwen3.6-27B-DFlash-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 Anbeeld/Qwen3.6-27B-DFlash-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 Anbeeld/Qwen3.6-27B-DFlash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use Anbeeld/Qwen3.6-27B-DFlash-GGUF with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Anbeeld/Qwen3.6-27B-DFlash-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 Anbeeld/Qwen3.6-27B-DFlash-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 Anbeeld/Qwen3.6-27B-DFlash-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

How to use Anbeeld/Qwen3.6-27B-DFlash-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Anbeeld commited on 4 days ago

Commit

dabd38f

verified ·

1 Parent(s): 5b5dfe9

Upload README.md with huggingface_hub

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README.md +2 -2

README.md CHANGED Viewed

@@ -8,11 +8,11 @@ Tested with [BeeLlama.cpp v0.2.0](https://github.com/Anbeeld/beellama.cpp) — a
 * Target model: [Qwen 3.6 27B Q5_K_S](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF)
 * Setup: Windows 11, AMD Ryzen 7 5700X3D, 32 GB DDR4 RAM, RTX 3090 24 GB
-* Config: same as recommended in [quick start docs](https://github.com/Anbeeld/beellama.cpp/blob/main/docs/quickstart-qwen36-dflash.md), but with adaptive DM disabled
 * Baseline is llama.cpp [b9275](https://github.com/ggml-org/llama.cpp/releases/tag/b9275) CUDA 13.1 Windows prebuilt: 36.8 tok/s median
 <details>
-<summary>Prompt: Doubly-linked list</summary>
 Write a complete Python 3 module implementing a doubly-linked list with the following methods: append, prepend, insert_at, remove_at, find, reverse, to_list, length, is_empty, iter. Include comprehensive docstrings, type hints, and pytest unit tests for every method. Return only the code, no commentary.

 * Target model: [Qwen 3.6 27B Q5_K_S](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF)
 * Setup: Windows 11, AMD Ryzen 7 5700X3D, 32 GB DDR4 RAM, RTX 3090 24 GB
+* Config: same as in [quick start docs](https://github.com/Anbeeld/beellama.cpp/blob/main/docs/quickstart-qwen36-dflash.md), but with reasoning and adaptive DM disabled
 * Baseline is llama.cpp [b9275](https://github.com/ggml-org/llama.cpp/releases/tag/b9275) CUDA 13.1 Windows prebuilt: 36.8 tok/s median
 <details>
+<summary>Prompt: Doubly-linked list (output: ~4K tok)</summary>
 Write a complete Python 3 module implementing a doubly-linked list with the following methods: append, prepend, insert_at, remove_at, find, reverse, to_list, length, is_empty, iter. Include comprehensive docstrings, type hints, and pytest unit tests for every method. Return only the code, no commentary.