Instructions to use DJLougen/Qwable-5-27B-Coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DJLougen/Qwable-5-27B-Coder-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DJLougen/Qwable-5-27B-Coder-GGUF",
	filename="Qwable-5-27B-Coder-IQ1_S.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 DJLougen/Qwable-5-27B-Coder-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DJLougen/Qwable-5-27B-Coder-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DJLougen/Qwable-5-27B-Coder-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DJLougen/Qwable-5-27B-Coder-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DJLougen/Qwable-5-27B-Coder-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DJLougen/Qwable-5-27B-Coder-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": "DJLougen/Qwable-5-27B-Coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

Ollama
How to use DJLougen/Qwable-5-27B-Coder-GGUF with Ollama:
```
ollama run hf.co/DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
```

Unsloth Studio

How to use DJLougen/Qwable-5-27B-Coder-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 DJLougen/Qwable-5-27B-Coder-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 DJLougen/Qwable-5-27B-Coder-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DJLougen/Qwable-5-27B-Coder-GGUF to start chatting

How to use DJLougen/Qwable-5-27B-Coder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DJLougen/Qwable-5-27B-Coder-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": "DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DJLougen/Qwable-5-27B-Coder-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 DJLougen/Qwable-5-27B-Coder-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 DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use DJLougen/Qwable-5-27B-Coder-GGUF with Docker Model Runner:
```
docker model run hf.co/DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
```

Lemonade

How to use DJLougen/Qwable-5-27B-Coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwable-5-27B-Coder-GGUF-Q4_K_M

List all available models

lemonade list

Qwable-5-27B-Coder-GGUF / README.md

DJLougen

Rewrite GGUF card: point to base debrief, honest quant notes

0d8c134 verified 5 days ago

preview code

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metadata

license: apache-2.0
base_model: DJLougen/Qwable-5-27B-Coder
tags:
  - code
  - agentic
  - distillation
  - demonstration
  - gguf
  - quantized
language:
  - en
pipeline_tag: text-generation

Qwable-5-27B-Coder-GGUF

GGUF quantizations of DJLougen/Qwable-5-27B-Coder.

Update (2026-06-22): Read the base model card before using these. The original release was deliberately under-documented as part of a point about hype versus evidence in local AI. The full recipe and rationale are now on the base card.

What this actually is

GGUF builds of a Qwen3.6-27B base that was post-trained on 10 traces total (5 from a Fable 5 dataset, 5 generated by Kimi 2.7 Coder) in roughly 3 minutes on a single DGX Spark. That is the entire recipe.

It was released to demonstrate how little work it takes to make a model look credible through framing alone, and these quants exist so the demonstration reaches the people who run local in llama.cpp / Ollama / LM Studio.

Why this exists

See the base model card. Short version: as local AI grows, the community has to reward measured evidence over hype, buzzword names, and impressive teacher names. This release is a worked example of the failure mode.

What you should actually do

Test it yourself rather than trusting the card or the teacher names.
Demand real evals: data volume and methodology, not just "distilled from {impressive model}."
Be skeptical of version-numbered names and benchmark-maxxing.
Prefer reproducible, hardware-specific open evals.

Intended use

Educational and illustrative. Not recommended for production coding. No methodology-backed benchmark numbers are provided, by design.

Quantization notes

Fill in the exact quant types you shipped.

Quant	Approx size	Notes
Q4_K_M	TBD
Q5_K_M	TBD
Q6_K	TBD
Q8_0	TBD

Quantization further compounds the caveat on the base card: at n=10 the behavioral delta over base is already narrow and underdetermined, and low-bit quants will shift it further. Do not generalize any apparent strength.

Attribution

Base model: Qwen3.6-27B (see its card for license and terms)
Fine-tune: DJLougen/Qwable-5-27B-Coder
Seed data: Fable 5 dataset, Kimi 2.7 Coder generations