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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="aaardpark/Qwen3.6-27B-GGUF",
	filename="qwen3.6-27B-aaardpark-uniform-Q3_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use aaardpark/Qwen3.6-27B-GGUF 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 aaardpark/Qwen3.6-27B-GGUF
# Run inference directly in the terminal:
llama cli -hf aaardpark/Qwen3.6-27B-GGUF

Install from WinGet (Windows)

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

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 aaardpark/Qwen3.6-27B-GGUF
# Run inference directly in the terminal:
./llama-cli -hf aaardpark/Qwen3.6-27B-GGUF

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 aaardpark/Qwen3.6-27B-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aaardpark/Qwen3.6-27B-GGUF

Use Docker

docker model run hf.co/aaardpark/Qwen3.6-27B-GGUF

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

Unsloth Studio

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

Using HuggingFace Spaces for Unsloth

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

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

Start the llama.cpp server

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

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": "aaardpark/Qwen3.6-27B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use aaardpark/Qwen3.6-27B-GGUF with Hermes Agent:

Start the llama.cpp server

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

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 aaardpark/Qwen3.6-27B-GGUF

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.Qwen3.6-27B-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.6-27B-GGUF / README.md

aaardpark

Update README.md

70aa6e4 verified 2 months ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen3.6-27B
	tags:
	- quantized
	- gguf
	- 3-bit
	- qwen3.6
	- aard-q3
	model_type: qwen3_6
	quantized_by: aaardpark
	---

	# Qwen3.6-27B \| aard-Q3

	11 GB of Qwen 3.6-27B

	\| \| GSM8K (50 problems, 5-shot) \|
	\|---\|---\|
	\| FP16 (full precision) \| 49 / 50 = 98% \|
	\| aard-Q3 (this file) \| 47 / 50 = 94% \|

	96% of the model's reasoning capacity carries through 3-bit compression.

	## Why this 3-bit pack isn't bad

	I've been messing around with a new quant method that handles 3 bit pretty well using a new method to preserve important weights.

	## Three sample outputs

	Math:
	> Q: What is 5/8 of 2/3?
	>
	> A: To find 5/8 of 2/3, multiply the two fractions:
	> 5/8 × 2/3 = (5 × 2)/(8 × 3) = 10/24 = 5/12.

	Factual:
	> Q: What year did the Treaty of Westphalia end the Thirty Years' War, and name two of its key provisions.
	>
	> A: The Peace of Westphalia was signed in 1648, ending the Thirty Years' War. Two key provisions:
	> 1. State sovereignty — each state had exclusive right to govern its internal affairs (religion and military), laying the foundation for the modern sovereign-state system.
	> 2. Religious tolerance — extended the Peace of Augsburg to recognize Calvinism alongside Lutheranism and Catholicism, ending decades of religious warfare in the Holy Roman Empire.

	Code debug:
	> Q: Find the bug in this Python:
	> ```python
	> def merge(a, b):
	> result = []
	> i, j = 0, 0
	> while i < len(a) and j < len(b):
	> if a[i] < b[j]:
	> result.append(a[i])
	> i += 1
	> else:
	> result.append(b[j])
	> i += 1
	> result.extend(a[i:])
	> result.extend(b[j:])
	> return result
	> ```
	>
	> A: The bug is in the `else` branch. When `a[i] >= b[j]`, the code appends `b[j]` but increments `i` instead of `j`. The wrong pointer advances, later iterations re-read `b[j]`, and the function eventually walks off the end of `a`. Fix: change `i += 1` in the `else` branch to `j += 1`.

	## Quick stats

	\| \| \|
	\|---\|---\|
	\| File \| `qwen3.6-27B-aaardpark-uniform-Q3_K.gguf` \|
	\| Size \| 11 GB \|
	\| Format \| GGUF, uniform Q3_K (497 × Q3_K + 1 × Q6_K, 3.59 BPW) \|
	\| Min VRAM \| 16 GB \|
	\| Throughput \| ~30 tok/s on Apple M-series \|
	\| Native context \| 262K (Gated Delta Net linear attention) \|

	## Run it

	```bash
	huggingface-cli download aaardpark/Qwen3.6-27B-GGUF \
	qwen3.6-27B-aaardpark-uniform-Q3_K.gguf --local-dir .

	llama-cli -m qwen3.6-27B-aaardpark-uniform-Q3_K.gguf -ngl 99 -c 32768
	```

	Qwen 3.6 is a thinking model — it emits a `<think>…</think>` block before the final answer. Budget at least 2048 tokens (4096 for hard reasoning), or set `enable_thinking=False` if your runtime supports it. Needs llama.cpp build 8670 or later.

	## More from aaardpark

	- [Qwen 3.5 27B GGUF](https://huggingface.co/aaardpark/Qwen3.5-27B-GGUF) — 11 GB, 96% GSM8K
	- [gemma-4-31B-it](https://huggingface.co/aaardpark/gemma-4-31B-it-GGUF) — 15.3 GB, 96% GSM8K
	- [Qwen 2.5 72B Instruct](https://huggingface.co/aaardpark/Qwen2.5-72B-Instruct-GGUF) — 35 GB, 88% GSM8K
	- [Qwen 2.5 32B Instruct](https://huggingface.co/aaardpark/Qwen2.5-32B-Instruct-GGUF) — 15 GB