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

Redesign GGUF model card

763ee7d verified 10 days ago

preview code

Raw

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	---
	license: apache-2.0
	library_name: gguf
	pipeline_tag: text-generation
	base_model:
	- DJLougen/Qwable-5-27B-Coder
	base_model_relation: quantized
	language:
	- en
	tags:
	- gguf
	- llama.cpp
	- qwen
	- qwen3_6
	- qwen3_5
	- coder
	- coding-agent
	- agentic-coding
	- tool-use
	- repository-work
	- terminal-workflows
	- long-context
	- imatrix
	- qlora
	---

	<p align="center">
	<img src="./assets/banner.jpeg" alt="Qwable-5-27B-Coder banner" style="width:100%;max-width:1024px;border-radius:18px;" />
	</p>

	<p align="center">
	<img src="./assets/trace-board.svg" alt="Qwable GGUF quant runway" style="width:100%;max-width:1100px;border-radius:22px;" />
	</p>

	# Qwable-5-27B-Coder-GGUF

	Qwable-5-27B-Coder-GGUF packages the Qwable coder-agent tune for llama.cpp, Ollama, and local workstation inference. It comes from a Qwen3.6-based model trained first on Claude Fable 5 traces, then continued on Kimi 2.7 Coder traces.

	Use this repo when you want Qwable's coding-agent behavior in GGUF form: repository inspection, patch planning, terminal feedback, verifier recovery, and long-context coding prompts.

	<a href="https://ko-fi.com/djlougen"><img alt="Support on Ko-fi" src="https://img.shields.io/badge/Support%20the%20compute-Ko--fi-ff5f5f?style=for-the-badge&logo=kofi&logoColor=white"></a>

	## Quant menu

	\| File \| Quant \| Approx. size \| Best for \|
	\| --- \| ---: \| ---: \| --- \|
	\| `Qwable-5-27B-Coder-Q8_0.gguf` \| Q8_0 \| 28.6 GB \| quality checks, quant comparisons, high-memory local serving \|
	\| `Qwable-5-27B-Coder-Q4_K_M.gguf` \| Q4_K_M \| 16.5 GB \| default local starting point \|
	\| `Qwable-5-27B-Coder-IQ1_S.gguf` \| IQ1_S \| 7.1 GB \| tight memory budgets; expect quality tradeoffs \|

	`IQ1_S` uses an importance matrix computed on the training traces.

	## Model facts

	\| Attribute \| Details \|
	\| --- \| --- \|
	\| GGUF repo \| `DJLougen/Qwable-5-27B-Coder-GGUF` \|
	\| Source checkpoint \| [`DJLougen/Qwable-5-27B-Coder`](https://huggingface.co/DJLougen/Qwable-5-27B-Coder) \|
	\| Upstream base \| [`unsloth/Qwen3.6-27B`](https://huggingface.co/unsloth/Qwen3.6-27B) \|
	\| Runtime target \| llama.cpp-compatible local inference \|
	\| Architecture tag \| `qwen3_5` \|
	\| Scope \| Text tower only; no vision sidecar in this repo \|
	\| Training signal \| Claude Fable 5 traces, then Kimi 2.7 Coder traces \|
	\| License \| Apache-2.0 \|

	```text
	BF16 source checkpoint
	-> GGUF conversion
	-> Q8_0: quality reference
	-> Q4_K_M: normal local use
	-> IQ1_S: smallest imatrix build
	```

	Early maintainer runs show the source checkpoint outperforming the base model on a private coder benchmark. Public benchmark details are not posted yet, so treat that as early maintainer signal rather than a reproducible leaderboard claim.

	## Quickstart

	Requires a llama.cpp build with `qwen3_5` support.

	Run a local OpenAI-compatible server:

	```bash
	llama-server -hf DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M \
	--jinja -ngl 99 -fa -c 32768 \
	--temp 1.0 --top-p 0.95 --top-k 20
	```

	Run with Ollama:

	```bash
	ollama run hf.co/DJLougen/Qwable-5-27B-Coder-GGUF:Q4_K_M
	```

	Download one file:

	```bash
	hf download DJLougen/Qwable-5-27B-Coder-GGUF \
	Qwable-5-27B-Coder-Q4_K_M.gguf \
	--local-dir .
	```

	## Choosing a file

	- Start with `Q4_K_M` unless you are explicitly testing quality ceilings or memory floors.
	- Use `Q8_0` for comparisons against the source checkpoint or high-memory local serving.
	- Use `IQ1_S` only when the model otherwise will not fit; verify quality on your own tasks.
	- Keep prompts concrete: include repository context, exact errors, constraints, and verifier commands.

	## Related releases

	- Source BF16 Transformers checkpoint: [`DJLougen/Qwable-5-27B-Coder`](https://huggingface.co/DJLougen/Qwable-5-27B-Coder)
	- NVFP4 ModelOpt checkpoint: [`DJLougen/Qwable-5-27B-Coder-NVFP4`](https://huggingface.co/DJLougen/Qwable-5-27B-Coder-NVFP4)

	## Limitations

	- Public benchmark tables are pending.
	- Low-bit GGUF quantization can reduce instruction following, code precision, and tool-call reliability.
	- This repo contains text GGUF files only; it is not the full multimodal Transformers checkpoint.
	- Long-context behavior depends on llama.cpp build, hardware, KV cache settings, and prompt layout.
	- Safety behavior is inherited from the base model and fine-tuning data; no separate safety alignment claim is made here.

	## License

	Released under Apache-2.0, following the upstream base model license metadata.