Instructions to use liodon-ai/Qwable-3.6-27b-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liodon-ai/Qwable-3.6-27b-imatrix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="liodon-ai/Qwable-3.6-27b-imatrix-GGUF",
	filename="Qwable-3.6-27b-Q2_K.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 liodon-ai/Qwable-3.6-27b-imatrix-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 liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/Qwable-3.6-27b-imatrix-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 liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf liodon-ai/Qwable-3.6-27b-imatrix-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 liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use liodon-ai/Qwable-3.6-27b-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liodon-ai/Qwable-3.6-27b-imatrix-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liodon-ai/Qwable-3.6-27b-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M

Ollama
How to use liodon-ai/Qwable-3.6-27b-imatrix-GGUF with Ollama:
```
ollama run hf.co/liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M
```

Unsloth Studio

How to use liodon-ai/Qwable-3.6-27b-imatrix-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 liodon-ai/Qwable-3.6-27b-imatrix-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 liodon-ai/Qwable-3.6-27b-imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for liodon-ai/Qwable-3.6-27b-imatrix-GGUF to start chatting

How to use liodon-ai/Qwable-3.6-27b-imatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf liodon-ai/Qwable-3.6-27b-imatrix-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": "liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use liodon-ai/Qwable-3.6-27b-imatrix-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 liodon-ai/Qwable-3.6-27b-imatrix-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 liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use liodon-ai/Qwable-3.6-27b-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use liodon-ai/Qwable-3.6-27b-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwable-3.6-27b-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Qwable-3.6-27b — iMatrix GGUF

The first iMatrix GGUF for Qwable-3.6-27b — Q2_K through Q8_0 with importance matrix calibration.

Qwable-3.6-27b is a fine-tune of Qwen 27B trained on Fable 5-style reasoning traces — it thinks before answering, with structured deliberate responses optimized for code and technical tasks.

These GGUFs are produced from the F16 source using importance matrix (iMatrix) calibration on 2M tokens of wikitext-103. iMatrix identifies which weights matter most during inference and protects them during quantization — the result is noticeably better coherence at Q2/Q3/Q4.

Quick Start

llama.cpp

llama-cli -hf liodon-ai/Qwable-3.6-27b-imatrix-GGUF:Q4_K_M

LM Studio / Jan

Search liodon-ai/Qwable-3.6-27b-imatrix-GGUF and pick your quant.

Available Quants

Quant	Size	VRAM	Notes
`Q2_K`	10.9 GB	9 GB	tiniest — runs almost anywhere, iMatrix-improved
`Q3_K_M`	13.5 GB	11 GB	great for 8GB VRAM, iMatrix-improved
`Q4_K_M`	16.8 GB	14 GB	sweet spot (recommended), iMatrix-improved
`Q5_K_M`	19.5 GB	18 GB	high quality, iMatrix-improved
`Q6_K`	22.4 GB	20 GB	near-lossless, iMatrix-improved
`Q8_0`	29.0 GB	28 GB	basically full quality

Why iMatrix for Qwable?

Qwable uses chain-of-thought reasoning — it emits long <think> traces before answering. At low-bit quantization, coherence over long sequences matters more than for simple Q&A models. iMatrix protects the weights that sustain long reasoning chains, giving noticeably better output at Q2_K and Q3_K_M compared to standard quantization.

What is iMatrix?

Standard quantization rounds all weights equally. iMatrix:

Runs calibration text through the full-precision model
Measures which weights activate most (the "importance matrix")
Allocates more precision to important weights, less to unimportant ones

Same file size. Better output. Most noticeable at Q2/Q3/Q4.

Calibration

Importance matrix computed from 2M tokens of wikitext-103 — 128 calibration chunks.

Source Model

Original: Mia-AiLab/Qwable-3.6-27b — 22.9K downloads
Architecture: Qwen3.5 27B fine-tuned on Fable 5 reasoning traces
Strengths: Code, debugging, technical reasoning, structured tasks
License: MIT

Downloads last month: 326

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for liodon-ai/Qwable-3.6-27b-imatrix-GGUF

Base model

Qwen/Qwen3.6-27B

Finetuned

unsloth/Qwen3.6-27B

Quantized

Mia-AiLab/Qwable-3.6-27b

Quantized

(5)

this model