Instructions to use aaardpark/Qwen3.6-27B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- Pi
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
File size: 3,232 Bytes
8a2ae5d 70aa6e4 8a2ae5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | ---
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
|